U.S. patent application number 16/576858 was filed with the patent office on 2020-01-09 for controlling building systems.
The applicant listed for this patent is NetESCO LLC. Invention is credited to Michael Craig Scelzi.
Application Number | 20200012307 16/576858 |
Document ID | / |
Family ID | 48903612 |
Filed Date | 2020-01-09 |
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United States Patent
Application |
20200012307 |
Kind Code |
A1 |
Scelzi; Michael Craig |
January 9, 2020 |
Controlling Building Systems
Abstract
Methods, apparatus, and systems are provided for measuring the
supply of a consumable product to a facility over time and
analyzing the measurements to determine the consumption or supply
of the product by one or more loads and/or sources in the facility,
and to determine induced and residual heat flow through the
facility's envelope. Various aspects compare the measured supply of
the consumable product to a database of consumption signatures.
Operating conditions and facility characteristics may be further
considered in determining a particular user's access of the
consumable product. Thermal resistance factors of the building may
be determined, which are based on the induced and residual heat
flow through the facility. Finally, one or more signatures of a
building system are analyzed to determine a building's overall
efficiency, including determining a controllable load of a building
and/or determining an efficient start time for one or more building
systems.
Inventors: |
Scelzi; Michael Craig; (Glen
Allen, VA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NetESCO LLC |
Glen Allen |
VA |
US |
|
|
Family ID: |
48903612 |
Appl. No.: |
16/576858 |
Filed: |
September 20, 2019 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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15258160 |
Sep 7, 2016 |
10452090 |
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16576858 |
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13800831 |
Mar 13, 2013 |
9471045 |
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15258160 |
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12960149 |
Dec 3, 2010 |
8843416 |
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13800831 |
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12557992 |
Sep 11, 2009 |
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12960149 |
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
Y02P 90/84 20151101;
Y04S 10/50 20130101; G05B 11/01 20130101; G06Q 10/00 20130101; G01K
17/20 20130101; G01R 21/1333 20130101; G05F 1/66 20130101; G05B
2219/2642 20130101; G06Q 50/06 20130101; G06Q 30/018 20130101 |
International
Class: |
G05F 1/66 20060101
G05F001/66; G05B 11/01 20060101 G05B011/01; G01R 21/133 20060101
G01R021/133; G01K 17/20 20060101 G01K017/20; G06Q 50/06 20060101
G06Q050/06; G06Q 10/00 20060101 G06Q010/00; G06Q 30/00 20060101
G06Q030/00 |
Claims
1. A method comprising: identifying at least one building component
associated with a facility; electronically retrieving an energy
signature from a database related to the at least one building
component; and displaying on a display an estimate of consumption
of energy by the at least one building component based on the
energy signature.
2. The method of claim 1 further comprising: generating a report
detailing the estimate of consumption of energy by the at least one
building component.
3. The method of claim 2, wherein the report comprises an estimated
cost corresponding to the estimate of consumption of energy by the
at least one building component.
4. The method of claim 2, wherein the report comprises an estimated
greenhouse gas emission by the at least one building component.
5. The method of claim 1 further comprising: retrieving
characteristics of the facility from the database.
6. The method of claim 5, wherein the characteristics of the
facility comprise at least one of: insulation factors of walls in
the facility; insulation factors of windows in the facility; load
factors of the facility; and historical seasonal usage information
of the facility.
7. The method of claim 1 further comprising: determining at least
one of: one or more energy ratings of the facility; compliance of
the facility with one or more governmental regulations; and
compliance of the facility with one or more trade group
certifications.
8. The method of claim 1 further comprising: determining one or
more thermal properties of an envelope of the facility.
9. A method comprising: determining, by an autonomous processor, a
measured signature of a consumable product supplied to a building
at a current operating condition; determining, by the autonomous
processor, a measured start time and a measured stop time of the
measured signature; and comparing, by the autonomous processor, the
measured stop time to the measured start time of the measured
signature; determine an optimal ramp time based on the comparing,
wherein the optimal ramp time is an interval of time before an
occupancy time which a building system should be started for the
current operating condition; determining, by the autonomous
processor, a building system start time based on the optimal ramp
time; and starting, by the autonomous processor, the building
system at the determined building system start time.
10. The method of claim 9, wherein determining the optimal ramp
time includes comparing the optimal ramp time to a table comprising
a plurality of optimal ramp times at a plurality of operating
conditions.
11. The method of claim 9, wherein determining the optimal ramp
time includes determining, by the autonomous processor, an
inflection point of a curve of the measured signature.
12. The method of claim 11, wherein the inflection point
corresponds to a time at which a slope of the curve of the measured
signature becomes negative.
13. The method of claim 11, wherein the measured stop time is a
time corresponding to the inflection point on the curve of the
measured signature.
14. The method of claim 9, wherein the optimal ramp time is a time
interval between the measured start time and the measured stop time
for the measured signature.
15. The method of claim 9, wherein the optimal ramp time is greater
than a time interval between the measured start time and the
measured stop time for the measured signature.
Description
[0001] This application is a divisional of U.S. patent application
Ser. No. 15/258,160 entitled "Controlling Building Systems" filed
Sep. 7, 2016, which is a continuation of U.S. patent application
Ser. No. 13/800,831 entitled "Controlling Building Systems", filed
Mar. 13, 2013, now U.S. Pat. No. 9,471,045, which is a
continuation-in-part of U.S. patent application Ser. No.
12/960,149, entitled "Determining Energy Consumption in a
Structure", filed Dec. 3, 2010, now U.S. Pat. No. 8,843,416, which
is a continuation-in-part of U.S. patent application Ser. No.
12/557,992, entitled "Determining Consumption and/or Generation of
Consumable Products in a Distributed System", filed Sep. 11, 2009.
Each application is herein incorporated by reference in its
entirety.
FIELD OF THE DISCLOSURE
[0002] Aspects relate generally to measuring consumption and
production of a consumable product in a distribution system by one
or more loads and/or sources. Further aspects relate generally to
measuring energy efficiency of buildings via direct measurements as
opposed to calculated theoretical measurements and/or utilizing
these direct measurements in incentive based construction
contracts. Methods, apparatus, and systems are disclosed which
determine access to the consumable product (e.g., electricity) by a
particular load or source in the distribution system through the
use of techniques which characterize consumption/generation of the
consumable product by one or more of the loads or sources. The
methods, apparatus, and systems further provide real time
monitoring of environmental conditions and usage of a building to
characterize the building's current and historical energy
performance and/or R-value.
BACKGROUND
[0003] Utility costs represent one of the largest expenses
effecting net operating cost of residential, commercial, and
industrial facilities. For example, a large office building
comprised of 60,000 square feet will have an electrical consumption
of approximately $10,000 monthly in the Mid Atlantic states in the
summer months. Knowing how a building is being utilized by its
tenants and knowing the building energy performance are both
factors in understanding and controlling these costs.
SUMMARY
[0004] With respect to building utilization, tenants are constantly
connecting electrical consumption devices including servers and
other electric equipment not only to dedicated tenant lines but
also to building power lines. Being able to recover this cost from
tenants of the facility is critical to maximizing value, maximizing
loan capacity of the facility, and maximizing revenue stream
generated from the facility. However, being able to accurately
match the consumption of utilities such as electrical power to
individual tenants and/or buildings is often difficult. Further,
some tenants and/or buildings will provide for generation of power
for input into a smart grid. These generation facilities may
include, for example, solar panels and/or wind generation
facilities located proximate to buildings such as on top of
buildings. There is a need to account for these installations.
Additionally, electrical power, which is distributed to a number of
tenants, may be provided to a facility with one supply service
measured by one meter. To recover the cost of the electrical power,
the facility manager may have to install costly additional supply
services and meters or retro-fit the electrical distribution system
in the facility such that each tenants electrical usage can be
measured individually. Alternatively, the cost of the utility may
be averaged and allocated to each tenant equally.
[0005] Situations may arise where one tenant consumes a
disproportionate amount of the utility. For example, a particular
tenant may install high powered add-on equipment such as computer
server rooms, laboratory systems, or cellular network towers. In
such cases, the facility operator may find that averaging the
utility cost across all of the tenants may push the facility's
fixed cost per square-foot to be greater than the facility's value
per square-foot.
[0006] Building systems lack a simple understandable method for
tracking the utility consumption. Due to the inability to simply
track the consumption, building automation systems are often
removed from service, electrically jumpered out of the distribution
system, adjusted to extend start and stop times beyond optimal
settings, not adjusted to reflect changes in the hours of occupancy
from the original lease schedule, etc., and thus, the facility
consumes more energy due to inadequate controls and monitoring. One
technique to monitor and control the consumption is, for example, a
graphical user interface which may be variously configured. In
exemplary embodiments, it may be configured to compare historical
values (as for example adjusted for outside temperature) with
current values. The graphical user interface may employ an
appropriate algorithm and graphical representations showing
deviations which likely indicate either a problem or new energy
usage by a particular tenant.
[0007] Another factor in controlling energy costs is understanding
the thermal performance of the building's envelope (i.e.,
structure). Improving energy efficiency in new construction and in
the remodeling of existing structures has become a primary concern,
which is driven by such factors as utility costs, public concern
for the environment and human health, government regulation,
corporate social responsibility, globalization, and other market
forces. In response to this concern, industry groups have formed,
which put forth efficiency guidelines and certification programs
for builders to follow. These certifications and other design
benchmarks require energy efficiency to be addressed early in the
design and construction process.
[0008] These requirements and verifications typical are based on
simulation of building models, and an as-built structure may not,
and often does not, meet the energy performance requirements of the
planned design on day-one after completion. The errors in the
simulation may be caused by design variations that are not
reflected in the model, construction of the structure which is not
to specification, incorrect assumptions on building usage and
weather, utility equipment which is not installed correctly or
functioning according to specification, insufficient model
fidelity, and numerous other factors. Further, a building's energy
performance may change over time due to the aging of materials,
modifications to building structures and systems, or damage to the
structures and systems.
[0009] Currently, no means exist to comprehensively measure a
building's actual energy performance or to monitor the energy
performance over time. Thus, the verification and management of a
building's designed energy efficiency is based on incomplete or
inaccurate information.
[0010] To overcome these problems described above and other
problems, methods and systems are needed to determine the use of a
utility by individual tenants, and to provide comprehensive in-situ
measurement of a building's actual energy performance. These
techniques allow building developers to insert incentive provisions
in their contracts to ensure that buildings actually meet their
design requirements. The end result may be specified without
micromanaging the building process. This allows the building
process to proceed as efficiently as possible and allows new
technologies to be easily integrated without renegotiating the
overall contract.
[0011] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This Summary is not intended to identify
key features or essential features of the invention.
[0012] Various techniques are presented for measuring the supply of
a consumable product, such as electrical power, to a facility over
time and analyzing the measurements to determine the consumption or
supply of the product by one or more loads and/or sources (i.e.
users) in the facility. Various aspects compare the measured supply
of the consumable product to a database of signatures, and/or lease
schedules which characterize access to the consumable product by
particular users. In one exemplary embodiment, the supply of
consumable product to the HVAC system may be measured and analyzed
as compared to the inside temperature, outside temperature, sun
loading, and heat generated by other devices such as lighting and
computers to determine the overall R-value of a building.
[0013] The various techniques may be used, for example, in
facilities to provide detailed reports of the usage of utilities
such as electrical power, gas, and water, by multiple different
users connected to a common measurable supply of the product. In
doing so, the various techniques may be used to more accurately
divide the cost of such utilities between different tenants of the
facility without having to install individual services that can be
individually measured for each tenant. In addition, by analyzing
real time data and historical utility signatures the user can
modify schedules to match leases, verify equipment operation either
on/off, and verify large equipment loads by reviewing building
utility signatures.
[0014] In a first embodiment, measurements are made from a common
service, recorded as a data sequence, and transmitted to one or
more processors for analyses. The processor(s) may retrieve
signatures from a database to analyze various parameters such as
the data sequences and determine, for example, different parameters
such as how much of the measured product is consumed or produced by
one or more particular users within a group of users connected to
the measured service. Reports may then be generated which detail
the use and/or supply.
[0015] In other embodiments operating conditions of the various
users in the facility are measured at the same time supply of the
consumable product may be measured. These operating conditions may
be stored and/or transmitted to the processor(s), and the
processor(s) may be configured in various ways. In one
configuration, the processors may use the operating conditions as
additional data in determining the usage of the consumable product
by one or more users. Operating conditions may include, for
example, temperatures inside and outside of the facility, and/or
the number of people in the facility. These parameters may be
utilized to determine a base line and/or inform the building
manager whenever the building varies from the baseline, potentially
indicating an anomaly.
[0016] In another embodiment, artificial intelligence algorithms
such as, for example, neural networks may be used in the analysis
of the data sequences and/or signatures to determine the usage of
one or more particular users. The artificial intelligence may
develop and learn over time using both rule based input and learned
input from a trained operator.
[0017] In other embodiments, the signatures may be created by
monitoring the use and/or supply of the consumable product in the
distribution system of a facility and comparing the monitored use
and/or supply to measured or controlled operating conditions of
users of the product within the facility. The signatures may be
determined, for example, by training an artificial intelligence
process such as a neural network with the measured supply and/or
operating conditions.
[0018] Further techniques are presented for measuring induced
and/or residual heat flow through a building envelope. Various
illustrative induced heat flows include heat resulting from:
electrical power flowing into a building and being converted to
heat by the distribution system and by electrical loads; fuel such
as natural gas flowing into the building to produce heat when used;
water which flows into the building and carries heat by virtue of
its thermal mass; climate control systems which mechanically move
heat through the building; and people which dissipate heat while
inside the building. Residual heat flow includes the passive heat
transfer through the building structure which is induced by the
difference in the environments within the building and outside of
the building. These various induced and residual heat flows may be
determined periodically and in real time.
[0019] In exemplary embodiments, the building may be qualified
while minimizing the transient heat flows generated by people,
water, and non-HVAC heat sources such as lights and computers. This
qualification may take place both at initial building launch and
after a set period of time such as after building buildout.
Incentives may be built into the contract that are conditioned on
meeting predetermined performance criteria, such as R-value
criteria. These R-value criteria may be specified with and/or
without transient heat flows minimized.
[0020] In various embodiments, the residual heat flow and thermal
resistance of wall assemblies of the building are measured using
embedded sensors within the wall and/or periodically using
transportable sensors. In one embodiment, the sensors are embedded
in the material making up a layer of the wall assembly by the
manufacturer of that material layer. In another embodiment, a
temperature probe is inserted through the layers of the wall,
wherein the probe has the ability to sense multiple temperatures at
incremental depths along the probe.
