U.S. patent application number 14/470811 was filed with the patent office on 2014-12-18 for system and methods to aggregate instant and forecasted excess renewable energy.
The applicant listed for this patent is EXPANERGY, LLC. Invention is credited to Jeffrey Alan Dankworth, Paul W. Donahue, Michel Roger Kamel.
Application Number | 20140371936 14/470811 |
Document ID | / |
Family ID | 48466244 |
Filed Date | 2014-12-18 |
United States Patent
Application |
20140371936 |
Kind Code |
A1 |
Kamel; Michel Roger ; et
al. |
December 18, 2014 |
SYSTEM AND METHODS TO AGGREGATE INSTANT AND FORECASTED EXCESS
RENEWABLE ENERGY
Abstract
Systems and methods dynamically measure, ascertain, and compare
a local facility load with local renewable energy generation in
substantially real time and determine whether excess energy exists
from the local distributed renewable energy resource. Further,
systems and methods forecast the available excess energy from the
local distributed renewable energy resources for acquisition to
third parties. A pulse width modulation (PWM) controller permits
delivery of acquired increments of the available excess renewable
energy.
Inventors: |
Kamel; Michel Roger; (Buena
Park, CA) ; Donahue; Paul W.; (Newport Coast, CA)
; Dankworth; Jeffrey Alan; (Reno, NV) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
EXPANERGY, LLC |
Reno |
NV |
US |
|
|
Family ID: |
48466244 |
Appl. No.: |
14/470811 |
Filed: |
August 27, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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13685478 |
Nov 26, 2012 |
|
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|
14470811 |
|
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|
61564219 |
Nov 28, 2011 |
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Current U.S.
Class: |
700/291 |
Current CPC
Class: |
H02J 13/0006 20130101;
G06Q 50/06 20130101; G01R 21/1333 20130101; G05B 15/02 20130101;
Y04S 40/20 20130101; H02J 13/00014 20200101; G06Q 10/06312
20130101; H02J 2310/10 20200101; H02J 13/00004 20200101; Y04S 20/00
20130101; G01R 21/133 20130101; H02J 2203/20 20200101; Y02B 70/30
20130101; G05F 1/66 20130101; Y02P 80/10 20151101; Y02P 90/845
20151101; H02J 2310/16 20200101; Y04S 50/10 20130101; Y02P 90/84
20151101; Y04S 20/221 20130101; G06Q 30/06 20130101; Y04S 20/222
20130101; G01R 21/001 20130101; Y02B 70/3225 20130101; H02J
13/00034 20200101; Y02B 90/20 20130101; Y02P 90/82 20151101 |
Class at
Publication: |
700/291 |
International
Class: |
G06Q 50/06 20060101
G06Q050/06; G01R 21/133 20060101 G01R021/133 |
Claims
1. A method to allocate excess energy from distributed renewable
energy resources, the method comprising: automatically receiving in
a substantially continuous way a measurement of energy generated by
a distributed renewable energy resource; automatically receiving in
a substantially continuous way a measurement of energy consumed
from the energy generated by the distributed renewable energy
resource; based at least in part on the received measurement of
energy generated and the received measurement of energy consumed,
automatically forecasting a quantity of excess energy generated by
the distributed renewable resource; transmitting an indication
comprising the quantity of forecasted excess energy over a network
to one or more third parties; and allocating excess energy with a
pulse width modulation (PWM) controller in response to a request to
acquire an amount of the forecasted excess energy.
2. The method of claim 1 wherein allocating the excess energy
comprises allocating the amount of the excess energy.
3. The method of claim 2 wherein the amount of the excess energy
comprises a range between approximately 0% and approximately
100%.
4. The method of claim 3 wherein allocating the excess energy
further comprises inputting the amount of the excess energy to an
electrical grid.
5. The method of claim 4 wherein the network comprises the
Internet.
6. The method of claim 1 wherein receiving in a substantially
continuous way the measurement of energy consumed comprises
receiving the measurement of energy consumed at least once every 15
minutes.
7. The method of claim 1 wherein receiving in a substantially
continuous way the measurement of energy generated comprises
receiving the measurement of energy generated at a rate sufficient
to synchronize the energy generated by the distributed renewable
resource with energy on an electrical grid.
8. The method of claim 1 further comprising automatically
determining a quantity of instant excess energy generated by the
distributed renewable energy resource.
9. The method of claim 8 wherein the indication further comprises
the quantity of instant excess energy.
10. The method of claim 9 further comprising automatically
aggregating the quantities of instant excess energy generated by a
plurality of distributed renewable energy resources.
11. The method of claim 10 further comprising automatically
aggregating the quantities of forecasted excess energy generated by
a plurality of distributed renewable energy resources.
12. The method of claim 11 wherein the indication further comprises
the aggregated quantities of instant excess energy and the
aggregated quantities of forecasted excess energy.
13. The method of claim 1 wherein automatically forecasting the
quantity of excess energy comprises forecasting the quantity of
excess energy generated by the distributed renewable resource for a
predetermined period of time.
14. An apparatus to allocate excess energy from distributed
renewable energy resources, the method comprising: a first analog
to digital converter configured to automatically provide in a
substantially continuous way a measurement related to energy
generated by a distributed renewable energy resource; a first data
sampling device configured to receive the measurement related to
energy generated and to provide energy generated data; a second
analog to digital converter configured to automatically provide in
a substantially continuous way a measurement related to energy
consumed from the energy generated by the distributed renewable
energy resource; a second data sampling device configured to
receive the measurement related to energy consumed and to provide
energy consumed data; a data analyzer comprising computer hardware
configured to automatically forecast a quantity of excess energy
generated by the distributed renewable resource based at least in
part on the energy generated data and the energy consumed data; a
communication port configured to transmit an indication comprising
the quantity of forecasted excess energy over a network to one or
more third parties; and a pulse width modulation (PWM) controller
configured to allocate excess energy in response to a request to
acquire an amount of the forecasted excess energy.
15. The apparatus of claim 14 wherein allocating the excess energy
comprises allocating the amount of the excess energy.
16. The apparatus of claim 15 wherein the amount of the excess
energy comprises a range between approximately 0% and approximately
100%.
17. The apparatus of claim 16 wherein allocating the excess energy
further comprises inputting the amount of the excess energy to an
electrical grid.
18. The apparatus of claim 17 wherein the network comprises the
Internet.
19. The apparatus of claim 14 wherein receiving in a substantially
continuous way the measurement related to energy consumed comprises
receiving the measurement related to energy consumed at least once
every 15 minutes.