[0021] In further embodiments, a thermal resistance factor of the
building envelope is determined based on the measured induced
and/or residual heat flows. Thermal resistance factor, R.sub.C, is
often a composite measure of thermal performance of a building
envelope. The thermal resistance factor may be determined
statically, where some or all induced heat flow is cut off, and the
inside and outside environments are monitored over time as they
approach equilibrium with one another. In one aspect the thermal
resistance factor is determined as the amount of time taken for
equilibrium to be reached given predefined initial conditions. In
another aspect, the thermal resistance factor is defined by the
change in temperature over a given time period given at set of
initial conditions.
[0022] In other embodiments, the thermal resistance factor may be
determined dynamically, where induced heat flow and environmental
conditions are periodically and/or continuously monitored in real
time.
[0023] In various embodiments, the static or dynamic thermal
resistance factors may be used in one or more additional processes.
For example, in one embodiment, the process may utilize a one-time
snapshot of thermal resistance factor and/or a thermal resistance
factor determined repeatedly to capture changes in the thermal
resistance factor over time. In embodiments, the method of
determining a target thermal resistance factor and/or a required
thermal resistance factor may be specified in a building contract
as a design metric. In other embodiments, the specific methods for
determining R.sub.C may be used as industry standards to compare
different structures, or to establish minimum build criteria. In
still other embodiments, the thermal resistance factor may be used
along with measured or forecasted environment conditions in a
closed loop system to control the climate control system of the
building. For example, the closed loop system may further control
the climate control system and/or other building systems based on
utility usage by different users as determined above based on user
signatures.
[0024] In various embodiments, the energy performance of a building
is determined by analyzing a signature characterizing one or more
building system's access to a consumable product. One or more
metrics representing this energy performance may be determined in
order to quantify the building's energy performance. For example, a
controllable load, controllable load score, or building start time
score may be determined. Further, in some embodiments a weighted
energy score, which combines two or more of these metrics, may be
determined. Finally, in some embodiments, a signature
characterizing one or more building system's access to a consumable
product may be used to determine an optimal ramp time and
corresponding start time for a building system, such as an HVAC
system.
[0025] Other various embodiments include systems, equipment,
processes, and computer readable memory storing machine executable
instructions for performing the functions of the embodiments
described above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0026] A more complete understanding of the disclosure and the
advantages thereof may be acquired by referring to the following
description in consideration of the accompanying figures, in which
like reference numerals in different figures indicate similar
elements, in which the first portion of each reference numeral
corresponds to the figure number in which the referenced element is
first introduced, and wherein:
[0027] FIG. 1 illustrates an exemplary electrical distribution
system of a facility in which various embodiments may be
implemented;
[0028] FIG. 2 illustrates an exemplary display graph illustrating
data sequences representing consumption of a consumable product in
a distribution system;
[0029] FIG. 3 illustrates a flow diagram of an embodiment for
determining an amount of a consumable product accessed by a
user;
[0030] FIG. 4 illustrates a flow diagram of another embodiment for
determining an amount of a consumable product accessed by a
user;
[0031] FIG. 5 illustrates an exemplary facility incorporating
various sensors which may be used with various embodiments;
[0032] FIG. 6 illustrates a flow diagram of an embodiment for
determining signatures which characterize access to a consumable
product by one or more users;
[0033] FIG. 7 illustrates a hardware block diagram of a processor
according to some embodiments;
[0034] FIG. 8 illustrates a building envelope in which various
embodiments may be implemented;
[0035] FIG. 9 illustrates heat flow through a building envelope in
which various embodiments may be implemented;
[0036] FIG. 10 illustrates a flow diagram of an embodiment for
determining heat flow and thermal performance of a building
envelope;
[0037] FIG. 11 illustrates a flow diagram of an embodiment for
analyzing data sequences to determine induced heat flow from
various energy sources;
[0038] FIG. 12 illustrates a flow diagram of an embodiment for
measuring environmental conditions and self contained heat emitting
bodies;
[0039] FIG. 13 illustrates an illustrative facility incorporating
various sensors which may be used with various embodiments;
[0040] FIG. 14 illustrates various embodiments of sensors and
resulting sensor data;
[0041] FIG. 15 illustrates an illustrative lookup table used in
conjunction with determining a building start time score;
[0042] FIG. 16 illustrates a flow diagram of an embodiment for
determining a controllable load and/or a controllable load score of
a building;
[0043] FIG. 17 illustrates a flow diagram of an embodiment for
determining a building start time score and/or a weighted energy
score of a building; and
[0044] FIG. 18 illustrates a flow diagram of an embodiment for
determining an optimal start time for a building system.
DETAILED DESCRIPTION
[0045] In the following description of the various embodiments,
reference is made to the accompanying drawings, which form a part
hereof, and which are shown by way of illustration. It is to be
understood that other embodiments may be utilized and structural
and functional modifications may be made without departing from the
scope of the disclosure.
[0046] FIG. 1 illustrates an exemplary system 100 in which various
embodiments may be implemented. System 100 includes an electrical
distribution system of a facility 101 in which electrical power is
distributed as a consumable product to various electrical loads
(i.e. users) connected to the distribution system. The facility may
be commercial, residential, industrial, some combination thereof,
or may be any other structure which contains a distribution system
for the supply and/or generation of a consumable product. Exemplary
facilities include apartment buildings, strip malls, office
buildings, hospitals, industrial parks, etc.
[0047] A consumable product such as electrical power may be
provided to facility 101 via any suitable source such as a public,
private, and/or cooperative utility through transmission lines 128
or may be generated from on site sources such as solar panel 130,
or back up generator 131. The utility may be provided as one supply
feed 103 through a single meter 102, or may be supplied through
several separate feeds and/or meters. Often, a single feed is split
at a distribution point 106 into several separate distribution
services 120-124, each of the services providing one or more
different users access to the utility. Exemplary users may include
loads typically found in facilities, including lighting 107-112
and/or heating, ventilation and/or air conditioning systems (HVACs)
117-119. Additional non-standard loads may also be connected to the
distribution system depending on the particular use of the
facility. Illustrative non-standard loads may include a computer
server room 114, industrial machinery 115, medical equipment 113,
and/or a cellular network tower 116. Additional exemplary
non-standard loads may include charging circuits 129 in a garage
associated with electric cars. Users may inductively or via a
plug-in cable charge their electric cars while parked in the
garage. These charges may be billed back to the individual parking
tenant rather than distributed to all users in the building.
Further, during peak periods, cars in the garage may be utilized to
store and resource power back into the building and/or distribution
network. Exemplary users may include producers of the consumable
product that is put back into the distribution system for use by
loads locally and/or for distribution back into the transmission
lines 101. Such producers may include solar panels 130, back-up
generators 131, batteries from electric cars, etc. which may be
distributed about the building including on the sides, within the
building, on the roof, and/or disposed around a building, cell
tower, or other facility 101. These sources may be coupled to the
distribution device 106 for use locally and/or input back into the
system 100 via transmission lines 128.
[0048] Distribution systems may be designed to support specific
users, and/or specific tenants of the facility who connect a
specific group of users to the distribution system. The design of
the distribution system may provide individual tenants and
individual employees of the tenant their own supply feed,
distribution service, and/or different combinations thereof such
that use and/or generation of the consumable product by the
individual tenant/employee may be uniquely measured using a meter
on the tenant's/employee's supply feed or using a sub-meter on the
tenant's distribution service. However, designing the distribution
around specific tenants is not always accomplished or even
possible. As requirements for the facility change, users of the
consumable product may be added in an ad-hoc manner without
accounting for how the consumable product is shared between the
tenants.
[0049] In instances where access (consumption or production) to the
consumable product by a specific user (load or producer), or group
of users, needs to be measured on a supply feed or distribution
service shared with other users, some embodiments may add a meter
104 or sub-meter 105 to a supply feed or distribution service
respectively, to measure and record consumption or supply of the
consumable product over time. From these measurements, analysis may
be performed according to certain embodiments to determine access
to the consumable product by the user, or group of users, of
interest, from the total access by all the users on the measured
supply feed or distribution service.
[0050] Meter 104 and sub-meter 105 may be variously configured. In
one embodiment, meter 104 and sub-meter 105 may include one or more
sensors coupled to the supply feed or distribution service used to
measure the supply of the consumable product. Various sensors
appropriate for measuring the consumption and/or supply of the
consumable product will differ depending on the consumable product
being measured. In an electrical distribution system in system 100,
the sensor may be an inductively coupled transformer, a current
shunt, or other appropriate sensor for measuring the consumable
product such as power, electrical current and/or voltage. In other
distribution systems for other consumable products such as natural
gas and water, appropriate flow meters may be used. Meters 104 and
105 will further include a computing platform to operate the
sensor, and accumulate pulse inputs (periodic measurements) from
the sensors. Each meter may include several sensors and accumulate
data from several different paths in the distribution system. As an
example, meter 104 may include a circuit board with 10 sensor
channels for sensors which may each collect pulse data in parallel.
A processor on the circuit board may read each channel and
accumulate data in the same and/or separate memory devices (e.g.
registers) for each channel. The meter 104 may further have a data
display which scrolls periodically and/or continuously to
illustrate the pulses per channel. In addition to the data display,
meter 104 may have buttons or other inputs which can be used for
on-site programming and/or trouble shooting. After on-site
programming/trouble shooting, further programming may be from a
remote location and/or computer.
[0051] The meters may be variously configured. In some embodiments,
the meters may transmit data (e.g., pulse data) to a different
computing platform, such as server/workstation 127 via a private
network (e.g., cellular network 125) and/or a public network (e.g.,
the Internet 126). The pulse data from one or more sensors may be
individually transmitted, and/or may be grouped in any appropriate
manner such as being totaled over a user defined and/or predefined
and/or variable period and transmitted. The server/workstation 127
may further accumulate data from one and/or several different
meters. Server/workstation 127 may be within the facility,
collocated with the meters, or remote as illustrated in FIG. 1.
Each meter and server/workstation may have one or more interfaces
to one or more communication paths to transfer data between the
meter and the server/workstation. Exemplary communication paths may
include various public and private local area networks (LAN) and
wide area networks (WAN), etc., over various physical networks,
including voice band and digital subscriber line (DSL) modems on
public switched telephone networks (PSTN), cable and fiber-optic
modems and networks, cellular phone networks, satellite networks,
Wifi, Wimax, etc. The various communication paths may provide a
direct connection between the meters and the server/workstation,
and/or may provide connection through the Internet via an Internet
Service Provider (ISP). System 100 in FIG. 1 illustrates an
exemplary Internet connection 126 connecting meter 104 to
server/workstation 127, and an exemplary cellular phone network
connection 125, connecting sub-meter 105 to server workstation 127.
These communication paths could also, for example, include a
combination of these networks. For example, sub-meter 105 could
alternatively use any suitable wireless protocol (including
802.11a/g/n, wireless internet protocol, 3G, 4G, GSM, PHS, HCSD,
TACS, CDMA, HSDPA, TDMA, CDMA2000, iDEN, TD-SCDMA, EV-DO, Mobitex,
UMTS, FDMA, NMT, WCDMA, GAN, PCS, WiDEN, GPRS, PDC, WiMAX, and/or
ISM band) over a network such as cellular network 125 to connect
the meters to each other and/or to a public or private network
(e.g., Internet 126) and to any appropriate server/workstation 127.
For example, the ISM band may allow for either battery operated
and/or inductively powered meters which can operate without having
to be plugged in and/or connected to a wired interface and/or
power. In embodiments using an Internet protocol, meter 104 may
utilize a dynamic IP address, and, once powered and connected to
the Internet via an Ethernet connection, may automatically find
server/workstation 127 on the network and register each new channel
as a new user, assigning a unique address for each channel. For
example, the device may each have an auto configure and
registration mode which allows the installer to remotely activate
and register the device to a particular building using a laptop
with a wireless card and/or a handheld smart phone like device.
[0052] The server/workstation 127 and the various meters may
collect the pulse data in a variety of ways. For example,
server/workstation 127 may host a website which may accumulate the
pulses in one or more memories such as data registers. There may be
one or more memories per sensor. Depending on the type of meter
device utilized and on the type of consumable product/energy
source, the data pulses and/or other signal indication may be
converted to consumption (e.g. kWhr, Therm, Gallons, Lumens, etc.)
on each measured supply feed or distribution service based on a
programmed conversion factor. The time rate of measuring and
collecting pulse data may be pre-programmed or adjusted based on
such factors as the type of analysis to be done on the data, the
bandwidth available to transfer the data from the meters to the
server/workstation, or the capability of the meters themselves. The
pulse data may be accompanied by meta-data, such as time stamps of
when the pulses were measured. The data may further be protected
with data encryption and/or other security measures to ensure the
integrity and privacy of the data during transmission between the
meters and the server/workstation 127 and during access to the data
once stored in server/workstation 127. For example, the data may be
encrypted and/or accompanied by a digital signature to ensure that
the meters may not be altered or spoofed. An initial key exchange
may occur between the meters themselves and/or between the meters
and/or the workstation. In this way, once the meters are
registered, the communications may not be spoofed and/or altered.
Hence all reporting is done in a secure manner. Where time stamps
are used, the time stamps may utilize any time base/zone, such as
GMT-0 such that collection of data may be time synchronized with
other measurements collected from the same distribution system
and/or facility, or from other distribution systems and/or
facilities.
[0053] Once collected, the server/workstation may compile the data
from each sensor/channel into time sequences of data. Exemplary
data sequences may be graphically illustrated either on the meter
and/or on the server/workstation 127 as, for example, illustrated
in FIG. 2. This graph may also be analyzed remotely on a laptop
computer, across the Internet, and/or on a smartphone. Graph 200,
for example is a representative plot of kilowatt-hours (kWhr) of
electrical power measured by meter 104 in FIG. 1 over a period of a
week. As can be seen in FIG. 2, in exemplary embodiments, power
typically oscillates during the week, with peaks reached during
typical business hours and dropping during off hours and/or the
weekend. Graph 200 may be variously configured including as a
composite of smaller data sequences such as sequences 201 and 202.
A representative graph of what data sequence 202 may look like is
shown in more detail below the graph of data sequence 200. As can
be seen in the graph of sequence 202, more detailed sequences, such
as sequence 203 may be extracted. The detailed data sequences
associated with individual meters may help pinpoint potential
issues with the generation and/or use of a particular consumable
resource.
[0054] FIG. 3 illustrates a process, according to some embodiments,
to analyze these data sequences to determine any deviations from an
expected amount of power that is consumed and/or produced by one or
more particular users within a group of users connected to the
measured service. Process 300 starts at 301 where the supply of a
consumable product is measured in a distribution system by a meter
(e.g. 104). The measured values are then transmitted from the meter
in step 302, and subsequently received by a processor (e.g.
server/workstation 127) in step 303. Steps 301, 302, and 303 may be
accomplished as already described with respect to FIG. 1 and may
result in one or more data sequences as illustrated in FIG. 2.
Steps 301, 302, and 303 may occur on a pre-determined scheduled
basis, as a result of the processor requesting the measured data
from the meters, or both. In step 304, signatures of users are
retrieved from a database. Each signature is a characterization of
the access to the consumable product by one or more of the users.
In step 305, the processor uses the signatures and the measured
data sequences to determine which of the group of users connected
to the distribution service is actually accessing the consumable
product, and/or how much of the product is being accessed. Further,
the consumable products consumption and/or generation from a
plurality of facilities may be aggregated over time and/or over
different facilities and used to formulate profile for a collection
of assets. These profiles may then be used to negotiate with
various suppliers of consumable products in the purchase of the
consumable product. For example, an individual building owner often
lacks sufficient market power to negotiate efficiently. However,
using embodiments tens, hundreds, thousands, ten thousand, and even
hundreds of thousands of facilities may be aggregated over the
continent and over the world to negotiate the least expensive rates
for consumption of the consumable products and the most favorable
offsets for sources of the consumable products. In this manner,
embodiments may allow the aggregation of many facilities to provide
market power and to take advantage of the smart networks for
consumables and the increasing deregulated environments for the
delivery of consumables.
[0055] As an example of step 305, a processor in server/workstation
127 may use a pattern matching algorithm to match data sequence
203, illustrated in FIG. 2, to a signature which characterizes cell
tower 116 in FIG. 1. The processor may further use more than one
signature or may combine signatures to determine use of the
resource by one or more users. For example, signatures for cell
tower 116 and HVAC 119 may be combined additively to determine
simultaneous use by 116 and 119. The processor may also manipulate
the signatures and/or data sequence using various signal processing
algorithms in the process of determining the users. For example,
the processor may transform the signatures and data sequences from
the time domain to the frequency domain using various Fourier
transform algorithms. The processor may also use various artificial
intelligence/smart agent/learning algorithms to process the
signatures and data sequences either in the time domain and/or the
frequency domain. The algorithm may also smooth the uses by
filtering them with a high pass and/or low pass filter in order. In
exemplary embodiments, the use of filters allows the artificial
intelligence algorithms to operate more efficiently. For example,
the processor may train a neural network on known operating
conditions of various users, different combinations of signatures,
and various data sequences acquired during the known operating
conditions (with or without filtration) to develop a matching
algorithm that is subsequently used in identifying later
aberrations from known usage patterns.
[0056] In one exemplary embodiment, an artificial intelligence
engine may implement the following algorithms:
[0057] a. Average exceeding threshold by Standard Deviation [0058]
1. Sample 30 minutes data; [0059] 2. Determine if in occupied mode
or unoccupied mode, determine outside temperature; [0060] 3. Look
at averages of sampled data to see if it exceeds the previous
average (threshold power) by a standard deviation (either user
selectable or automatically determined by past experience; [0061]
4. In event the Threshold Power is exceeded, send email energy
alarm to network administrator and/or customer including, for
example, date, time and alarm type such as reading.
[0062] b. Determine Appropriate Start and Stop times [0063] 1.
Monitor data points immediately following occupancy in the Morning
start up sequence including outdoor temperature; [0064] 2. As slope
of kW line changes by an administrator configurable amount in
consecutive data points (including average time windows), store the
amount of change; [0065] 3. The result may be used to alert the
building owner based on the forecast of what time their building
should start and compare that to what time they have in their
occupancy schedule. [0066] 4. Allows critical functions such as
HVAC to be matched to actual work schedules in building.
[0067] The Artificial Intelligence Engine may constantly search the
utility signatures in the database to associate a signature to a
hard asset. For example: a 50 HP Fan Motor with Variable Frequency
Drive may have a particular electrical consumption signature
comprised of amps, power factor, watts. The AI engine may
constantly review every library signature in the database (whether
real or from a factory test stand--manufacturer's data) to
correlate the motor signature to the library via statistical
analysis. The AI engine may determine a correlation error factor
between the motor signature and the library signatures (e.g. motor
signature-library signature=error factor) via heuristics,
optimization, simulated annealing, beam search, random optimization
and/or a custom AI algorithm. When the error factor is below an
acceptable level, the AI engine may output the load associated with
the library signature, i.e. the 50 KW fan motor with Variable
Frequency Drive.
[0068] The AI engine may thus inform an operator what load to look
for. The AI engine may also write to the facility automation system
sending computer code (bacnet, lontalk, any communication protocol
accepted by facility automation system) to shutdown the load (50 Hp
motor in this case) based on a permissive such as occupancy of
facility, demand reduction, etc. In simple terms, an exemplary
embodiment captures a signature measured from the distribution
system and compares the measured signature to the library of
signatures. The AI engine may search for Global Signatures such as
for an entire facility or a sub-level within the facility (e.g. 50
KW Motor in a HVAC Unit on Roof). The comparison may be used to
isolate and identify potential loads. The potential loads may then
be communicated to an operator/customer or automatically controlled
(e.g. on/off) via a communication protocol to regulate use of the
distributed service.
[0069] Returning now to FIG. 3, the sequence will be further
explained. After step 306, the processor (e.g. server/workstation
127) may then generate reports which detail the usage of the
consumable product by various users and/or alerts when any user
diverges from an expected usage. The report may be customized to
detail access by a particular user over a fixed duration, and/or
may detail a group of users of a specific tenant. The processor may
further determine costs of the access by the particular user and/or
group of users to the consumable product and include the cost in
the report. For example, as in FIG. 1, the consumable product may
be a public utility such as electrical power. Cost may simply be
based on a constant rate, or may be based on a tiered utility rate
which accounts for different rates at different times (i.e. peak
and non-peak usage times). The reports may also include usage of a
consumable product in graphical form, such as in FIG. 2. The report
may further include other secondary data that may be derived from
the consumable product usage. Exemplary secondary data may include
calculations of green house gas emissions by a particular user.
These reports may be processed as bills and sent directly to the
users as well as copied to the building managers.
[0070] Process 300 may be performed by an autonomous processor that
works continuously collecting data (e.g., pulse data) and
determining users and/or aberrations in real-time or near
real-time, and generating reports on a fixed schedule (i.e.
monthly) or based on a certain level of use or cost (or aberrations
in use or cost) being reached by a particular user or group of
users. The reports may take the form of an invoice and sent to
tenants responsible for the particular users detailed in the
report. These reports may be generated and sent in the form of
hard-copies and mailed, in electronic form and sent via electronic
mail, text message or other form of electronic transfer, or in the
form of voice messages sent via phone line. Further embodiments may
allow the reports, including billing information and graphical data
to be displayed on any customer interface device; desktop, laptop,
PDA, Blackberry and or client internet portal, and may be further
provided through a website hosted by the processor. By serving the
data from a website, a tenant/customer may be able to view usage
and cost data and graphic displays in real-time and/or near
real-time. As referred herein, "real-time" refers to updating the
usage data as it is collected and calculated with little and/or
relatively little delay other than the time it takes to process the
data. The amount of delay may be a designed limit on processing
time, such that the data may be used in closed loop control of
users, or the delay may simply be dependent on the resources
available in measuring, transferring, and processing the data. For
the purposes of this application, "real-time" and "near real-time"
refers to the same concept in processing data.
[0071] Process 300 may be augmented with additional steps of
process 400 illustrated in FIG. 4, and described with respect to
FIG. 5, for incorporating operating conditions into the
determination of the various users. In this exemplary embodiment,
Process 400 starts at 401 by measuring environmental conditions of
areas which are proximate to a particular user or are served by a
particular user. For example, as illustrated in FIG. 5, HVAC 119
and lighting 120 may serve a room 500 in the facility 101 of FIG.
1. Environmental conditions such as temperature 503 inside of the
room and temperature 502 outside of the room may be measured. Other
operating conditions which affect usage of the consumable product
may also be captured, as in step 402 for example, where persons
within room 500 may be counted by a sensor 504. Such measurements
of operating conditions may be accompanied by meta-data such as
time stamps or time intervals such that the operating conditions
may later be correlated to usage data of electrical power by users
119 and 120. In step 403, the measured environmental and other
captured operating conditions are transmitted to the processor.
FIG. 5 illustrates an exemplary data collection node 501 collecting
the measured values and transmitting them to the processor in
server/workstation 127 through cellular network 125. Data
collection node 501, may be the same as meters 104 and 105, or may
be some other computing platform operating in the same manner as
104 and 105 over the same types of communication links to transfer
data to server/workstation 127. In step 404, the processor in
server/workstation 127 receives the transmitted data. In addition
to receiving operating conditions measured from the facility, the
processor may retrieve other operating conditions from a database
such as in step 405. The processor, in step 405, may retrieve
facility characteristics from the database, such as square footage
of rooms in the facility; age of the facility; insulation factors
of walls, windows, and other structures; load factors which
indicate peak usage versus minimal usage ratios, historical
seasonal usage information, age and efficiencies of the users, etc.
In step 406, the same steps as in steps 301 to 305 of process 300
are performed except that the operating conditions measured from
the facility and retrieved from the database are incorporated in to
the step 305 for determining access to the consumable product by a
particular user. In step 407, a report may be generated in the same
manner as in step 306 of process 300. The report may further
include details of the operating conditions acquired in steps 401
to 405, and other secondary data that may be derived from the
operating conditions and consumable product usage. Exemplary
secondary data may include calculations of green house gas
emissions by a particular user.
[0072] As with process 300, the steps of process 400 may be
performed autonomously, in which the operating conditions and usage
data are continuously collected, users are determined in real-time
or near real-time, and reports are generated on a fixed schedule
(i.e. monthly) or when certain levels of usage or costs are reached
by a particular user or group of users.
[0073] In order to perform processes 300 and 400, the processor
performing the process it may be desirable for the processor to
have either preloaded and/or learned signatures of the various
users connected to the distribution system. In another exemplary
embodiment, the processor may create these signatures according to
a process 600 as illustrated in FIG. 6. In one exemplary
embodiment, Process 600 in FIG. 6 starts by measuring access to a
consumable product by a plurality of users on a supply feed or a
distribution service over a fixed period of time. During the
measuring, the operating states of the plurality of users are also
determined. The operating states may be determined by monitoring
the users or controlling the users. The monitoring and controlling
may be performed by the processor using the same or similar
communication links used for receiving data from meters. Other
operating conditions may also be monitored or measured over the
fixed period of time. The operating conditions may include the same
measured (e.g. inside and outside temperature, person count, etc.)
and facility characteristics (e.g. square footage, facility age,
insulation factors, load factors, historical seasonal usage
information, age and efficiencies of the users, etc.) as in process
400. Measuring access to the consumable product and monitoring the
operating conditions may be achieved by the same or similar manner
as is accomplished in processes 300 and 400. Once access to the
consumable product and operating conditions are measured or
determined, the data is correlated to the known operating states to
determine the signatures which characterize the access by one or
more users of the plurality of users. The signatures may contain
variables to account for different operating conditions or may
assume an average or estimated operating condition. Multiple
signatures may further be created for the same on or more users,
with each signature reflecting a different set of operating
conditions.
[0074] The creation of signatures may be accomplished by a variety
of different algorithms. For example, referring back to FIG. 2,
data sequence 203 may have been recorded when HVAC 119 was being
cycled on and off, lighting 112 was being powered during regular
operating hours of the facility, and cell tower 116 was operating.
A processor in server/workstation 127 may use a pattern matching
algorithm to correlate transitions in data sequence 203 to the
changes in states of HVAC 119, lighting 112, and cell tower 116 to
create signatures which characterize each of these loads or a
combination of these loads. Previously determined signatures (e.g.
for lighting 112 and HVAC 119), may be used to cancel out the
effects of certain loads (e.g. lighting and HVAC) in determining a
signature of just one of the users (e.g. cell tower). In this
manner, the signature may be combinable or divisible to uniquely
reflect use of the consumable product by a combination of users on
a single supply feed or distribution service. The processor may
also manipulate the data sequence and state information using
various signal processing algorithms in the process of determining
the signatures. For example, the processor may transform the data
sequences from the time domain to the frequency domain using
various Fourier transform algorithms. The processor may also use
various artificial intelligence/intelligent agents/learning
algorithms to determine the signatures. For example, the processor
may train a neural network on known operating conditions, operating
states, and measured data sequences to determine the sequences. The
signatures may take on a plurality of forms, including a time
sequence of data or a frequency spectrum of data that may be
combined with other signatures to be matched to measured data
sequences. In the case of using a neural network to identify a user
in process 300 and 400, the signature may be in the form of branch
weights in the neural network for identifying a particular
combination of users.
[0075] After determining one or more signatures, the signatures may
be stored in a database at step 604. In addition to storing the
signatures, the measured or determined operating conditions may
also be stored to the database in step 605. The signatures and
operating conditions may be stored in a single database, or may be
stored in separate and numerous databases. The databases may be
collocated with the processor, or may be remote and accessed by the
processor through a network connection. Process 600 may finish with
generating a report of the stored signatures and operating
conditions. The databases may then be used, for example, in
processes 300 and 400 for later determining access to the
consumable product by a particular user. In the example of FIG. 1,
the various signatures would reflect various electrical loads and
sources as already described. The database of signatures may also
be used in other processes such as determining energy ratings of
users or compliance of different users and facilities with
applicable governmental regulations, or trade group
certifications.
[0076] In addition to determining energy usage by users within a
facility, determining energy performance of the facility may also
be used in various embodiments. FIG. 8 illustrates a building
envelope 800 of a facility in which energy performance may be
determined. Building envelope 800 may include the building
structure, which thermally separates an enclosed volume from an
outside environment. The structure may include, for example, any
combination of building material (e.g., cement, glass, wood, metal,
etc.), and may be used for any purpose (e.g., residential,
commercial, industrial, etc.). For simplicity, building envelope
may be shown schematically to include a solid perimeter with a
single enclosed space, but the building envelope may also include
floors, ceilings, and any other structure which encloses the volume
of interior space.
[0077] Various aspects of building envelope 800 described herein
may also apply equally to multi-room structures, a single room
within a multi-room structure, a single floor within a multi-floor
structure, structures with openings such as doorways, vents,
windows and other fenestration, structures with ceilings of various
heights, structures with various shaped perimeters, and/or
structures with walls, floors, and ceilings of different shapes and
sizes.
[0078] One function of building envelope 800 is to provide thermal
isolation between an outside environment 809 and inside environment
808, such that the inside environment 808 may be controlled in an
energy efficient manner. Although, generally, the outside
environment 809 comprises the space outside of the entire building
structure, outside environment 809 may equally refer to one or more
rooms or spaces within the building structure bordering a building
envelope which encloses only portion of the building structure
and/or to adjacent buildings where, for example, the building abuts
an adjacent building. The building envelope may, for example,
enclose a group of one or more rooms, or a single floor.