20. The apparatus of claim 14 wherein receiving in a substantially
continuous way the measurement related to energy generated
comprises receiving the measurement related to energy generated at
a rate sufficient to synchronize the energy generated by the
distributed renewable resource with energy on an electrical
grid.
21. The apparatus of claim 14 wherein the data analyzer is further
configured to determine a quantity of instant excess energy
generated by the distributed renewable energy resource.
22. The apparatus of claim 21 wherein the indication further
comprises the quantity of instant excess energy.
23. The apparatus of claim 22 wherein the data analyzer is further
configured to aggregate the quantities of instant excess energy
generated by a plurality of distributed renewable energy
resources.
24. The apparatus of claim 23 wherein the data analyzer is further
configured to aggregate the quantities of forecasted excess energy
generated by a plurality of distributed renewable energy
resources.
25. The apparatus of claim 24 wherein the indication further
comprises the aggregated quantities of instant excess energy and
the aggregated quantities of forecasted excess energy.
26. The apparatus of claim 14 wherein forecasting the quantity of
excess energy comprises forecasting the quantity of excess energy
generated by the distributed renewable resource for a predetermined
period of time.
Description
[0001] Any and all applications for which a foreign or domestic
priority claim is identified in the Application Data Sheet as filed
with the present application are hereby incorporated by reference
under 37 CFR 1.57.
BACKGROUND
[0002] This disclosure relates generally to evaluating energy
performance of a building, a building system, and/or a collection
of buildings locally or over a large geographic area.
[0003] Existing energy and greenhouse gas measurement and
verification protocols rely on walk around and observe audits that
are defined in, for example, International Standards Organization
(ISO) 50001, American Society of Heating, Refrigerating and
Air-Conditioning Engineers (ASHRAE) Level 1 audits, ASHRAE Level 2
audits, and the like. These rely on static analytics and do not
produce accurate results.
[0004] Another example of existing energy protocols is the U.S.
EPA's Energy Star.RTM. program. The Energy Star.RTM. program has
developed energy performance rating systems for several commercial
and institutional building types and manufacturing facilities.
These ratings, on a scale of 1 to 100, provide a means for
benchmarking the energy efficiency of specific buildings and
industrial plants against the energy performance of similar
facilities of the same space type, based on a national average. A
rating can be generated for ratable space types based on building
attributes, such as square footage, weekly operating hours, and
monthly energy consumption data. The Energy Star.RTM. ratings rely
on static analytics, estimates, and forecasting, and do not produce
accurate results and can be difficult to verify.
[0005] As a result of the lack of accurate and consistently
reliable measurement and verification standards, false claims of
carbon credits, Negawatts (energy saved as a result of energy
conservation or increased efficiency), and other energy reductions
are being made. Further, the lack of accurate and consistent energy
assessment makes it difficult to accurately determine the benefit
of corrective actions to equipment and systems, to normalize energy
conservation investments, to calculate paybacks from energy
conservation investments and retrofits of buildings, and the
like.
SUMMARY
[0006] There is a need to dynamically assess the energy
sustainability of a facility, i.e., how well it is using its
energy, and identify wasted energy that is consistent and
accurate.
[0007] Embodiments relate to an energy search engine using dynamic
analytic algorithms based at least in part on, but not limited to
one or more of smart meter data, other sensor data, sub-metered
energy measurement data, weather data, gas data, utility rate
schedules, basic facility information, such as, for example, the
direction (north, south, east or west) that the building faces,
total facility square footage, occupant scheduling, facility use,
and the like to dynamically assess the energy sustainability of a
facility.
[0008] In accordance with various embodiments, a method to assess
energy usage comprises receiving in a substantially continuous way
a measurement of actual energy consumption, receiving in a
substantially continuous way a measurement of ambient conditions,
and comparing the measurement of actual energy consumption with a
target energy consumption to calculate a substantially continuous
energy performance assessment, wherein the target energy
consumption is based at least in part on the measurement of ambient
conditions. The method further comprising receiving in a
substantially continuous way a measurement of facility occupancy
and usage, wherein the target energy consumption is based at least
in part on the measurement of ambient conditions and facility
usage. In one embodiment, receiving in the substantially continuous
way the measurement of ambient conditions comprises receiving the
measurement of ambient conditions at least every 15 minutes. In one
embodiment, the substantially continuous energy performance
assessment comprises comparisons occurring at least every 15
minutes of the measurement of actual energy consumption with the
target energy consumption.
[0009] Certain embodiments relate to a method to dynamically assess
energy efficiency. The method comprises obtaining a minimum energy
consumption of a system, receiving in a substantially continuous
way a measurement of actual energy consumption of the system, and
comparing the minimum energy consumption to the measurement of
actual energy consumption to calculate a substantially continuous
energy performance assessment. The system can be at least one of a
building envelope, a building, a zone within a building, an energy
subsystem, a facility, a group of buildings in near proximity to
each other, a geographically diverse group of buildings, and the
like.
[0010] In an embodiment, comparing the minimum energy consumption
to the measurement of actual energy consumption comprises at least
one of comparing in a substantially continuous way a theoretical
minimum energy consumption of the system to the measurement of
actual energy consumption to determine a theoretical energy
efficiency for the system, where the theoretical minimum energy
consumption is based at least in part on the theoretical
performance limit of system components, comparing in a
substantially continuous way an achievable minimum energy
consumption of the system to the measurement of actual energy
consumption to determine an achievable energy efficiency for the
system, where the achievable minimum energy consumption is based at
least in part on specifications for high energy efficient
equivalents of the system components, and comparing in a
substantially continuous way a designed minimum energy consumption
of the system to the measurement of actual energy consumption to
determine a designed energy efficiency for the system, where the
designed minimum energy consumption is based at least in part on
specifications for the system components.
[0011] Certain other embodiments relate to a method to dynamically
assess energy usage. The method comprises obtaining an expected
energy usage for a building having installed building systems and a
load profile, receiving in a substantially continuous way
measurements of actual energy consumption after an installation of
at least one energy improvement measure for the building,
establishing an energy usage for the building with the load profile
based at least in part on the measurements received after the
installation of the at least one energy improvement measure, and
determining an impact of the at least one energy improvement
measure. The method further comprises quantifying the effectiveness
of the at least one energy improvement measure by determining at
least one of a payback calculation, a payment of an incentive, a
valuation of real property, and a carbon offset used in carbon
trading. The installed building systems can comprise at least one
of an HVAC system, a lighting system, at least one plug load, a
data center system, a water heating system, and the like. Installed
energy improvement measure can comprise installing a renewable
energy system, retrofitting equipment, commissioning, load
shifting, load shedding, installing energy storage, and the
like.