[0079] Determining energy efficiency of a building may include
determining the thermal properties of the building envelope. In
particular, where a building envelope has a thermal mass, the
building may have several components of induced and residual heat
flow through the building envelope.
[0080] Thermal mass (e.g., heat capacity) may be considered to be
the property of an object to store heat and may be measured in
Joules per degree Celsius (J/.degree. C.), in British Thermal Units
per degree Fahrenheit (BTU/.degree. F.), and/or equivalent. The
thermal mass of an object may depend on the specific heat
capacities of the materials making up the object. Specific heat
capacity, denoted C, is often considered the materials heat
capacity per unit of the material, and may be specified per mass
(e.g., BTU/[.degree. F..times.lb.sub.m]), and/or per volume (e.g.,
BTU/[.degree. F..times.ft.sup.3]). Dense objects, such as brick and
stone, typically have a greater capacity to store heat than less
dense objects, such as wood or foam insulation. The building
envelope's thermal mass, M.sub.ENV, will be a function of the
material making up the buildings structure.
[0081] In addition to the M.sub.ENV of the building envelope,
thermal mass may also be present within the inside environment,
represented as object 801 having thermal mass M.sub.I, and within
the outside environment, represented by object 810 having a thermal
mass M.sub.O. Objects 801 and 810 may each represent a single
object, or may each represent a composite of the thermal masses of
multiple objects. Inside object 801, for example, may include
furniture, equipment, vehicles, people, warehoused goods, and other
movable or permanently affixed structures. Outside object 810 may
include other buildings, building structures, roadways, and other
movable and affixed objects.
[0082] Various embodiments include determining heat flow through
and/or into the building envelope. FIG. 8 includes various
illustrative examples of heat flow shown as heat paths 802 to 807
having respective heat flows Q.sub.ENV, Q.sub.EL, Q.sub.W, Q.sub.G,
Q.sub.P, and Q.sub.HVAC. Heat flow is often the quantity of heat
energy transferred per unit of time (e.g., W=J/s, BTU/Hr). Related
to heat flow, heat flux, denoted Q.sup.f, may be defined as the
quantity of heat per cross-sectional unit area, and may be measured
in watts per meter squared (W/m.sup.2), and/or other equivalent
units. The heat paths shown in FIG. 8 may move heat between the
outside environment 809 and the inside environment 808 and between
the environments and the thermal masses M.sub.EV, M.sub.I and
M.sub.O. Various embodiments include tracking of heat flow along
the various induced paths periodically over time.
[0083] Heat path 803-807 often represent induced heat flow, and
heat path 802 often represents a residual heat flow. Induced heat
flow may be the result of supplying or injecting energy sources
into the building envelope, which may then produce heat through the
sources consumption (i.e., a consumable product). Examples of
energy sources include electricity, fossil fuels, and people.
Induced heat flow may also result by mechanical means, such as
heating, ventilation, and air conditioning (HVAC) systems, which
often mechanically move heat in or out the building envelope to
control the climate within the building envelope.
[0084] Heat path 803, Q.sub.EL, of FIG. 8, represents heat flow
produced by electrical power supplied to the building envelope. In
various aspects, electrical power may be measured and/or its
distribution monitored as described with respect to FIG. 1 in order
to determine heat produced from its use within the building
envelope.
[0085] Referring back to FIG. 1, heat flow 803, Q.sub.EL, into the
building envelope may depend on the distribution and use of the
electrical power. The distribution system itself, consisting of
distribution point 106, and services 120-124, has electrical
resistance and may have a power loss in the form of heat according
to the equation of P=I.sup.2*R, where P is the power consumed, I is
the current passing through any particular path, and R is the
resistance of the path. Heat may also be produced in various forms
by the end load. For example, lighting 107-112, may produce heat
that is predominantly radiation heat, while computer equipment in
computer server room 114 may heat the interior environment
predominantly through conduction and convection. Other loads, such
as charging circuit 129 may not convert all of the electrical power
into heat, but may instead store the power in batteries, fuel
cells, or other storage device. Still, other loads may convert some
of the electrical power into mechanical work.
[0086] In various aspects, heat flow 803, Q.sub.EL, is determined
by measuring the flow of electrical power through one or more
meters and sub-meters, such as 102-105, and/or by monitoring one or
more loads, such as light/load meter 133 monitoring lighting 110.
It should be noted that the various loads and distribution paths,
such as HVACs 117-118, cell tower 116 and portions of services
120-122, may be outside of the building envelope, and thus, would
not add heat within the building envelope environment.
[0087] Heat path 804, Q.sub.W, in FIG. 8 may be another source of
induced heat flow produced by the flow of water through the
building envelope. Like all matter, water has the ability to store
heat, and has a heat capacity of 1 BTU/[.degree. F..times.lb.sub.m]
(approximately 4.2 J/(g*K)). As water flows through a building
envelope, the water may transfer heat to or from the inside
environment and/or the thermal mass of the building envelope. In
many situations, the amount of heat transfer from water may be
negligible, but in various embodiments, water has a
non-insignificant effect. For example, in hotter climates, water
which has been cooled through supply lines underground, may draw
heat from the inside environment 808 as the water passes through
pipes or through radiators within the building. In other
embodiments, hot water used to heat numerous buildings in a campus
system may be piped into a building envelope and through radiators
to dissipate the stored heat within the building envelope. In yet
other embodiments, water which has been heated within the building
envelope by heat produced from another energy source may carry heat
from the building envelope through drain pipes. In various
embodiments, water flow and temperature may be monitored in and/or
out of the building in order to calculate a heat flow 804, Q.sub.W,
resulting from the water flow.
[0088] Heat path 805, Q.sub.G, in FIG. 8 provides another source of
induced heat flow produced by the flow of fossil, bio, or synthetic
fuels. The fuel may be a gas, such as natural gas, biogas, propane,
butane, etc., may be liquid, such as compressed natural gas, liquid
propane, gasoline, kerosene, diesel, etc., or may be solid, for
example coal, wood, etc. Like water, heat flow may result from the
heat capacity of the fuel itself storing energy, transferring heat
in or out of the building envelope directly as a result of the fuel
flow. Accordingly, in various embodiments, temperature and fuel
flow in and/or out of the building are monitored by one or more
meters to calculate a heat flow Q.sub.G resulting from the heat
capacity of the fuel.
[0089] Like electricity, the heat path 805, Q.sub.G, may also
result from fuel flowing into the building environment, and then
being consumed to produce heat. For example, a building may have a
natural gas utility supply used for water heating, furnaces,
clothes dryers, cooking, and other various functions. The heat flow
Q.sub.G resulting from consuming the fuel will depend on the
quantity of fuel consumed and energy conversion efficiency of each
particular application within the building envelope. Accordingly,
one embodiment monitors distribution of the fuel to various
consumption points within the building envelope, and determines
heat flow based on known, estimated, or measured energy
conversion/efficiency factors. For example, natural gas has a known
energy conversion factor through combustion (e.g., 1000
BTU/ft.sup.3). In a cooking application using natural gas, one
embodiment may monitor the quantity of gas consumed by a stove and
use the known energy conversion factor to determine heat transfer
into the building envelope. In the same system, exhaust vents over
the stove may be monitored to determine air temperature and flow
from the cooking area to determine heat flow leaving the building
envelope through the exhaust. In various embodiments, the heat path
induced by the exhaust may be included in the heat flow calculation
Q.sub.G, or may be calculated as a separate heat flow factor. The
heat flow resulting from the exhaust would be determined in the
same way as water based on flow rate, temperature, and heat
capacity of the exhaust.
[0090] Heat path 806 having heat flow Q.sub.P includes another
source of induced heat flow resulting from the moving of self
contained heat emitting bodies being moved in and out of the
building. The most typical self contained heat emitting bodies are
people moving in and/or out of the building. In various
embodiments, doorways and other entrances to a building envelope
are monitored to count people entering or exiting. The monitoring
may exist only for building entrances, or may occur per floor, or
per room. Each person's contribution to heat flow Q.sub.P may then
be determined by estimating heat emission based on an average
person. In another embodiment, heat emission estimates of each
person may be based on more detailed monitoring which determines
size, height and/or weight of persons entering and exiting the
building, room, or floor. In yet another embodiment, each person's
heat emission may be estimated based on the amount of activity each
person exerts, which may be measured, for example, by using motion
sensors. In yet another embodiment, thermal detectors or cameras
may measure a person's heat signature or heat output to determine
that person's contribution to Q.sub.P.
[0091] Although Q.sub.P is described with respect to people, the
same embodiments may equally be applied to other illustrative self
contained heat emitting bodies such other animals. In yet another
embodiment, the self contained heat emitting bodies may include
automobiles moving in and out of parking garages or other spaces
within the building envelope. In monitoring the automobiles,
various embodiments may treat the automobile, drivers, and
passengers as one heat emitting body, or may distinguish each
automobile, driver, and passenger as separate heat emitting bodies
and/or based on the size and type of the automobile.
[0092] Heat path 807 having a heat flow Q.sub.HVAC represents heat
flow induced by Heating, Ventilation, and Air Conditioning (HVAC)
systems. HVAC systems, such as a heat pump, typically mechanically
move heat in or out of the building envelope to control the climate
inside the building envelope. In various embodiments, an HVAC
system may include electric heaters, natural gas furnaces,
hot/chilled water circulation, or other systems, which create heat
paths that include previously described heat paths, such as
Q.sub.EL, Q.sub.W, or Q.sub.G. For example, an HVAC system with
electric heaters would generate heat directly from an electric
utility service as described above with respect to Q.sub.EL. In
these embodiments, the heat path may be considered as either
Q.sub.HVAC or as one of the other described heat flows. In other
various embodiments having an HVAC system such as an air
conditioning system or a heat pump, electricity or other energy
source may be converted into mechanical energy to create a separate
heat path which forces heat in or out of a building envelope.
[0093] In various embodiments, heat flow Q.sub.HVAC may be
determined by directly monitoring input and/or outputs of the HVAC
system. For example, in a forced air furnace, intake and outtake
airflow, along with temperature of the intake and outtake air may
be measured with one or more meters to calculate the heat output of
the furnace. In other embodiments, the energy source (e.g.,
electricity) supplying the HVAC system may be monitored, and an
estimated, measured, or manufacture supplied conversion/efficiency
factor may be applied to determine the heat movement through the
building envelope based on the measured energy source.
[0094] As shown in FIG. 8, in addition to the induced heat paths
heat paths 803 to 807 having respective heat flows Q.sub.EL,
Q.sub.W, Q.sub.G, Q.sub.P, and Q.sub.HVAC, a building envelope may
also have a residual net heat path 802 having a heat flow
Q.sub.ENV. Heat flow Q.sub.ENV, often depends on the insulation
properties of the building structure.
[0095] In a simple embodiment, heat path 802 may be predominantly
through conduction of the building envelope and can be
characterized by a composite thermal resistance, or R.sub.VALUE, of
the material that makes up the building structure. R.sub.VALUE for
a particular material is described by the equation
R.sub.VALUE=(T2-T1)/Q.sup.f, where T2-T1 is the delta temperature
on either side of the material and Q.sup.f is the heat flux, or
heat flow per unit area, through the material. R.sub.VALUE for many
materials is well known and provided by the manufacturer.
[0096] Often, the building structure may not be a single material,
but a composite of multiple materials in layers. For example, as
shown in FIG. 14, a cross-sectional view of a building wall 1400
may include an outside concrete layer 1401, next to a thin air
layer 1402, followed by a sheathing layer 1403, an insulation layer
1404, and a drywall layer 1405. In this example a composite
R.sub.VALUE would be calculated as
R.sub.VALUE=R.sub.VALUE(CONCRETE)+R.sub.VALUE(AIR)+R.sub.VALUE(SHEATHING)-
+R.sub.VALUE(INSULATION)+R.sub.VALUE(DRYWALL). In practice, wall
construction is typically more complicated and may include many
more parts such as wooden or metal studs, epoxies, nails, pipes,
etc. To determine R.sub.VALUE of complicated wall assemblies, a
weighted average of each R.sub.VALUE of each material may be used,
or the composite R.sub.VALUE may be computed using modeling
software of the wall assembly.
[0097] In many buildings, R.sub.VALUE, based on conduction alone is
insufficient since many more factors contribute to the residual
thermal path 802. Radiation and convection from the atmosphere also
play a role, as well as air infiltration through doorways, windows,
vents, and cracks. FIG. 9 illustrates the building envelope of FIG.
8, but replaces thermal path 802 with a more comprehensive
illustration of residual thermal paths 901 to 909. In FIG. 9,
thermal path 901 having heat flow Q.sub.CDN represents the
conduction thermal path previously discussed. Heat flow Q.sub.CDN
will depend on R.sub.ENV, the R.sub.VALUE of the building envelope,
and on the temperatures T.sub.ENVI and T.sub.ENVO, which often are
the inside and outside surface temperatures 910 and 915
respectively of the building envelope. T.sub.ENVI and T.sub.ENVO,
in turn, may be determined by heat transfer from the inside and
outside environments 808 and 809, through radiation, conduction and
convection.
[0098] In the outside environment, radiation from the sun,
represented by Q.sub.RAD may be determined predominantly by the sun
position, obstructions which block the sun, atmospheric conditions
such as cloudiness and green house effects, and reflectivity of the
building surface. Time 920 and Date 921 may be used to determine
sun position. Weather forecasts and models may be used, or direct
measurements may be made at various locations on the outside of the
building envelope, to determine atmospheric conditions such as
brightness 916. From these factors, radiation hitting the building
envelope may be determined. The determined radiation along with the
known reflectivity of the surface may then be used to determine
heat energy transferred to the outside surface and/or the lower
temperature space or zone.
[0099] In addition to outside radiation, heating of the building
envelope's outside surface occurs through conduction 903 and
convection 902 having heat flow Q.sub.CDN and Q.sub.CVN,
respectively. Heat transfer through conduction can be determined by
the difference between the air temperature 919 and building surface
temperature 915 having temperatures T.sub.AIRO and T.sub.ENVO
respectively, and the respective thermal masses (i.e., heat
capacities) of the air and building envelope. The thermal mass of
the air may vary with humidity 918 (H.sub.O) and barometric
pressure 922 (P.sub.O). Heat transfer through convection 902 is
affected by the same factors as conduction path 903, but may
further be affected by atmospheric conditions such as wind 917
(W.sub.O).
[0100] In the same ways that heat is transferred from the outside
environment to the outside surface of the building envelope, heat
may be transferred from the inside surface of the building envelope
to the inside environment through radiation heat path 909,
conduction heat path 908 and convection heat path 907.