[0012] According to a number of embodiments, an apparatus to
dynamically assess energy usage of a system comprises computer
hardware including at least one computer processor, and computer
readable-storage comprising computer-readable instructions that,
when executed by the computer processor, cause the computer
hardware to perform operations defined by the computer-executable
instructions comprising obtaining a minimum energy consumption of a
system, receiving in a substantially continuous way a measurement
of actual energy consumption of the system, and comparing the
minimum energy consumption to the measurement of actual energy
consumption to calculate a substantially continuous energy
performance assessment.
[0013] The computer-executable instructions further comprise at
least one of comparing in a substantially continuous way a
theoretical minimum energy consumption of the system to the
measurement of actual energy consumption to determine a theoretical
energy efficiency for the system, where the theoretical minimum
energy consumption is based at least in part on the theoretical
performance limit of system components, comparing in a
substantially continuous way an achievable minimum energy
consumption of the system to the measurement of actual energy
consumption to determine an achievable energy efficiency for the
system, where the achievable minimum energy consumption is based at
least in part on specifications for high energy efficient
equivalents of the system components, and comparing in a
substantially continuous way a designed minimum energy consumption
of the system to the measurement of actual energy consumption to
determine a designed energy efficiency for the system, where the
designed minimum energy consumption is based at least in part on
specifications for the system components.
[0014] The system can comprise at least one of a building, a
building envelope, at least one building system, a zone within the
building, a data center, and the like. Receiving in the
substantially continuous way the measurement of actual energy
consumption of the system can comprise receiving the measurement of
actual energy consumption at least every 15 minutes. The
substantially continuous energy performance assessment can comprise
comparisons occurring at least every 15 minutes of the minimum
energy consumption to the measurement of actual energy consumption
and at least one of a gas energy carbon footprint, an electrical
energy carbon footprint, an estimate of wasted energy, an energy
rating, an energy efficiency, and a power index.
[0015] For purposes of summarizing the disclosure, certain aspects,
advantages and novel features of the inventions have been described
herein. It is to be understood that not necessarily all such
advantages may be achieved in accordance with any particular
embodiment of the invention. Thus, the invention may be embodied or
carried out in a manner that achieves or optimizes one advantage or
group of advantages as taught herein without necessarily achieving
other advantages as may be taught or suggested herein.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] FIG. 1 illustrates a schematic diagram of a system to assess
and optimize energy usage for a facility, according to certain
embodiments.
[0017] FIG. 2 illustrates an exemplary schematic diagram of an
energy management system, according to certain embodiments.
[0018] FIG. 3 is a flow chart of an exemplary energy search engine
process to assess the amount of energy used for cooling and the
amount of excessive cooling affected, according to certain
embodiments.
[0019] FIG. 4 is an exemplary substantially continuous excessive
cooling performance assessment, according to certain
embodiments.
[0020] FIG. 5 is a flow chart of an exemplary energy search engine
process to assess energy usage, according to certain
embodiments.
[0021] FIG. 6 is a flow chart of an exemplary energy search engine
process to dynamically assess energy efficiency, according to
certain embodiments
[0022] FIG. 7 is a flow chart of an exemplary energy search engine
process to quantify the effectiveness of energy improving measures,
according to certain embodiments.
[0023] FIG. 8 is an exemplary substantially continuous energy
performance assessment, according to certain embodiments.
[0024] FIG. 9 is an exemplary continuous yearly key performance
indicator assessment, according to certain embodiments.
[0025] FIG. 10 is an exemplary continuous monthly key performance
indicator assessment, according to certain embodiments.
[0026] FIG. 11 illustrates an exemplary block diagram of a system
on a chip or circuit board for real time continuous and automated
digital measurement, analysis, and communication with optional
input from external sensors, analysis of energy use, analysis of
external data and control of energy use and the measurement,
analysis, storage, and reporting of energy quality and metrics,
according to certain embodiments.
[0027] FIG. 12 illustrates an exemplary block diagram of an energy
data reduction system that enables the use of existing
communication methods and channels to handle the anticipated
broadband flow of energy and control data that will emerge as
"smart grid" data measurement, communication, and control tools
expand in use and reach into residences, buildings, commercial
facilities, industrial facilities and into smart appliances and
devices, according to certain embodiments.
[0028] FIG. 13 illustrates an exemplary block diagram of the system
of FIG. 1, according to certain embodiments.
[0029] FIG. 14 represents a unique algorithm that can be used with
this invention or with any energy or environmental measuring and
reporting device or embodiment and/or with any network of energy or
environmental devices or systems that measure, report, and
optionally control energy or environmental data.
DETAILED DESCRIPTION
[0030] The features of the systems and methods will now be
described with reference to the drawings summarized above.
Throughout the drawings, reference numbers are re-used to indicate
correspondence between referenced elements. The drawings,
associated descriptions, and specific implementation are provided
to illustrate embodiments of the inventions and not to limit the
scope of the disclosure.
[0031] Embodiments of an energy search engine use dynamic energy
related data to determine how well a facility is using energy and
to identify wasted energy. Further embodiments dynamically guide
building system adjustments to reduce energy waste, and verify the
results of such actions. For example, contemporary heating,
ventilation, and air conditioning (HVAC) systems use a combination
of chilled coolant or chilled water, evaporative coils, forced air
ducting, and hot water intermixed to provide comfort and fresh air
to occupants of buildings. To provide this comfort, many building
HVAC systems waste energy by simultaneously heating and cooling
building air by relying on static factors and no external
information to dynamically adjust the various HVAC components.
[0032] In one embodiment, a combination of dynamically varying
factors are evaluated to dynamically adjust HVAC or other building
systems for optimum occupancy comfort, lowest energy use, lowest
tariff cost, and lowest GHG emissions. These factors include, but
are not limited to, natural and environmental factors, occupant
factors, utility tariff factors, and GHG emission factors. For
example, outside temperatures near a building vary hourly
throughout the workday and evening. Throughout the day, the sun
heats different aspects of the building which creates a variable
heating component. As occupants move in and out of rooms and in and
out of the building during the workday, their heat load
contribution, fresh air requirements, occupant comfort
requirements, and energy use via lighting, computers, and other
office equipment or industrial processes in the building vary.
[0033] The energy search engine incorporates at least one of these
dynamic variables in energy modeling algorithms to provide, for
example, one or more of benchmarking energy use, comparing required
energy use and costs to wasted energy use and costs, dynamically
guiding building system adjustments, verifying the results of such
actions, and determining an optimum size of alternate electric
energy systems, such as solar, wind, fuel cells, and the like to
generate energy for the building.