[0101] Radiation may also enter the building envelope directly
through heat path 905, Q.sub.RAD, which may comprise openings such
as doorways, windows, and/or other fenestration. Open doorways
would provide no resistance to radiation entering, whereas windows
will typically have a designed emissivity (e) which is a measure of
the amount of radiation reflected, and thus prevented from entering
the building envelope.
[0102] Building envelope may also have a residual heat path 906,
Q.sub.INF, resulting from air infiltration through openings in the
building envelope. Heat path 906 has a representative thermal flow
Q.sub.INF, which may be determined by the cross section and
positions of openings, and environmental factors such as outside
wind W.sub.O, inside and/or outside humidity, 918 (H.sub.O) and 912
(H.sub.I) respectively, and/or inside and/or outside barometric
pressures, 922 (P.sub.O) and 923 (P.sub.I) respectively.
[0103] Building envelopes are generally designed to minimize the
thermal paths 901-909 shown in FIG. 9. As previously discussed,
as-built structures may not and often do not meet the insulation
performance of a planned design after completion. Errors in the
designed thermal performance may be caused by design variations
that are not reflected in a model, construction of the structure
which is not to specification, incorrect assumptions on building
usage and weather, utility equipment which is not installed
correctly or functioning according to specification, insufficient
model fidelity, and numerous other factors. Further, a building's
energy performance may change over time due to the aging of
materials, modifications to building structures and systems, or
damage to the structures.
[0104] In addition, thermal mass M.sub.ENV of the building envelope
may impact the thermal performance of the building envelope by
serving as a heat reservoir for some of the conducted heat through
the building envelope, thereby damping or adding a time delay to
the conducted heat transfer between the inside and outside
environments. Likewise internal object 801 and external object 810
may have the same effect in dampening variations in the inside and
outside temperatures 911 and 919 respectively. For example, a house
in the countryside will be in the presence of a vastly different
outside thermal mass than that of an office building within a heat
island of a dense city, and thus the outside temperature of the
building in the city may be higher. Further, objects 801 and 810
may be moved, or new objects erected, such as constructing new
adjacent buildings. Because objects 801 and 810 may be moved or
erected, their thermal impact on building envelope 800 may change
over time.
[0105] Because of errors in the designed thermal performance and
the change in performance over time, various embodiments may
periodically determine the actual residual heat flow through the
building envelope. In one embodiment, heat flow and thermal
performance of a building envelope are determined by process 1000
as shown in FIG. 10. In step 1001, induced thermal paths 803-807
are determined as discussed above through monitoring and
measurement of the various energy sources entering and leaving the
building envelope. In step 1002, resultant induced thermal flows
Q.sub.EL, Q.sub.W, Q.sub.G, Q.sub.P, and Q.sub.HVAC are calculated
as disclosed above. In step 1003, the inside and outside
environments are measured. The measurements may occur periodically
at fixed time intervals, may occur in real-time, and may all be
synchronized with each other, and with the measurements of the
induced heat flows.
[0106] In step 1004, residual heat flow is determined from the
determined induced thermal flows and by measuring a change in the
internal and external environments. For example if the internal
environment stays static (e.g., temperature, pressure, and humidity
do not change), than the residual heat flow may be determined as
Q.sub.ENV=Q.sub.HVAC-Q.sub.EL-Q.sub.W-Q.sub.G-Q.sub.P. In other
embodiments, residual heat flow Q.sub.ENV may be determined by
taking into consideration the induced heat flows combined with
changes in the internal environment (T.sub.ENVI, P.sub.I, and
H.sub.I), changes in temperature of internal object 801, and/or
changes in temperature of the structure of building envelope
800.
[0107] Based on the determined residual thermal flow Q.sub.ENV, and
the measured environments, the actual thermal performance of the
building envelope may be determined in step 1005. The actual
thermal performance can be calculated as a composite thermal
resistance factor, R.sub.C, which not only includes an R.sub.VALUE
characterizing conduction, but also incorporates the other residual
thermal paths discussed above due to radiation, convection, and
infiltration. In various embodiments, measured R.sub.C may thus be
a real-time function of the measured environmental variables. In
other embodiments a single R.sub.C value may be determined from the
measured parameters, as a value defined over an average period,
and/or at predefined environmental conditions. In various
embodiments, the measured R.sub.C function or single R.sub.C value
may be specified in construction or sales contracts, as a design
metric or contractual obligation to meet by one party to the
contract.
[0108] In step 1006, an error may be quantified between the
measured R.sub.C and a modeled R.sub.C based on a modeled design.
In step 1007, a source of the error may be determined based on the
type of error found. For example, if the quantified error indicates
that thermal conduction was a primary factor in the error, than a
builder may conclude that a wall was not assembled according to
specification.
[0109] In step 1008, reports may be generated which detail the
errors. The reports may be provided at the conclusion of
construction of the building, periodically during operation of the
building to stakeholders, such as owners, building managers, and
leaseholders, or provided in real time through, for example, web
applications and analytical engines, which may continually
calculate and display R.sub.C which may vary based on changing
conditions.
[0110] In various embodiments, process 1000 may be performed in a
static manner, such that one or more of the induced thermal paths
may be inhibited. While inhibited, step 1003 may be performed to
determine residual heat flow by measuring over time, the inside
environment and/or building envelope temperature approaching
equilibrium with the outside environment. For example, electric,
gas, and water utilities may be cut off from the building, and all
ventilation from an HVAC system shut. Once induced heat flow is
inhibited, various environmental conditions may be monitored over
time, such as T.sub.AIRI, H.sub.I, B.sub.I, P.sub.I, T.sub.ENVI,
T.sub.AIRO, H.sub.O, B.sub.O, P.sub.O, T.sub.ENVO, W.sub.O, Time,
Date, temperature of the envelope, temperature of inside objects,
and/or temperature of outside objects. Where no thermal paths are
induced, any heat transfer is typically residual. R.sub.C may then
be determined as a function of time during which the two
environments approach equilibrium with each other. In one
embodiment, the observation of the measured environment may occur
over a limited specific interval after the induced heat paths have
been shut off. The change in temperature between the inside and
outside temperatures over the fixed interval may be specified as
the thermal resistance factor, R.sub.C. Alternatively, the maximum
amount of time for the inside environment to reach equilibrium with
the outside environment may be measured within a predetermined
threshold. The maximum amount of time to reach equilibrium under
the specific conditions may be specified as the thermal resistance
factor, R.sub.C. In either case, specific initial outside and
inside temperatures or other environment variables may be
specified.
[0111] In other various embodiments, process 1000 may be performed
in a dynamic manner, where changes in the induced thermal paths are
measured and tracked over time. For example, heat flow from
occupants may be tracked in real time so that R.sub.C may be
determined under conditions in which the building is often used. In
another example, heat flow in a hotel may be tracked in which
occupants, electricity, gas, and water are monitored. Determining
heat flow through process 1000 may show that excessive heat from
shower and/or laundry waste water is being lost from the building
envelope. In this sense, process 1000 may be used not only as an
audit of the residual thermal performance of the building envelope,
but also as an audit of unintended induced heat paths such as drain
water. Process 1000, in one embodiment may thus be used to
determine how both residual and induced heat paths may be
improved.
[0112] In both static and dynamic embodiments, process 1000 may be
repeated continuously at periodic intervals (e.g., weeks, days,
years, months, etc.) through loop 1011 in order to track changes in
the thermal performance of a building envelope over time. Changes
may be the result of aging of materials, defects in materials,
modifications to building structures and systems, or damage to the
structures. Changes may also result from changes in the environment
(e.g., seasonal changes, solar cycles), or may result from changes
in the building envelop and adjacent structures (e.g., new adjacent
buildings, changes in occupancy, remodeling, etc.).
[0113] In both static and dynamic embodiments, process 1000 may
also be applied at different stages of construction of the building
envelope. For example, process 1000 may be performed when the outer
layer (e.g., brick, concrete) has been completed, but interior
build out has not yet been completed. In this way, thermal
performance (e.g., thermal resistance and thermal mass) may be
evaluated for different components and layers of the building
envelope.
[0114] In either the static and dynamic embodiments for determining
R.sub.C, thermal mass of M.sub.ENV and M.sub.I may also be
determined in step 1009 by monitoring the rise in temperature of
building envelope 800 and object 801, and by monitoring any delay
in the conductance of heat through from the inside environment and
outside environment. For example, using the methods discussed
above, induced and residual heat flow may be measured over a fixed
period of time and integrated to determine the total amount of heat
energy flowing into and out of the building envelope. A composite
thermal mass of the building structure, M.sub.ENV or M.sub.I (or a
composite of the two) may be determined as the difference between
the heat flow in and out of the building per degree temperature
change of the building structure over the measuring period. We note
here that M.sub.ENV or M.sub.I, may not be thermal mass in strict
definition of the term since the temperature of the building
structure may vary from location to location within the building
envelope. Accordingly, in various embodiments, we use a thermal
mass factor M.sub.C, which may be based on estimates or averages of
heat flow and temperature changes. In one example, M.sub.C may be
defined in terms of energy storage of the building envelope with
respect to a change in the inside environment temperature (i.e.,
.DELTA. T.sub.AIRI) at equilibrium, given otherwise static
environmental conditions.
[0115] We have simplified the calculation of the thermal mass
M.sub.ENV, and thermal mass factor M.sub.C, however in some
embodiments a more complicated measure the building ability to
store energy. For example, heat flow Q.sub.W may be more
efficiently transferred to the thermal mass M.sub.ENV than heat
flow Q.sub.G. Therefore, in certain embodiments, the thermal mass
M.sub.ENV, and thermal mass factor M.sub.C may be employed which is
based on the building envelope's energy storage properties across
different environmental conditions, with the different types of
heat flows.
[0116] The determined thermal resistance factor R.sub.C and thermal
mass factor M.sub.C may be applied in various applications in step
1010. In the various applications of step 1010, a one-time
measurement of R.sub.C and/or M.sub.C, a periodically/continuously
measured R.sub.C and/or M.sub.C, and/or measured changes in R.sub.C
and/or M.sub.C over time may be applied. In one illustrative
application, the method of measuring R.sub.C and/or M.sub.C and a
specific measured R.sub.C and/or M.sub.C may be specified in
construction or sales contracts as a performance metric. Specific
damages may further be specified in the contracts based on R.sub.C
and/or M.sub.C. For example, the contract may specify how to
calculate a monetary loss in heating or cooling a building based on
the delta between a specified R.sub.C and/or M.sub.C in the
contract and a measured R.sub.C and/or M.sub.C. In some variations,
specified, forecast, or measured environmental variables may
further be considered in determining the monetary loss. In another
illustrative application, the specific methods for determining
R.sub.C and/or M.sub.C may be used as industry standards to compare
different structures, or to establish minimum build criteria.
[0117] In another variation, resistance factors R.sub.C and thermal
mass factors M.sub.C may be collected from a number of buildings
may be posted on a website or provided in a publication for
providing comparative performance data between buildings. The data
may provide R.sub.C and/or M.sub.C or some performance metric
derived from these factors, and may organize the data in a manner
to rank the building in order of performance. The builders of the
buildings may be ranked in a similar manner based on the buildings
they construct. The builder's performance may be based on one
building or a number of building the builder has constructed. In
this manner, historical R.sub.C and M.sub.C data may be used for
certification purposes of different builders.
[0118] In another illustrative embodiment of step 1010, R.sub.C
and/or M.sub.C may be periodically tracked in real-time and used in
a closed loop system for autonomously controlling a climate control
system (e.g., HVAC) of the building. The periodically tracked
R.sub.C and/or M.sub.C may be used in conjunction with measured or
forecasted environment conditions, and/or in conjunction with
varying cost rates of energy source to control the climate control
system. For example, on a night during off-peak rates for
electricity, when cold weather is forecast for the next day, the
inside environment may be pre-heated such that energy is stored in
the thermal masses of the inside object 801 and the building
envelope 800 for later dissipation into the inside environment
during the day when the building is being occupied and when
electricity is at its peak rate. In this example, the determined
R.sub.C and M.sub.C may be used to determine the amount, duration,
and time to pre-heat the building in order to optimize cost
savings. Because R.sub.C and M.sub.C, energy rates, and thermal
mass inside the building envelope may change over time, the optimum
parameters for pre-heating may change as well. R.sub.C and/or
M.sub.C may be applied to pre-cooling as well. The determined
R.sub.C and/or M.sub.C may show that no cost savings could be
achieved because the thermal insulation of the building envelope is
insufficient to retain heat for a required amount of time.
[0119] In various aspects, dynamically determining R.sub.C and
M.sub.C may provide a means for managing use of the building. For
example, R.sub.C and/or M.sub.C may be determined on a room by room
basis, and show that some areas of the building are inefficient to
heat or cool. Building use may be adapted such the spaces with poor
R.sub.C and M.sub.C may be used for storage or other purpose where
climate control is less important.
[0120] Referring to FIG. 1, to perform the steps of process 1000,
the induced heat paths may be monitored. Using the electrical heat
path as an example, distribution systems may be designed to support
specific users, and/or specific tenants of the facility. The design
of the distribution system may provide individual tenants and
individual employees of the tenant their own supply feed,
distribution service, and/or different combinations thereof such
that use and/or generation of an energy source by the individual
tenant/employee may be uniquely measured using a meter on the
tenant's/employee's supply feed or using a sub-meter on the
tenant's distribution service. In various aspects, such monitoring
of individual tenants or portions of the facility may be used to
determine induced heat flow for building envelopes enclosing only a
portion within the overall building. In various embodiments,
induced heat flow may be monitored on a per tenant basis. For
example one tenant may have several more occupants (e.g., self
contained heat emitting bodies), and thus induce greater heat flow.
Lease rates may be proportioned between tenants based on the
relative induced heat flows.
[0121] In various embodiments, meter 104, sub-meter 105, and
light/load meter 133, may be added to a supply feed, distribution
service, or particular load respectively, to measure and record
supply and consumption of the energy source over time. From these
measurements, analysis may be performed according to certain
embodiments to determine the induced heat flow for the building
envelope of the entire building or for a smaller volume within the
overall building.
[0122] Meter 104, sub-meter 105, and sensor 133 may be variously
configured. In one embodiment, meters 104, 105, and 133 may include
one or more sensors. Various sensors appropriate for measuring the
consumption and/or supply of the energy source will differ
depending on the energy source being measured. As previously
discussed, in the electrical distribution system in system 100, the
sensor may be an inductively coupled transformer, a current shunt,
or other appropriate sensor for measuring power, electrical current
and/or voltage. In other distribution systems for other energy
sources such as natural gas and water, appropriate flow meters may
be used. For people or automobiles, thermal sensors, thermal
imaging systems, imagers, etc. may be used. While meters 104, 105,
and 133 are described with respect to electrical power, the various
embodiments including the collection and processing of data will be
the same regardless of the energy source.