[0034] FIG. 1 illustrates an exemplary schematic diagram of a
system 100 to dynamically assess and optimize energy usage for an
energy subsystem of a facility, the facility or a network of
facilities 104. Facilities 104 can comprise one or more buildings,
residences, factories, stores, commercial facilities, industrial
facilities, data centers and the like, one or more rooms, one or
more offices, one or more zoned areas in a facility, one or more
floors in a building, parking structures, stadiums, theatres, and
the like, one or more systems, subsystems, and/or components 104a,
a zone within the building/facility 104, a building envelope, and
the like, locally or geographically remote.
[0035] The network of facilities 104 can comprise, for example, a
geographic area, facility owner, property manager, campus, weather
pattern, climate zone, facility activity type, facility total
square footage, facility occupied square footage, volume of
facility free space, facility schedule, facility activity levels
(e.g. production quantities, number of students, etc.), utility
company, applicable utility rate schedule, source of energy, type
and size of local generation systems, type and size of local
alternative energy systems (e.g. thermal solar, thermal storage,
energy storage, etc.), type of construction material used, type of
energy systems used, model of energy systems used, facility design
specifications, required air changes, measured air changes, type of
energy management system installed, model of energy management
system installed, performance of any existing energy management
system, applicable energy codes, applicable energy regulations,
applicable energy standards, applicable greenhouse gas emissions
codes, applicable greenhouse gas emissions regulations, applicable
greenhouse gas emissions standards, energy service company
servicing the facility, energy consulting firm servicing the
facility, and the like.
[0036] Examples of the systems, subsystems and/or components 104a
include but are not limited to fans, pumps, motors, chillers,
lights, heaters, heat exchangers, blowers, electric valves, air
conditioning equipment, compressors, heat pumps, HVAC systems,
lighting systems, motors, water heating systems, plug loads,
data/Telco, variable air volume devices (VAV), gas systems,
electrical systems, mechanical systems, electromechanical systems,
electronic systems, chemical systems, and the like.
[0037] The facility 104 and/or building 104 in the following
discussion refer to the facility, its systems, subsystems,
components, and/or a network of facilities as described above.
[0038] Energy entering the facility 104 can be of many forms, such
as, for example, thermal, mechanical, electrical, chemical, light,
and the like. The most common forms are typically electricity or
power, gas, thermal mass (hot or cold air, people), and solar
irradiance. The electrical energy can be generated from traditional
fossil fuels, or alternate forms of power generation, such as solar
cells, wind turbines, fuel cells, any type of electrical energy
generator, and the like. Ambient weather conditions, such as cloudy
days, or time of day, such as nighttime, may be responsible for
radiant energy transfer (gains or losses).
[0039] The facility 104 comprises measurement devices 104b
configured to measure actual energy usage in real time. For
example, sensors, such as wired and/or wireless sensors and/or
sensor systems, can measure kilowatt hours and energy spikes of
electrical energy used to power the lighting system, to power the
air compressor in the cooling system and to heat water for
lavatories, cubic feet of gas consumed by a heating or HVAC system,
amount of air flow from compressors in the cooling or HVAC system,
and the like. The sensors can comprise current sensors, voltage
sensors, EMF sensors, touch sensors, contact closures, capacitive
sensors, trip sensors, mechanical switches, torque sensors,
temperature sensors, air flow sensors, gas flow sensors, water flow
sensors, water sensors, accelerometers, vibration sensors, GPS,
wind sensors, sun sensors, pressure sensors, light sensors,
tension-meters, microphones, humidity sensors, occupancy sensors,
motion sensors, laser sensors, gas sensors (CO2, CO), speed sensors
(rotational, angular), pulse counters, and the like.
[0040] The facility 104 further comprises control systems, such as,
for example, load shedding relays, load shifting relays, Energy
Management Systems (EMS), Building Management Systems (BMS), and
the like, to control energy consuming and energy saving components
of the facility 104. For example, one or more controllers can raise
or lower automatic blinds, shut off/reduce heating or cooling in an
HVAC system in the entire or just one room of the facility 104,
switch usage of electricity from conventional generation to
electricity generated by alternate forms, such as wind or solar,
and the like.
[0041] The system 100 comprises an energy search engine 102 and a
user interface 108. In an embodiment, the energy search engine 102
is a cloud computing system based in a network 110, such as the
Internet 110, as illustrated in FIG. 1.
[0042] In other embodiments, the energy search engine 102 is not a
cloud computing system, but receives and transmits information
through the network 110, such as the Internet 110, a wireless local
network, or any other communication network. In an embodiment, the
energy search engine 102 is hosted in a device located inside the
facility 104. The device acquires sensor data and/or smart meter
data directly from existing sensors and smart meters 104b. The
device receives weather information, utility rate schedules,
utility pricing information, grid usage information, BIM
information, and other via RF broadcast signals. The device
calculates locally the energy performance, actionable information
and communicates control signals to local relays 104c, energy
systems and other systems.
[0043] The user interface 108 allows a user to transmit information
to the energy search engine 102 and receive information from the
energy search engine 102. In an embodiment, the user interface 108
comprises a Web browser and/or an application to communicate with
the energy search engine 102 within or through the Internet 110. In
an embodiment, the user interface 108 is associated with a display
and a user input device, such as a keyboard.
[0044] The energy search engine 102 receives energy usage
information from the measurement devices 104b measuring energy
usage of the systems, subsystems, and components 104c of the
facility 104 in a substantially continuous way. The measurement
devices 104b deliver data output that can include but is not
limited to electric energy consumption data, natural or renewable
gas data, air temperature data, air flow data, air quality data,
building occupancy data, building zone level occupancy data, water
data, environmental data, and geographic data, and the like. This
data can be derived from individual circuits, critical components
within the building 104 or its zones, or those systems that
externally serve a building or group/network of buildings. In
another embodiment, additional measurements of vibration,
temperature, sound from critical motor components, and the like
within buildings 104 or wherever motors are used for industrial or
manufacturing processes are used to gauge the health of motor and
equipment functions within the facility 104.
[0045] Further, the energy search engine 102 receives in a
substantially continuous way dynamic data relating to energy usage
from one or more of a Building Information Modeling (BIM) 106, a
power grid 112, a utility company 114, building management 116, and
an environmental service 118. For example, the BIM 106 can provide,
but is not limited to specifications for the systems, subsystem,
and components 104a installed in the facility 104, specifications
for the systems, subsystem, and components with a higher energy
rating that could have been installed in the facility 104, and the
like. The power grid 112 can provide, but is not limited to a
dynamic grid response to renewable energy sources, plug-ins,
projected grid demand, grid load information, energy supply
capacity, and the like. The utility company or other sellers of
energy 114 can provide, but are not limited to utility rate
tariffs, real-time energy pricing, price bids, and the like. The
building management 116 can provide, but is not limited to facility
and zone level scheduling of the facility 104, occupancy
information, system status information (e.g. open doors, open
windows, open shutters, etc.), and the like. The environmental
service, such as a weather service, can provide, but is not limited
to dynamic weather data for the location of the facility 104,
projected weather for the location of the facility 104, sever
weather alerts, geographical factors, and the like.
[0046] The energy search engine 102 analyzes the static data and
the dynamic data received in the substantially continuous way and
provides a substantially continuous energy assessment. Examples of
the substantially continuous energy assessment include but are not
limited to reports, benchmark results, energy performance
assessments for the facility 104, network of facilities 104 or any
of its systems, subsystems, and components 104a, site energy carbon
footprint, source energy carbon footprint, source energy
assessment, building and systems commissioning strategies, lighting
strategies, data center and Telco strategies, water performance
assessment, gas performance assessment, energy retrofit assessment,
renewable energy assessment, and the like.
[0047] In an embodiment, the energy search engine 102 transmits
commands to the control systems 104c to control the systems,
subsystems, and components 104a to reduce or optimize the energy
usage of the facility 104. In an embodiment, the energy search
engine 102 controls the systems, subsystems, and components 104a in
a substantially continuous way.
[0048] In an embodiment, substantially continuous comprises within
a length of time or not to exceed a length of time which occurs at
regular intervals. In another embodiment, data received in a
substantially continuous way comprises data that is received within
a definite length of time marked off by two instances. In other
words, data received in a substantially continuous way is data that
is received at regular time intervals, where the time interval does
not exceed a pre-defined time interval. In another embodiment, the
time interval is approximately within a pre-defined time interval.
In another embodiment, the time interval is based at least in part
on the type of information received. For example, weather can be
received substantially continuously every hour, smart meter
information can be received substantially continuously every 15
minutes, and grid load can be received substantially continuously
every time interval which does not exceed an hour.
[0049] Further, in an embodiment, providing substantially
continuous energy assessment comprises providing the energy
assessment within a pre-defined time interval, not to exceed a
pre-defined time interval, or the like. Further yet, controlling in
a substantially continuous way to optimize energy usage comprises
sending commands to the control systems 104c or the like within a
pre-defined time interval. Again, the time interval is based at
least in part on the specific system being controlled. For example,
the energy search engine may direct the facility 104 to shed or
redistribute power at an interval not to exceed 5 minutes, while
directing the blinds to raise or lower at an interval not to exceed
2 hours.
[0050] In certain embodiments, substantially continuous time
intervals comprise one of time intervals not to exceed 1 minute,
time intervals not to exceed 5 minutes, time intervals not to
exceed 15 minutes, time intervals no to exceed 1 hour, time
intervals not to exceed 1 day, and time intervals not to exceed 1
week.
[0051] FIG. 2 illustrates an exemplary block diagram of an
embodiment of the energy search engine 102. The energy search
engine 102 comprises one or more computers or processors 202 and
memory 204. The memory 204 comprises modules 206 including
computer-executable instructions, that when executed by the
computer 202 cause the energy search engine 102 to analyze the
energy data and provide the substantially continuous energy
assessment metrics. The memory 204 further comprises data storage
208 including one or more databases to store the dynamic and or
static data by the modules 206 to analyze energy usage and provide
energy usage assessments.
[0052] The computers 202 comprise, by way of example, processors,
Field Programmable Gate Arrays (FPGAs), System on a Chip (SOC),
program logic, or other substrate configurations representing data
and instructions, which operate as described herein. In other
embodiments, the processors 202 can comprise controller circuitry,
processor circuitry, processors, general-purpose single-chip or
multi-chip microprocessors, digital signal processors, embedded
microprocessors, microcontrollers and the like. The memory 204 can
comprise one or more logical and/or physical data storage systems
for storing data and applications used by the processor 202. The
memory 204 can further comprise an interface module, such as a
Graphic User Interface (GUI), or the like, to interface with the
user interface 108.
[0053] In one embodiment, the energy search engine 102 calculates a
score reflecting the energy performance of the facility 104. In an
embodiment, the score is a weighted average of one or more metrics
that are calculated based at least in part on one or more energy
variables. Examples of energy variables include, but are not
limited to the time history of the energy (power, water, gas)
consumed by the facility, the carbon equivalent of energy used at
the site, the carbon equivalent of the energy generated at the
source, the time history of the ambient weather conditions, the
facility activity type, the facility total square footage, the
facility occupied square footage, the volume of free space in the
facility, the facility schedule, the facility activity levels (e.g.
production quantities, number of students, and the like), the
location of the facility, the applicable utility rate schedule, the
output of any existing local (or on site) generation systems, the
output of any existing local alternative energy systems (e.g.
thermal solar, thermal storage, energy storage, and the like), the
potential for local renewable generation, the potential for local
alternative energy systems, the type of construction material used,
the type of energy systems used, the facility design
specifications, required air change, measured air changes, the type
of energy management system installed, the performance of any
existing energy management system, data from any existing energy,
environmental and security monitoring systems, and the like.
[0054] The facility 104 and/or building 104 and/or subsystems 104a
refer to one or more of the facility, its systems, subsystems, and
components, multiple buildings comprising the facility located
locally or remotely, and a network of facilities in the following
discussion.
[0055] In an embodiment, the score or energy metrics are calculated
based on historical energy data for the past week, month, quarter,
year or longer time period.
[0056] In an embodiment, data from one time period is used to
backfill data missing from another time period. For example, if the
data for February of 2012 is missing, then it can be backfilled
using the following:
Y.sub.2=X.sub.2/X.sub.1*Y.sub.1
where X.sub.1 is the average workday energy consumption for January
2011, X.sub.2 is the average workday energy consumption for January
2012, and Y.sub.1 is the 15-minute, hourly, daily, or weekly energy
consumption for workdays in February 2011, and Y.sub.2 is the
15-minute, hourly, daily, or weekly energy consumption for the
corresponding workdays in February 2012. The above method can be
used to backfill missing energy data for off days.