[0123] As previously described with respect to meters 104 and 105,
sensor 133 may further include a computing platform to operate the
sensor, and accumulate pulse inputs (periodic measurements) from
the meters and sensors. Meter 133 may include several sensors and
accumulate data from several different paths in the distribution
system. As an example, meter 133 may include a circuit board with
10 sensor channels for sensors which may each collect pulse data in
parallel. A processor on the circuit board may read each channel
and accumulate data in the same and/or separate memory devices
(e.g. registers) for each channel. The meter 133 may further have a
data display which scrolls periodically and/or continuously to
illustrate the pulses per channel. In addition to the data display,
meter 133 may have buttons or other inputs, which can be used for
on-site programming and/or trouble shooting. After on-site
programming/trouble shooting, further programming may be from a
remote location and/or computer.
[0124] In various aspects, sensor 133 may transmit data to a
server/workstation or other computing device as previously
described with respect to meters 104 and 105. Once collected, the
server/workstation may compile the data from each sensor/channel
into time sequences of data. The detailed data sequences and graphs
associated with individual meters may help pinpoint particular
induced thermal paths.
[0125] FIG. 11 illustrates a process, according to some
embodiments, to analyze data sequences to determine induced heat
flow from various energy sources. Process 1100 starts at 1101 where
the supply of an energy source is measured in a distribution system
by a meter (e.g. 104). The measured values may then be transmitted
from the meter in step 1102, and subsequently received by a
processor (e.g. server/workstation 127) in step 1103. Steps 1101,
1102, and 1103 may be accomplished as already described with
respect to FIG. 1 and may result in one or more data sequences.
Steps 1101, 1102, and 1103 may occur on a pre-determined scheduled
basis, as a result of the processor requesting the measured data
from the meters, or both. In step 1104, conversion factors of the
energy supply to heat production are retrieved from a database.
Each conversion factor is a characterization of a loads production
of heat from consumption of the energy source. In step 1105, the
processor uses the conversion factors and the measured data
sequences to determine the induced heat flow through the building
envelope. These values may then be used in step 1002 of process
1000.
[0126] Process 1100 may be augmented with additional steps of
process 1200 illustrated in FIG. 12, and described with respect to
FIG. 13, for measuring environmental conditions and self-contained
heat emitting bodies. In this illustrative embodiment, process 1200
starts at 1201 by measuring the environmental conditions
illustrated in FIG. 9. For example, as illustrated in FIG. 13,
instruments 1303 may be used to measure inside environmental
conditions such as such as T.sub.ENVI, P.sub.I, and H.sub.I, and
instruments 1302 may be used to measure outside environmental
conditions such as T.sub.AIRO, H.sub.O, B.sub.O, P.sub.O,
T.sub.ENVO, and W.sub.O. Step 1201 may also include acquiring
environmental conditions, weather forecasts and/or other data such
as heating and cooling degree day forecasts and utility rate data
from external sources such as web servers which compile and
distribute such data.
[0127] Other operating conditions such as the presence or heat
emission of a self contained heat emitting body may be monitored in
step 1202. In step 1202, self contained heat emitting bodies 1305
to 1308 within room 1300 may be counted by a sensor 1304.
Alternatively, heat signatures of the heat emitting bodies may be
detected using thermal imaging device 1309. These sensors may
monitor self contained heat emitting bodies as they enter and leave
the building, and/or as they move from room to room within the
building. FIG. 13 illustrates the heat emitting bodies as people,
but the bodies could equally be another animal, an automobile,
and/or other object that emits heat generated from internally
stored energy.
[0128] In various embodiments, instruments 1302 and 1303 may
include installed wired and/or wireless temperature transmitters
(Infrared, RTD, thermistors, or any temperature measurement
platform) on the inside, outside and within the building structure
forming the building envelope. These sensors may be arranged in a
matrix format and/or may be arranged to measure temperatures at
particular points of interest. FIG. 9 details illustrative aspects
of temperature instruments within 1302 and 1303.
[0129] In FIG. 14, wall 1400 is illustrated with four layers
1401-1405. As previously described, wall 1400 may include an
outside concrete layer 1401, next to a thin air layer 1402,
followed by a sheathing layer 1403, an insulation layer 1404, and a
drywall layer 1405. In various embodiments represented by
temperature instrument 1406, temperature sensors may be placed
between each material layer making up a wall or enclosure.
Temperature instrument 1406 may for example consist of six
temperature sensors with each sensor embedded between each of layer
as shown (dotted bubble provided for clarity). The sensors are
coupled by wires or wireless transmitters through the layers to a
measurement device 1407, which may be located on either side of the
wall. With temperature instrument 1406 temperature may be detected
at multiple depths in the wall simultaneously, periodically, and in
real-time. Instrument 1407 may measure temperature data from the
sensors periodically and relay the data as discussed with respect
to FIGS. 4 and 12.
[0130] The sensors of temperature sensor 1406 may be installed
during installation of the material layers, or the sensors may be
installed by the material manufacture as an integral part of the
material layers. For example, in one embodiment a manufacture of
rolled fiberglass insulation, may imbed a string of wired sensors
spaced and taped along the paper backing of the insulation
roll.
[0131] In another illustrative embodiment of a temperature
instrument, a temperature detection probe 1408 having the ability
to measure temperature along incremental distances of the probe is
inserted through the multiple layers of the wall. The probe may be
a thin cylindrical (or other shape) rod which is inserted into a
hole drilled or built into the wall. With the probe, temperature
may be detected at multiple depths in the wall simultaneously,
periodically, and in real-time. Instrument 1407 may measure or
receive temperature data from probe 1408 via wired or wireless
connection and periodically relay the data as discussed with
respect to FIGS. 4 and 12.
[0132] Using the illustrative temperature instruments 1406, 1408,
or other similar instrument, a gradient of temperature can thus be
detected through the wall, and a more detailed view of the wall's
thermal resistance (R.sub.VALUE), thermal mass (M), thermal
resistance factor (R.sub.C), and thermal mass factor (M.sub.C) can
be determined.
[0133] In other aspects, instruments 1302 and 1303 may be used
during the construction of the building envelope to determine the
wall's thermal resistance (R.sub.VALUE), thermal mass (M), thermal
resistance factor (R.sub.C) and thermal mass factor (M.sub.C). For
example, a thermal imaging camera may be used during the
construction of a wall. As each layer of a wall is assembled, the
thermal surface temperatures of the wall may be measured using the
thermal imaging camera, or other thermal sensor. In this way, the
contribution of each layer to the walls thermal resistance, thermal
mass, or the thermal resistance factor may be independently
verified. This may provide an advantage, for example, in detecting
faulty or incorrectly installed material before the entire wall is
assembled. As a further advantage of measuring each layers
contribution, either before or during the completion of the wall,
these embodiments are able to pinpoint the cause or causes of under
or over thermal performance by a particular component of the
construction.
[0134] The measurements obtained in process 1200 may be accompanied
by meta-data such as time stamps or time intervals such that the
operating conditions may later be correlated to data captured in
processes 1000 and 1100. In step 1203, the measured environmental
and other captured operating conditions are transmitted to the
processor. FIG. 13 illustrates an exemplary data collection node
1301 collecting the measured values and transmitting them to the
processor in server/workstation 127 through network 125/126, which
may be the same as the communication paths describe with respect to
FIG. 1, or which may different than, but of the same types as those
of FIG. 1. Data collection node 1301, may be the same as meters
104, 105, and 133, or may be some other computing platform
operating in the same manner as 104, 105, and 133 over the same
types of communication links to transfer data to server/workstation
127. Server workstation 127 may be remotely located, or may be
located within the building envelope. In step 1204, the processor
in server/workstation 127 receives the transmitted data. The data
may then be used in steps 1003 and 1004 of process 1000.
[0135] Returning to FIG. 14, plot 1409 illustrates the output of
temperature sensors measured over time. Each of the plot line
1410-1415 represents temperature measured by the sensors on each
surface of layers 1401-1405. The distance between each plot line
represents the temperature difference through a layer. As is shown,
the temperature difference through each layer may vary over time at
different rates and magnitudes than other adjacent layers,
depending on that layers thermal resistance and thermal mass. Using
the plots, the thermal resistance and thermal mass may be
determined from the plots in the same manner as previously
discussed with respect to a single object.
[0136] Processes 1000, 1100, and 1200 may be performed by an
autonomous processor that works continuously collecting data (e.g.,
pulse data), and determining R.sub.C and induced and residual heat
flow in real-time or near real-time, and generating reports on a
fixed schedule (i.e. daily). These reports may be generated in the
form of hard-copies and mailed, in electronic form and sent via
electronic mail, text message or other form of electronic transfer,
or in the form of voice messages sent via a phone line. Further
embodiments may allow the reports, including billing information
and graphical data to be displayed on any customer interface
device, desktop, laptop, PDA, Blackberry and or client internet
portal, and may be further provided through a website hosted by the
processor. By serving the data from a website, an interested party
may be able to view usage and cost data and graphic displays in
real-time and/or near real-time. As referred herein, "real-time"
refers to updating the usage data as it is collected and calculated
with little and/or relatively little delay other than the time it
takes to process and/or transmit the data. The amount of delay may
be a designed limit on processing time, such that the data may be
used in closed loop control such as for use in controlling a
climate control system. The delay may also simply be dependent on
the resources available in measuring, transferring, and processing
the data. For the purposes of this application, "real-time" and
"near real-time" refers to the same concept in processing data.
[0137] In some embodiments, the signatures of one or more building
systems may be used to analyze a building's energy performance
and/or to optimize a building's energy efficiency. In these
embodiments, by analyzing a signature of a building system's access
to a consumable product as measured by, e.g., meter 104 or
sub-meter 105 (FIG. 1), an approximate start and stop time of one
or more building systems may be determined. Knowing the approximate
start and start time of these systems allows a building manager,
owner, etc., to adjust the start and/or stop time of the building
system to more appropriately coincide with user's use of the
particular building system. In such embodiments, the building can
ultimately reduce its consumption of the consumable product because
the system is not operating during hours when it is not needed
and/or efficiently ramps up and down after peak operating
hours.
[0138] By way of example, in some embodiments a start time of an
HVAC system may be adjusted to reduce consumption of a consumable
product (e.g., electrical energy). Specifically, for most
buildings, an HVAC system is not operated and/or not operated at
peak levels, around the clock. That is, the system is only
operated/operated at peak levels when a majority of tenants, users,
and others are occupying the building such that costs associated
with the system may be reduced. In such embodiments, during times
when an HVAC system is not operating, an internal temperature of a
building will approach an outside temperature and/or be at a
reduced temperature depending on the contract with the tenant.
Specifically, through the heat transfer principles as described
above in connection with FIG. 8 and FIG. 9, heat will permeate
through the building's envelope, ultimately causing the internal
temperature, T.sub.AIRI, to approach an outside temperature,
T.sub.AIRO, if the HVAC system is not started back up. Thus, in
warm weather, the internal temperature T.sub.AIRI of a building
will continually rise when the HVAC system (e.g., the air
conditioning system) is not operating. Similarly, in cool months,
the internal temperature T.sub.AIRI of the building will
continually drop when the HVAC system (e.g., the heating system) is
not operating.
[0139] However, in order to return the building to a desired
operating temperature when a majority of tenants, users, and others
ultimately return, the HVAC must be activated in advance of an
occupancy time. Used herein, occupancy time broadly refers to a
desired time by which to have an internal temperature of a building
at a desired occupancy temperature. In commercial leases, this may
be specified in the lease agreement. Accordingly, a landlord may
agree to have the building cooled/heated to an agreed temperature
by, e.g., 7:30 AM each morning. In other embodiments, an occupancy
time simply refers to a time by which a landlord, owner, manger,
etc., wants the building cooled/heated to a specified temperature.
In order to ensure the internal temperature of the building is at
the desired occupancy temperature by this occupancy time, the HVAC
system will need to be started in advance of the occupancy time in
order to sufficiently heat or cool the air inside.
[0140] Traditionally, start times for building systems have been
largely arbitrary. Returning to the example of HVAC systems, the
system may be programmed to start at the same time each day and run
until a desired internal temperature is met. This arbitrary start
time may be a time well before the occupancy time which the
owner/manager believes is necessary to reach the desired
temperature by the occupancy time. For example, an owner/manager
may program the start time as 5:00 AM each morning. Regardless of
the outside temperature, the building will thus start an HVAC
system at 5:00 AM and run until a desired internal temperature is
met, at which time the system will reach a steady state to maintain
the temperature. In typical situations, the system turns off once
the desired temperature is reached and then restarts at some later
time depending on numerous parameters. On some days, the desired
internal temperature is far from the external temperature (e.g.,
particularly hot or cold days) and thus a 5:00 AM start time may be
appropriate such that the HVAC system has ample time to reach the
desired occupancy temperature by the occupancy time. However, on
other days, the desired internal temperature may be much closer to
the outside temperature (e.g., mild days) and thus such an early
start time may be inefficient. More specifically, starting the HVAC
system at 5:00 AM on these days may result in heating/cooling the
building well ahead of what is necessary to reach the desired
temperature by the occupancy time. In still further embodiments,
the predicted outside temperature in addition to the current
outside temperature may be considered particularly where extended
periods are required to return the building to the desired
temperature.
[0141] According to some aspects of the disclosure, a signature of
one or more building systems' access to a consumable product may be
analyzed to determine the building's energy performance and/or
determine an appropriate start time for the one or more building
systems. Again referring to FIGS. 1-2, in these embodiments, access
to a consumable product may be measured by one or more meters 104
or sub-meters 105. A curve, such as graph 200, of the access may be
analyzed by, e.g., a processor in server/workstation 127, to
determine if one or more building systems are being started too
early. Returning to the example of an HVAC system, the HVAC system
may operate on, e.g., electricity. Accordingly, a signature of the
system's access to electricity may be analyzed to determine
approximately when the HVAC system stops and/or reaches a steady
state condition (corresponding to a time at which the building
reaches a desired occupancy temperature). This time may then be
compared to a desired occupancy time, and, if this time is well in
advance of a desired occupancy time, a start time for the system in
subsequent days may be adjusted such that the system operates more
efficiently. Thus, the control system may include a feedback
control loop utilized to maximize efficiency based on actual
experience in real world situations. This feedback control loop can
learn from past experience based on current outside temperature,
outside and inside temperature trends during the cycle, final
outside temperatures, and predicted outside temperatures from, for
example, a weather forecast. In some embodiments, one or more
pattern matching algorithms, artificial intelligence routines,
regression analysis, intelligent agents, other learning algorithms,
and/or feedback control loops may be employed to maximize the
efficiency of the heating and cooling cycles. Further, the system
may choose between different heating and cooling options to
determine how may resources to apply to the system, whether to
switch to an alternative heating/cooling system such as gas, heat
pump, outside air intake, and/or air conditioned air.