[0057] In another example, the same missing data for February 2012
can be backfilled using the following:
Y.sub.2=2*(X.sub.1Z.sub.2+Z.sub.1X.sub.2)/(X.sub.1Z.sub.1)*Y.sub.1
where X.sub.1 is the average workday energy consumption for January
2011, X.sub.2 is the average workday energy consumption for January
2012, Z.sub.1 is the average workday energy consumption for March
2011, Z.sub.2 is the average workday energy consumption for March
2012, and Y.sub.1 is the 15-minute, hourly, daily, or weekly energy
consumption for workdays in February 2011, and Y.sub.2 is the
15-minute, hourly, daily, or weekly energy consumption for the
corresponding workdays in February 2012. The above method can be
used to backfill missing energy data for off days.
[0058] In an embodiment, the energy used by the facility is
calculated using the following equation:
Energy Used=Energy Sourced by the Utility+Energy Generated on
Site-Energy Stored on Site
where energy sourced by the utility is energy that is purchased
from the utility company. The energy generated on site (locally) is
energy generated by local energy generation systems as solar PV,
wind turbines, fuel cells, gas power plant, etc. The energy stored
on site is energy that is purchased from the utility or generated
locally but is stored at the time of purchase or generation for
later use in energy storage systems such as batteries, compressed
air, pumped water, thermal storage, etc. If the energy storage
systems are discharging, then the sign of the stored energy in the
equation above is negative. Each of the components in the equation
above can be measured, calculated or estimated.
[0059] In an embodiment, the energy score and metrics can be
proportional to the energy performance of a facility relative in a
specific time period compared to its performance in a base period.
In an embodiment, the base period is one year.
[0060] In an embodiment, the metric can be proportional to the
composition of source energy (solar PV, utility power, fuel cell,
solar thermal, gas generator, energy storage, etc.) relative to an
optimum composition of source energy for a facility, given the
measured, calculated or estimated energy usage of the facility, the
type of systems in the facility, the facility schedule, the
facility location, the ambient weather conditions, and the
like.
[0061] In an embodiment, the metrics include but are not limited to
the facility electric energy use index (kwHr/ft.sup.2), the
facility gas use index (therms/ft.sup.2), and the facility electric
demand index (kw/ft.sup.2).
[0062] In an embodiment, the metric can be proportional to the
equivalent greenhouse gas emissions of the energy used at the
facility 104, proportional to the energy generated using local
renewable energy systems, proportional to the energy generated
using alternative fuel systems (e.g. hydrogen fuel cells, or the
like), proportional to the use of alternative energy systems,
proportional to the ratio of energy used during off hours to the
energy used during work hours, proportional to the ratio of energy
used during work days to the energy used during off days,
proportional to the minimum rate of energy consumption during a
period of time (day, month, year, etc.), proportional to the
simultaneous heating and cooling that may be occurring in the
facility 104, or the like.
[0063] In another embodiment, the metric can be proportional to the
correlation between energy used for heating energy and heating
requirements, proportional to the correlation between the energy
used for cooling and the cooling requirements, proportional to the
estimated, calculated, or measured energy used for heating divided
by the amount of heating affected, proportional to the estimated,
calculated or measured energy used for cooling divided by the
amount of cooling affected, and the like. Heating requirements,
cooling requirements, amount of heating affected, or the amount of
cooling affected can be calculated using, but not limited to one or
more of the following: ambient weather, ambient environmental
conditions, desired internal temperature, ventilation rates,
outside air circulation, recirculation rates, recirculated air,
energy consumed by loads inside the facility 104, heat generated by
other sources inside the facility 104, heat entering or leaving the
facility 104 through mass or thermal transfer, and the like. In one
embodiment, the heating degree hours, a difference between ambient
temperature and supply air temperature inside the facility for each
hour, can be used as a measure of affected heating (heating
kWhr/degree heated). In another embodiment, the cooling degree
hours, a difference between the ambient temperature and the supply
air temperature inside the facility 104 for each hour, can be used
as a measure of affected cooling (cooling kWhr/degree cooled).
[0064] In another embodiment, the required heating enthalpy hours,
a difference between ambient enthalpy and a target temperature and
humidity inside the facility, can be used as a measure of required
heating. In another embodiment, the required cooling enthalpy
hours, a difference between ambient enthalpy and a target
temperature and humidity inside the facility, can be used as a
measure of required cooling.
[0065] In another embodiment, the affected heating enthalpy hours,
a difference between ambient enthalpy and supply air enthalpy
inside the facility for each hour, can be used as a measure of
affected heating (heating energy kWhr/kJ heated). In another
embodiment, the cooling enthalpy hours, a difference between the
ambient enthalpy and the supply air temperature inside the facility
104 for each hour, can be used as a measure of affected cooling
(cooling kWhr/KJ cooled).
[0066] FIG. 3 is a flow chart of an exemplary energy search engine
process 300 to assess the amount of energy used for cooling and the
amount of excessive cooling affected. For example, the amount of
energy used for cooling during workdays during a year can be
estimated from the profile of total energy used during the year.
The interval energy (energy consumed at regular intervals,
typically 15 or 30 minutes, or any other regular interval) for a 12
month period is used. At blocks 310 and 312, the average energy and
average cooling degree requirement, respectively, for each time
interval during work days is calculated for each month.
[0067] At block 314, the minimum energy profile for workdays in
calculated and at block 316, the average energy used for cooling is
calculated as the difference between the energy profile and the
minimum energy used during workdays.
[0068] At block 318 to block 324, the minimum energy required to
cool the facility by one degree is estimated for each time interval
during workdays. At block 326, the minimum energy needed for
cooling during the year is calculated from the results of blocks
322 and 324.
[0069] At block 328, the amount of energy used for excessive
energy, i.e. the amount of energy wasted due to excessive cooling
is estimated. At block 330, the energy profile for the year is
calculated assuming the cooling and heating systems are at their
peak efficiency all year long. The process can be repeated for time
intervals during off-days.
[0070] FIG. 4 is an exemplary substantially continuous excessive
cooling performance assessment 400. The performance assessment 400
is a topographical map of the facility 104 showing estimated
excessive cooling energy for an average hour throughout the day in
each month for a year. The excessive cooling is estimated by
considering the load inside the building and the cooling
requirement from the ambient weather, as described in FIG. 3. For
example, the performance assessment 400 indicates total cooling
costs being estimated at $117,000, with $75,000 in overcooling
between 10 AM and 3 AM, and $19,000 in overcooling between 3 AM and
10 AM. Overcooling is estimated at $96,000, being up to 76% of the
cooling costs. In an embodiment of the performance assessment 400,
color can be used to indicate energy intensity, where brighter
shades of a color indicate greater energy intensity and darker
shades of the color represent lighter energy intensity (or vice
versa).
[0071] In an embodiment, the metric can be proportional to the time
history of the number of air changes, proportional to the fraction
of outside air introduced to the facility, proportional to the
fraction of return air recirculated, where return air is air that
is exhausted from the facility using exhaust fans, proportional to
the air quality inside the facility, proportional to facility peak
demand and the load duration curve, which represents the time spent
at each power level from the lowest demand to the peak demand,
proportional to the level of compliance of the facility 104 or any
of its subsystems 104a with one or more of existing and/or future
energy regulations, standards, codes, specifications and
guidelines, and the like.
[0072] In another embodiment, the metric can be proportional to the
level of energy demand reduction or load shedding initiated in
response to a request from the grid or utility. The energy demand
reduction can be calculated relative to a baseline that is adjusted
for one or more of the following factors: ambient weather
conditions, ambient environment conditions, changes in facility
schedule, changes in facility activity, changes in facility
occupancy, and the like. In an embodiment, the projected energy
demand reduction for the facility 104 can be calculated by
estimating the amount of energy that will be used for cooling as
described above and assuming that a certain percentage of the
cooling energy will be reduced.
[0073] In a yet further embodiment, the metric can be proportional
to the change in energy consumption of the facility 104 or any of
its energy subsystems 104a compared to an energy baseline, an
energy benchmark, a computed energy usage, an estimated energy
usage or a projected energy usage.
[0074] In an embodiment, an energy baseline can be calculated for
any measured or calculated metric, and correlated with ambient
weather conditions, facility usage, and facility schedule. The
calculated baseline can be used to project the value of the metric
given projections of ambient weather conditions, facility usage,
facility schedule, or changes in energy systems.
[0075] In an embodiment, the metric can be proportional to the cost
of total energy used at the facility 104, proportional to the cost
of gas energy used at the facility, proportional to the cost of
energy used at the facility 104 from renewable energy sources,
proportional to the cost of energy used at the facility 104 from
alternative energy sources, proportional to the total cost to
generate the energy at the source, proportional to the cost of
delivering the energy from the source to the facility 104,
proportional to the total cost of electric energy used at the
facility 104, proportional to the peak electric energy demand costs
at the facility 104, proportional to the electric energy
consumption costs at the facility 104, proportional to the avoided
energy consumption costs, proportional to the avoided peak demand
costs, or the like.
[0076] In another embodiment, the metric can be proportional to the
energy consumed in the facility 104, proportional to the total
energy that can be delivered to the facility 104, proportional to
the total energy that can be generated in the facility 104,
proportional to the total energy that can be reduced in the
facility 104, proportional to the reliability of the sources of
energy to the facility 104 and the total uptime of one or more of
the facility's energy sources, proportional to the power quality
(e.g. power factor, total harmonic distortion, energy in harmonic
frequencies, voltage spikes, voltage drops, power surges, etc.) of
the power in the facility 104 or any of its energy subsystems 104a,
proportional to number of megawatts (MW) or megawatt-hours (MWhr)
avoided as a response to an energy emergency, or the like. An
example of such avoided energy is the load shed as part of a
utility's Demand Response program.
[0077] In another embodiment, any of the metrics can be calculated
every year, month, week, day, hour, or in a substantially
continuous manner.
[0078] In a further embodiment, any of the metrics can be
calculated in the cloud-based server 102 and can be offered as a
subscription-based service.
[0079] FIGS. 5, 6, and 7 are flow charts of exemplary search engine
processes to actively process energy consumption data,
environmental data, and building use data, examples of which are
described above, in a plurality of time frames, including real
time, to provide one or more of the metrics related to the minimum
energy required by the facility 104 and its critical subsystems
104a for a unique geographic location, use, environment, and
occupancy associated with the facility 104, examples of which are
described above.
[0080] In other embodiments, the energy search engine algorithms
provide energy and sustainability ratings for commercial,
municipal, campus, state, and federal buildings. Another embodiment
provides carbon footprinting of buildings and facilities. Yet
another embodiment evaluates the value of real property by
evaluating its energy consumption and effectiveness and efficiency
of installed systems and components. A further embodiment evaluates
the instant demand response, load shedding, load shifting, and
additional local generating potential of buildings, facilities,
campuses and their systems. A yet further embodiment guides and
evaluates actionable energy efficiency and demand response
improvement measures, equipment retrofits, and commissioning
strategies. In an embodiment, the technology enables compliance
with legislated energy efficiency mandates and goals.
[0081] FIG. 5 is a flow chart of an exemplary energy search engine
process 500 to assess energy usage for the facility 104. The
facility 104 and/or building 104 and/or subsystem 104 refer to one
or more of the facility, its systems, subsystems, and components,
multiple buildings comprising the facility located locally or
remotely, and a network of facilities in the following discussion.
Beginning at block 510, the process 500 receives substantially
continuous measurements of actual energy consumption of the
facility 104. In an embodiment, the process 500 receives
measurements related to the actual energy consumption of the
facility 104 from the measurement devices 104b. Examples of the
measurements of actual energy consumption are smart meter readings,
electric meter readings, gas meter readings, current measurements,
facility energy variables as described above, and the like.
[0082] In another embodiment, receiving substantially continuous
measurements comprises receiving measurements at least every 15
minutes. In another embodiment, receiving substantially continuous
measurements comprises receiving measurements at least every 5
minutes. In a further embodiment, receiving substantially
continuous measurements comprises receiving measurements at least
every 1 hour.
[0083] At block 512, the process 500 receives substantially
continuous measurements of ambient conditions. For example, the
energy search engine 102 can receive weather reports including the
outside air temperature, outside air humidity, cloud coverage, UV
index, precipitation level, evaporative transpiration (ET) number,
weather forecast, and the like. In another example, the status of
doors, windows, and shutters associated with the facility 104 can
change with time and can be received in a substantially continuous
manner.
[0084] At block 514, the process 500 obtains a target energy
consumption of the facility 104 based at least in part on the
ambient conditions. Target energy consumption can be a calculated
energy consumption based on baseline performance, desired
environmental conditions inside the facility (temperature,
humidity, air quality, etc.) projected facility schedule, projected
facility usage, and projected weather conditions. Average facility
energy consumption at a given ambient condition and facility usage
level can be calculated based on historic data. This average can be
set as a target for the facility when similar weather and facility
usage are anticipated.