[0142] In some embodiments, one or more of the above-described
methods may be used to isolate a signature of a building system
(e.g., an HVAC system) such that the isolated signature may be
analyzed to determine the stop time of the system. In such an
embodiment, the signature of, for example, one or more components
of the HVAC system may be analyzed as discussed herein to determine
to the stop time (e.g., approximate time at which occupancy
temperature was reached). As discussed above, one or more control
systems may include a feedback control loop utilized to maximize
efficiency based on actual experience in real world situations on
each isolated HVAC system. This feedback control loop can learn
from past experience on each isolated HVAC and/or subsystem based
on current outside temperature, outside and inside temperature
trends during the cycle, final outside temperatures, and predicted
outside temperatures from, for example, a weather forecast. In some
embodiments, one or more pattern matching algorithms, artificial
intelligence routines, regression analysis, intelligent agents,
other learning algorithms, and/or feedback control loops may be
employed for each HVAC system and/or subsystem to maximize the
efficiency of the heating and cooling cycles. These units may also
interface directly to control systems in the HVAC systems and/or
subsystems using any suitable technique and/or interface such as a
wireless and/or wired connection including a serial port, fire
wire, and/or other suitable interface.
[0143] In still further embodiments, a signature characterizing the
system's HVAC access may not be isolated from, e.g., a signature of
the overall building. Specifically, for pre-occupancy times (e.g.,
times when many of the tenants, users, and others are not using the
building) the predominate system consuming the consumable product
may be, e.g., an HVAC system. Thus, if graph 200 from meter 104
(representing the entire building's access to the consumable
product) is smoothed (using, e.g., one or more low pass hardware
and/or software filters and/or other smoothing and/or isolating
technique such as other submeters), the graph may be configured to
have an overall positive slope when the HVAC system is accessing
the consumable product (corresponding to the HVAC system consuming
electrical power) which may terminate at the moment the HVAC system
reaches the desired temperature and moves into a steady state
condition (e.g., stops due to meeting the desired temperature).
Thus, by looking at graph 200 during pre-occupancy times, a time
when the slope of the signature becomes negative may be interpreted
as an approximate stop time for the HVAC system without the need to
actually measure via a submeter the signature of the HVAC system.
If this stop time is well in advance of a desired occupancy time,
then the system is operating inefficiently. Put another way, when
the HVAC system stops, the building is up to temperature. Thus, if
this time is well in advance of a desired occupancy time, the
building will have been heated/cooled prematurely (because many of
the tenants, users, etc., are not yet occupying the building). This
can be estimated in certain examples from the techniques described
herein such as graph 200. Some embodiments may use signatures from
multiple days to determine the thermal performance of a building.
In these embodiments, the determined thermal performance may be
used to determine, e.g., an appropriate start time for a building
system (e.g., HVAC system). By analyzing the signatures for
multiple days under a variety of operating conditions (e.g.,
differing outside start and stop temperatures), predicted
temperatures, and/or thermal properties of the building, a control
system may estimate the optimal start/stop times for the HVAC
system. For example, in one embodiment a plurality of ramp-up times
for a corresponding plurality of outside temperatures and/or
predicted outside temperatures may be analyzed to approximate
thermal properties of the building (e.g., an approximate R-value).
Thus, by analyzing data for a continuous, variable, fixed, and/or
sliding window period of time (e.g., two weeks), inherent thermal
properties of a building may be approximated and utilized when
determining appropriate future ramp-up times as discussed more
fully below. The period of time may be refined and updated over
time and/or tracked to determine any anomalies such as tenants
blocking open doors to terraces. Once the approximate stop time of
the system is approximated for one or more days, an appropriate
start time for the system in subsequent days may be determined in
order to increase the energy efficiency of the building.
[0144] Referring to FIG. 15, an exemplary process 1500 is shown
which may be variously implemented such as for controlling a start
time of a building system according to one or more aspects of the
disclosure. At step 1501, a plurality of measured signatures (e.g.,
each at corresponding operating conditions such as inside
temperature, outside temperature, predicted temperature) may be
determined. Each signature may characterize various operating
characteristics such as one or more building systems' access to a
consumable product (e.g., an HVAC system and/or subsystem) during
(e.g., a fixed, sliding, variable and/or predetermined) time
interval for the corresponding operating condition. For example, a
signature characterizing an HVAC system's access to electrical
power during a predetermined time interval prior to an occupancy
time of the building may be analyzed for a plurality of days
(corresponding to a plurality of operating conditions) in order to
determine optimal predicted parameters which may be utilized to
operate the HVAC system. Optionally, the signature(s) may be
further isolated from one or more signatures characterizing more
than one users' access to the consumable product (e.g., measured at
meter 104) at step 1501.
[0145] Using these signatures, optimal ramp times for each
corresponding signature may be estimated at step 1502. The
estimated optimal ramp time may refer to a time needed for the
system to effectively operate before an occupancy time. Whether the
prediction was accurate may be determined and this information may
be fed back into the feedback control system in exemplary
embodiments to achieve a better prediction for future operations.
For example, in the case of an HVAC system, the optimal ramp time
may include the time needed for the HVAC system to appropriately
heat/cool the internal temperature of the building prior to the
occupancy time of the building for the given operating conditions
(e.g., outside temperature, predicted temperature, heating,
cooling, which HVAC system/subsystem is being utilized) without
heating/cooling the building too far in advance of the occupancy
time. In some embodiments, the optimal ramp time may be the exact
time needed, nearly exact time needed and/or an approximation of
the time needed for the building system to heat or cool a building
by the desired occupancy time at a corresponding outside
temperature. In other embodiments, the optimal ramp time may refer
to the time needed to heat or cool the building by the desired
occupancy time plus a built-in "buffer" (e.g., 30 minutes, 60
minutes, etc.). The buffer time may be included in the control
system and/or adjusted based on contractual requirements. For
example, the feedback control system may determine that there are
instabilities in the feedback loop so that it is not possible to
predict the exact time in which to start the ramp up. In these
circumstances, where past experience was not accurately able to
predict the start time with precision, the feedback control system
may determine an amount of desired buffer time. This time may be as
little as one minute or may be 30 minutes or more. In any event, in
embodiments where a buffer is utilized, it may be either
automatically and/or manually configured. In these embodiments, the
buffer may be utilized to help ensure the building will reach a
desired temperature before the occupancy time even if there are
unforeseen outside temperature changes (e.g., a sudden drop of
outside temperature thus requiring more time for an HVAC system to
properly heat a building) and/or someone left a door and/or windows
open interfering with the desired ramp time. The optimal ramp time
in certain embodiments may be equal to a time interval between a
determined stop time of a building system and a determined start
time of the building system plus any desired buffer.
[0146] At step 1503 the current operating conditions are received.
For example, the current outside temperature may be received. As
presented above, on particularly cool or warm days, a building HVAC
systems will require more time to heat/cool the building than on
more mild days. Accordingly, at step 1503 the current operating
conditions (e.g., outside temperature) is retrieved such that an
optimal ramp time (e.g., an appropriate time to start the system
ahead of an occupancy time) can be determined at step 1504. In some
embodiments, a lookup table or other suitable storage means may be
utilized to store an optimal ramp time for each corresponding
operating conditions, and at step 1504 the received current
operating conditions will thus be compared to exemplar operating
conditions (e.g., outside temperature) stored in the lookup table
and the corresponding optimal ramp time of exemplar ramp times will
be selected. In other embodiments, an appropriate optimal ramp time
may be determined mathematically. Specifically, using a plurality
of received signatures for a plurality of corresponding operating
conditions, approximate thermal properties (e.g., an approximate
R-value, etc.) for the building may be determined and utilized to
determine optimal ramp times for subsequent days according to the
specific operating conditions
[0147] At step 1505, a building system start time may be determined
using suitable techniques discussed herein such as the
predetermined occupancy time and the determined optimal ramp time.
In this example, the building system start time may be the desired
occupancy time less the determined optimal ramp time. Finally, at
step 1506, the building system may be started at the determined
start time, such that the building system will efficiently bring
the building to an appropriate occupancy temperature by a desired
occupancy time. Returning to the example of an HVAC system example,
the HVAC system may be started at the determined start time such
that it is given enough time (plus any applicable buffer as
described herein) to heat/cool the building such that the building
reaches a desired occupancy temperature by the desired occupancy
time.
[0148] Similarly, aspects of this disclosure may be used to
determine one or more building systems' (e.g., HVAC system) access
to a consumable product (e.g., electrical power) at an end of an
occupancy period (e.g., the end of a day), and accordingly analyze
the one or more systems' energy performance with respect to
stopping the one or more system each day. More specifically,
knowing a predetermined departure time (e.g., a time of the day
after which the building no longer must be kept at a desired
temperature) a signature of the one or more systems' access to the
consumable product may be analyzed to determine a time at which to
stop a building system for the remainder of the day.
[0149] According to some aspects of the disclosure, metrics
associated with one or more building systems' access to a
consumable product may be determined to quantify the building's
energy performance. For example, in one embodiment a controllable
load may be determined to quantify the building's energy
performance. A controllable load may be variously configured. In
one embodiment, the controllable load may be one which a building
owner, manager, etc., may eliminate either by turning off unneeded
systems and/or allocating a consumption of a consumable product to
an appropriate user through the use of one or more sub-meters
105.
[0150] In one embodiment, a controllable load may be determined by
first determining a theoretical unoccupied load. A theoretical
unoccupied load may be variously determined but in one example it
may be a minimum theoretical consumption of a consumable product
obtainable during a predetermined period of time (e.g., an
unoccupied time of the building). For example, a theoretical
unoccupied load may be determined overnight in a building where
most or all of the tenants only occupy the building during the day.
The theoretical unoccupied load in this example represents access
to a consumable product by systems that cannot be eliminated due to
requirements by building code or for other similar reasons. For
example, "Exit" signs may consume, e.g., electrical power, however
building code may require these signs remain on even during periods
of no expected occupancy. Other systems may be required to remain
on as well, such as, e.g., emergency lighting and the like, and/or
some minimum HVAC activity to keep the building above some minimum
temperature as determined by contract or building codes. In this
example, the next step is to calculate an actual unoccupied load.
An actual unoccupied load may be variously defined but in this
example it may be determined as the amount of a consumable product
accessed during the predetermined period of time when the
theoretical unoccupied load is determined (e.g., overnight). The
actual unoccupied load may represent the amount of consumable
product actually accessed during this time. For example, computer
server room 114 may operate twenty-four hours a day, seven days a
week. Thus, even during "unoccupied" periods, computer server room
114 may still access a consumable product (e.g., electrical power).
Thus, in most situations, an actual unoccupied load will be greater
than a theoretical unoccupied load.
[0151] Next, any sub-metered loads may be taken into consideration
to isolate them from, for example, the HVAC system. In this way,
any access of a consumable product above the theoretical unoccupied
load may be allocated to the user responsible for the access.
[0152] In this example, the controllable load represents the total
amount of consumable product accessed that is not attributable
either to systems that cannot be turned off (e.g., "Exit" signs,
emergency lighting, etc.) or to sub-metered access by one or more
users, e.g., server rooms. This load is considered "controllable"
because it represents opportunity. More specifically, a building
owner, manager, etc., can either turn off systems accessing a
consumable product that are not required to remain on during a
period of no occupancy and/or use one or more sub-meters 105 to
meter access to the consumable product by one or more users and
accordingly allocate the cost of the consumable product to the
responsible user. In this example, the controllable load may be
equal to the actual unoccupied load less the sum of the theoretical
unoccupied load and the sub-metered load. This controllable load
may be presented in any desirable unit for quantifying access to a
consumable product, such as, e.g., kWhr, Therm, Gallons, Lumens,
etc. Thus, according to some aspects of the disclosure, access to a
consumable product can be analyzed to determine, for example, a
controllable load which can be used to analyze a building's energy
performance.
[0153] In addition to or in place of a controllable load, a
controllable load score may be determined in other examples. A
controllable load score is related to the concept of controllable
load in that it compares a theoretical unoccupied load and a
sub-metered load with an actual unoccupied load to determine a
building's energy performance. In some examples, a controllable
load score may be presented as a dimensionless value (e.g., a
percentage) such that a user, building owner, manager, etc., may
understand a current usage of a consumable product in terms of a
goal (e.g., an ideal usage of the consumable product) during a
period of no occupancy.
[0154] In these embodiments, the controllable load score may
compare the sum of the theoretical unoccupied load and the
sub-metered load to the actual unoccupied load. For example, during
a period of no occupancy a building manager would ideally want to
turn off all systems which consume a consumable product that are
not either (1) required to remain on (according to, e.g., a
building code) and/or (2) sub-metered and allocated back to a
responsible user. Accordingly, if the owner, manager, etc. is
successful in eliminating all consumption of a consumable product
that is not required to remain on (i.e., the theoretical unoccupied
load) and/or allocated back to a user (i.e., a sub-metered load), a
desirable controllable load score may be reached. In one
embodiment, a controllable load score of 1 (e.g., 100%) may be
reached. The controllable load score may be variously calculated.
In one example, the controllable load score may be calculated as
the sum of the theoretical unoccupied load and the sub-metered load
divided by the actual unoccupied load. In many practical
applications, the controllable load score will not be perfect.
Accordingly, the building's controllable load score would be less
than 1, or, rather, less than 100%. By expressing the controllable
load in terms of a dimensionless controllable load score, a user,
building owner, manager, etc., may better understand the building's
energy performance. Thus, a building receiving a controllable load
score of, e.g., 98% may be easily understood to be a rather
efficient building with respect to controllable load, while a
building receiving a controllable load score of, e.g., 48% may be
easily understood to be a rather inefficient building. These
dimensionless load scores are much easier for a building manager to
understand and manage. They are easy to compare between buildings
and projects.
[0155] Referring to FIG. 16, an exemplary process 1600 is
illustrated for determining a controllable load and/or a
controllable load score according to one aspect of the disclosure.
In step 1601, a theoretical unoccupied load of the building is
determined. At step 1602, a measured unoccupied load is retrieved,
e.g., from a contemporaneous and/or previous measurement. This
unoccupied load may be, e.g., an output reading from meter 104
during the predetermined time period and/or an unoccupied load
calculated using other techniques such as those discussed herein.
At step 1603, in this example suitable available sub-metered loads
are retrieved. The retrieved sub-metered loads in this example may
characterize one or more users' access to the consumable product
(which may ultimately be allocated back to the respective user)
during the predetermined time period as measured by one or more
sub-meters 105. In step 1604, a controllable load score may be
determined in this example using the determined theoretical
unoccupied load and the retrieved measured unoccupied load and
measured sub-metered load. In some embodiments, the controllable
load score may be determined using the formula presented in step
1604 (i.e., the sum of the theoretical unoccupied load and the
sub-metered load divided by the measured unoccupied load). Finally,
at step 1605, a controllable load may be determined. In some
embodiments, the controllable load may be determined using the
formula presented in step 1605 (i.e., the measured unoccupied load
less the sum of the theoretical unoccupied load and the sub-metered
load).
[0156] In still further embodiments, in addition to a controllable
load score, a building start time score may be determined to
quantify a building's energy performance. As presented above,
certain building systems (e.g., a building's HVAC system) consume
large quantities of a consumable product (e.g., electrical power).
When these systems are started well in advance of other comparative
buildings similarly situated, a building is operating
inefficiently. However, in order to quantify how well the building
system's actual start time is compared to, e.g., an optimal start
time, a building start time score may be determined. More
specifically, the building start time score quantifies how close to
a building occupancy time the building attains an occupancy
temperature. As with the controllable load score, the building
start time score may be a dimensionless value, and may be
presented, e.g., as a percentage. This enables building managers to
determine which buildings in their portfolio are efficient and
which buildings are inefficient. It also enables a building manager
to determine which buildings are candidates for further review and
analysis to fix issues associated with the building.
[0157] Returning to the example of an HVAC system using electrical
power, a slope of graph 200 (representing access to electricity)
may be variously analyzed. In some examples, the slope may change
(e.g., becomes negative) at an approximate time the building has
reached occupancy temperature. In this example, if the slope
becomes negative well before an occupancy time, there is a long
period of "unused" ramp time (i.e., the period of time between when
the system achieves an occupancy temperature and a desired
occupancy time). In such an embodiment, the system was started too
early because the building is being maintained at an occupancy
temperature even before the building is occupied. This unused ramp
time may be compared to a lookup table containing exemplar unused
ramp times and corresponding building start time scores in order to
determine a building start time score.
[0158] FIG. 17 illustrates one embodiment of a lookup table 1700
that may be utilized according to one aspect of the disclosure.
Specifically, lookup table 1700 includes a first column 1701
comprising exemplar unused ramp times, and a second column 1702
comprising exemplar building start time scores. Lookup table 1700
may be stored in any suitable location well known to those skilled
in the art, such as, e.g., at server/workstation 127. Lookup table
1700 may be used as described above to determine a building start
time score, with a score of 100 being optimal. As seen in lookup
table 1700, if a particular building has an unused ramp time of,
e.g., 90 minutes, the building may receive a building start time
score of 70. If the particular building instead has an unused ramp
time of 160 or more minutes, the building may receive a building
start time score of 0. Although lookup table 1700 contains exemplar
unused ramp times in increments of ten minutes, one skilled in the
art will appreciate that the exemplar unused ramp times in first
column 1701 and the exemplar building start time scores in second
column 1702 may be extrapolated to determine an appropriate
building start time score when a determined unused ramp time falls
between exemplar unused ramp times. For example, if a particular
building has an unused ramp time of 93 minutes, the building may
receive a building start time score of 67. Further, the table
contained in FIG. 17 is exemplary and other tables and
dimensionless characterizations may be utilized.
[0159] Further, one skilled in the art, given the benefit of this
disclosure, will understand that the values contained in lookup
table 1700 are for illustrative purposes only, and that, in
practice, these values may vary without departing from the scope of
this disclosure. For example, in some embodiments, an optimal
building start time score (i.e., 100) may only be achieved if the
building has no unused ramp time. In such an embodiment, rather
than 60 minutes of unused ramp time corresponding to a building
start time score of 100, 0 minutes would correspond to a building
start time score of 100. Further, although the relationship of
unused ramp time to building start time score in lookup table 1700
is linear (i.e., for every minute over 60 of unused ramp time, the
building start time score decreases by one), in other embodiments
the relationship between the unused ramp time and building start
time score may not be linear. For example, in some embodiments, the
relationship may be, e.g., quadratic, such that for each additional
minute of unused ramp time, the building start time score drops
exponentially.
[0160] In some embodiments, an appropriate building start time
score may be found mathematically without the use of lookup table
1700. For example, one skilled in the art will appreciate that the
information contained in lookup table 1700 may be expressed using a
system of equations. Specifically, in these embodiments, for a
determined unused ramp time (URT) an appropriate building start
time score (STS) may be found using the following system of
equations:
For URT.ltoreq.60.fwdarw.STS=100
For 60<URT<160.fwdarw.STS=160-URT
For URT.gtoreq.160.fwdarw.STS=0
[0161] Accordingly, a processor at, e.g., server/workstation 127
may mathematically determine an appropriate building start time
score by processing the unused ramp time using the above set of
equations rather than using the lookup table 1700. In any event,
given an unused ramp time as determined from one or more signatures
characterizing a building system's access to a consumable product,
an appropriate building start time score may be determined
quantifying one aspect of the building's energy performance.
[0162] In addition to the controllable load score and the building
start time score as presented above, in still further exemplary
embodiments the system may utilize a weighted energy score. A
weighted energy score may be variously configured, but in one
embodiment may be a single metric incorporating both the
controllable load score and the building start time score such that
the building's overall energy performance may be presented in an
easily understood metric. In some embodiments, the weighted energy
score may be an average of a building's controllable load score and
its building start time score. In such embodiments, the weighted
energy score would be equal to the sum of the building start time
score and the controllable load score divided by two. By way of
example, if, using any of the aforementioned methods, a particular
building is found to have a building start time score of 70 and a
controllable load score of 80, the building's weighted energy score
would be 75 (i.e., the sum of 70 and 80, 150, divided by two). This
weighted energy score may provide a building owner, manager, etc.,
with a useful metric to quantify her building's overall energy
performance taking into account both controllable loads and start
times of building systems (e.g., HVAC systems) and thus to form
useful comparisons between buildings.
[0163] In some embodiments, the weighted energy score may instead
comprise a weighted average of the above two metrics. That is, in
some embodiments a building owner, manager, etc., may put more
emphasis on their ability to control, e.g., a building system's
start time rather than a building's controllable load, and may
accordingly desire a metric which weighs the building start time
score more heavily than a controllable load score. By way of
example, the weighted energy score may weigh the building start
time score at, e.g., 75%, and accordingly the controllable load
score at 25%. In such an embodiment, the weighted energy score
would be the sum of 0.75 multiplied by the building start time
score and 0.25 multiplied by the controllable load score. Thus, if
a building receives a building start time score of 70 and a
controllable load score of 80, the weighted energy score in this
embodiment would be 72.5. Those skilled in the art, given the
benefit of this disclosure, will appreciate that any relative
weighting of each individual score (i.e., X % of building start
time score and Y % of controllable load score, where X+Y=100%) may
be employed without departing from the scope of this
disclosure.
[0164] FIG. 18 shows an exemplary a process 1800 for determining a
building start time score and/or a weighted energy score according
to one aspect of the disclosure. In this example, at step 1801, a
measured signature of a building system is received. Because one
system (e.g., an HVAC system) may comprise the majority of access
to a consumable product over a given period of time (e.g., a period
of time just before the building occupancy time), at step 1801 a
signature for the entire building's access to a consumable product
may be received and analyzed. In this example, such an
approximation is reasonable because other building systems may only
negligibly affect the overall signature curve. Alternatively, using
any of the techniques described herein such as pattern matching
algorithms and/or artificial intelligence/intelligent
agents/learning algorithms at, e.g., a processor in
server/workstation 127, a signature for the particular system of
interest (e.g., an HVAC system) may be isolated from other systems
by analyzing the overall consumption. In this example, at step
1802, the received signature is analyzed in order to determine an
approximate stop time of the building system. In some embodiments,
the stop time can be approximated as the time when a slope of the
signature changes (e.g., becomes negative). At step 1803, in this
example, an occupancy time is retrieved. Again, this occupancy time
may be variously determined in different embodiments such as user
defined and/or according to a commercial lease or the like. At step
1804 of this example, the retrieved occupancy time and determined
stop time of the building system may be used to determine an unused
ramp time of the building system. The unused ramp time may be
variously configured but in this example represents the amount of
time ahead of the occupancy time which the building was brought to
temperature (e.g., the interval of time between the determined stop
time of the building system and the occupancy time of the
building). Finally, in step 1805 a building start time score may be
determined as illustrated in this example. In some embodiments, the
building start time score may be determined by comparing the
determined unused ramp time to exemplary ramp times in a lookup
table, such as, e.g., the lookup table 1700 illustrated in FIG. 17.
In other embodiments, the building start time score may be
determined mathematically using, e.g., a system of equations as
presented above.
[0165] The metrics determined in step 1604 of process 1600 and step
1805 of process 1800 (e.g., the controllable load score and
building start time score, respectively) may optionally be combined
into a single metric in order to quantify the overall energy
performance of a building in step 1806. As presented herein in
various examples, this may be variously determined such as a simple
average of the determined building start time score and
controllable load score (i.e., the sum of the building start time
score and the controllable load score divided by two) and/or may be
a weighted average of the determined building start time score and
controllable load score (i.e., X % of building start time score and
Y % of controllable load score, where X+Y=100%).
[0166] FIG. 7 is a block diagram of an exemplary computing platform
700 of various embodiments, including an autonomous processor,
meters, sub-meters, communication devices, and other equipment for
performing the various described processes. The various embodiments
may be implemented as one computing platform or multiple computing
platforms, operating independently, or in a coordinated manner,
such as in a computer cluster. Using multiple computing platforms
may provide redundancy, increased analysis and/or data storage,
expanded capability to operate more users and/or geographically
disperse users and consumable products, and other advantages.
[0167] A processor 701 is configured to perform the various
operations of system control, telemetry sensing and gathering, data
reception and transmission, sensor calibration and control,
consumable product source and load control, telemetry processing.
Processor 701 may implement the various algorithms and processes as
described herein, including determining heat flow, thermal
resistance factors, and thermal mass factors, producing secondary
data products such as usage determinations and reports, determining
signatures of users and occupants, and determining specific user
access to consumable products/energy sources. The algorithms
implemented by processor 701 may include pattern matching
algorithms, signal processing algorithms, and artificial
intelligence algorithms such as neural networks. Processor 701 may
further control the operation of other components of computing
platform 700 or may control other remote equipment. Processor 701
may include one or more microprocessors, application specific
integrated circuits, field programmable gate arrays, programmable
interconnect and combinations thereof. Processor 701 may be
configured to communicate with and controls the various components
within 700 over one or more buses.
[0168] In at least some embodiments, processor 701 carries out
operations described herein according to machine readable
instructions (e.g. software, firmware, hardware configuration
files, etc.) stored in memory 702 and/or 703 and/or stored as
hardwired logic gates within processor 701. Memory 702 and 703 may
further store one or more databases which may be used to store
energy conversion factors, occupant heat signatures, consumption
signatures of various consumable product users, sensor telemetry,
calibration information, control information for various sensors,
actuators, and other system components, costing information of
various energy sources/consumable products, environmental
information, facility information, and other operating conditions.
The various databases may permit access by one or more processors
in 701 or one or more other processing platforms 700. The various
databases may be organized to include meta-data for the various
contents to enable selective retrieval of data to enable the
processing as described herein. As one example meta-data may be
added to consumption data such that it is retrievable from the
database in the correct time order, or such that signatures and
data specific to certain tenants or areas of the building are
provided from the database as a group of data that is easily
combinable.
[0169] Memory 702 and 703 may include volatile and non-volatile
memory and may include any of various types of storage technology,
including one or more of the following types of storage devices:
read only memory (ROM) modules, random access memory (RAM) modules,
magnetic tape, magnetic discs (e.g., a fixed hard disk drive or a
removable floppy disk), optical disk (e.g., a CD-ROM disc, a CD-RW
disc, a DVD disc), flash memory, and EEPROM memory.
[0170] Main processor 701 may be configured to communicate with
other computing systems, meters, sub-meters, etc. through various
interfaces such as wireless interfaces that may include additional
hardware and/or firmware. Such interfaces may include one or more
USB interfaces 708, Firewire interfaces 709, CAN protocol or other
standard sensor interfaces 710, other serial or parallel data
interfaces 711, and/or one or more wired and/or wireless network
interfaces 712, 713. For example, communication to remote hardware
may be accomplished through public and/or private networks using
network interfaces such as wireless interfaces 712, wired
interfaces 713, combinations of such interfaces and other
equipment. For example, wireless interface 712 may be a local WiFi
interface connected through a modem of a land line DSL, Coax, or
Fiber-optic service provider network which connects to the
Internet. Alternatively, wireless interface 712 may be equipment
for connecting to a satellite or a cellular network as commonly
used for cell phones, pagers, security systems, and personal
digital assistants (PDAs).
[0171] For human interaction with the system, computing platform
700 may include a display for presenting a graphical user
interface, graphs, charts, configuration information, or other data
relating to the embodiments described herein. Computing platform
700 may further include a console 705 for human interaction and
control of the various embodiments, and a printer 714 or other
output device for generating records such as invoices and usage
reports. Such consoles may include keyboards, mice or other input
output devices. The display, console, and printer may be co-located
with the other components of 700, or may be remote from 700. For
example, several of the components of 700 may operate as a server
that is remotely accessed over the Internet or private network and
which may provide web pages for presenting and interacting with the
system.
[0172] Computing platform 700 may further include other equipment
such as power supply 706, battery backups, fuses or other circuit
protection features, finger print readers and other security
devices, expansion slots for additional hardware, audio equipment,
infrared ports, etc.
[0173] The foregoing description is not intended to be exhaustive
or to limit embodiments of the present invention to the precise
form disclosed, and modifications and variations are possible in
light of the above teachings or may be acquired from practice of
various embodiments. The embodiments discussed herein were chosen
and described in order to explain the principles and the nature of
various embodiments and their practical application to enable one
skilled in the art to utilize the present invention in various
embodiments and with various modifications as are suited to the
particular use contemplated. The features of the embodiments
described herein may be combined in all possible combinations of
methods, apparatuses, modules, systems, and machine-readable
storage memory. Any and all permutations of features from the
above-described embodiments are within the scope of the invention.
For example, in performing processes 300, 400, 600, 1000, 1100,
1200, 1500, 1600, and 1800 the various computing platforms
performing the processes may perform the various steps in a
different order, may combine certain steps from the different
processes, or may omit certain steps.
[0174] Further, the various embodiments have been described in the
context of public utilities such as electricity and gas, and in the
context of human occupation. Such embodiments are exemplary only
and the principles described herein are equally applicable to other
energy sources where the distribution to multiple buildings may be
measured and analyzed. Other example include distribution of
compressed air, inert gases, steam, ice, dry ice, agricultural
irrigation, livestock, domestic animals, geothermal, nuclear,
biofuels, biomass, and any other energy source.
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