[0085] At block 516, the process 500 compares the measurement of
the actual energy consumption with the target energy consumption
for the facility 104, and at block 518, the process 500 calculates
a substantially continuous energy performance assessment based at
least in part on the comparison of the measurement of the actual
energy consumption with the target energy consumption.
[0086] Metrics found on the performance assessment can include but
are not limited to one or more of total gas and electric current
energy costs per square foot of the facility 104, baseline electric
energy rating, peak electrical energy rating, gas energy rating,
efficiency of heating gas use, simultaneous heating and cooling,
nighttime power index, weekend power index, EMS scheduling, full
time loading, an overall performance assessment, energy wasted
annually, range of estimated energy wasted annually, cost to
produce energy at the source, cost to deliver energy to the
facility 104, waste as a percent of total energy used, cost of
annual gas and electric energy wasted annually, electrical energy
carbon footprint, gas energy carbon footprint, total energy carbon
footprint, target energy usage in cost per square foot of the
facility 104, annual energy savings target, historical electricity
and gas usage, historical monthly peak demand for electricity,
historical energy map showing annual energy usage versus the time
of the day the usage occurred, wasted heating based on a comparison
of heating requirements and the actual energy used for heating,
simultaneous heating and cooling based on an estimated energy used
for cooling during business hours and an estimated energy used for
heating during business hours, wasted cooling during business hours
based on a comparison of relative cooling required during business
hours and an estimated energy used for cooling during business
hours, wasted cooling during non-business hours based on a
comparison of relative cooling required during non-business hours
and an estimated energy used for cooling during non-business hours,
cooling degree hours, heating degree hours, peak reduction, energy
savings recommendations, energy source planning, energy source
investment payback including but not limited to fuel call, grid
tied solar, thermal storage, battery-based peak shedding, and
utility based on electric utility data, gas utility data, National
Oceanic and Atmospheric Administration (NOAA) weather data for the
facility 104, and the like.
[0087] In another embodiment, the metric can be proportional to the
level of compliance with one or more energy standards, such as, for
example, ISO 50001, LEED Silver, LEED Gold, LEED Platinum, and the
like.
[0088] In another embodiment, the metric can be proportional to the
cost of bringing the facility to compliance with one or more energy
standard.
[0089] In another embodiment, the metric can be proportional to the
energy savings (in consumption kWhr, demand kW or energy costs $)
that can be realized by bringing the facility to compliance with
one or more energy standard.
[0090] In another embodiment, the metric can be proportional to the
absolute efficiency of the energy subsystem or facility 104. The
absolute efficiency of the energy subsystem 104 can be the ratio
between the measured, calculated or estimated energy consumed by
the subsystem 104 and the energy the subsystem 104 would have
consumed if it operated at the theoretical limits of the subsystem
104.
[0091] In another embodiment, the metric can be proportional to the
achievable efficiency of the energy subsystem or facility 104. The
achievable efficiency of the energy subsystem 104 can be the ratio
between the measured, calculated or estimated energy consumed by
the subsystem 104 and the energy the subsystem 104 would have
consumed if it operated at the highest efficiency achievable by
such subsystems 104.
[0092] In an embodiment, the metric can be proportional to the
design efficiency of the energy subsystem or facility 104. The
design efficiency of the energy subsystem or facility 104 can be
the ratio between the measured, calculated or estimated energy
consumed by the subsystem 104 and the energy the subsystem 104
would have consumed if it operated per the manufacturer's design
specification.
[0093] In an embodiment, the metric can be proportional to the
savings realized (consumption kWHr, demand KW, energy costs in $)
if one or more energy subsystems is operating at its theoretical,
absolute, or design efficiency.
[0094] FIG. 6 is a flow chart of an exemplary energy search engine
process 400 to dynamically assess energy efficiency for the
facility 104. Energy search engine algorithms analyze measured
energy data versus computed minimum required energy to provide, for
example, energy efficiency ratings, energy consumption profiles,
energy load factors, critical component assessment, and life cycle
analysis of critical components.
[0095] Beginning at block 610, the process 600 obtains a minimum
energy consumption for the facility 104.
[0096] Obtaining the minimum energy consumption at block 410
comprises obtaining the theoretical energy consumption based at
least in part on models of the installed building systems,
subsystems, and components 104a at block 612. In an embodiment,
obtaining the minimum energy consumption comprises obtaining a
theoretical energy consumption based at least in part on ideal
models or the theoretical limits of the installed building systems,
subsystems, and components 104a.
[0097] Obtaining the minimum energy consumption at block 610
further comprises obtaining an achievable minimum energy
consumption based at least in part on specifications for high
energy efficiency equivalents of the installed building systems,
subsystems, and components 104a at block 614 and obtaining a
designed minimum energy consumption based at least in part on
specifications and cumulative loads for the installed building
systems, subsystems, and components 104a at block 616.
[0098] At block 620, the process 600 receives substantially
continuous measurements of the facility energy consumption. In an
embodiment, the measurements are provided by the measurement
devices 104b. In another embodiment, measurements are calculated.
In a further embodiment, the measurements are estimated.
[0099] At block 630, the process 600 compares the minimum energy
consumption to the measurement of the facility energy consumption
in a substantially continuous way. Comparing at block 630 comprises
comparing the theoretical minimum energy consumption to the
measurement of the facility energy consumption at block 632,
comparing the achievable minimum energy consumption to the
measurement of the facility energy consumption at block 634, and
comparing the designed minimum energy consumption to the
measurement of the facility energy consumption at block 636.
[0100] At block 640, the process 600 calculates a substantially
continuous energy performance assessment for the facility 104.
Calculating the substantially continuous energy performance at
block 640 comprises determining a theoretical energy efficiency at
block 642, determining an achievable energy efficiency at block
644, and determining a designed energy efficiency at block 646.
[0101] For example, the absolute or theoretical efficiency metric
for a fan can be calculated from a measurement, estimation or
calculation of one or all of the following: fan upstream pressure,
fan downstream pressure, flow temperature, fan speed, mass flow
rate through the fan, volumetric flow rate through the fan, and/or
energy consumed by the fan. The fan's absolute efficiency is then
calculated by dividing the energy consumed by the energy that
should have been consumed by the fan if it operated at its
theoretical efficiency under the same conditions of upstream
pressure, downstream pressure, fan speed, flow temperature,
volumetric flow rate, or mass flow rate through the fan.
[0102] In another example, the achievable efficiency metric for a
fan can be calculated from a measurement, estimation or calculation
of one or all of the following: fan upstream pressure, fan
downstream pressure, flow temperature, fan speed, mass flow rate
through the fan, volumetric flow rate through the fan, and/or
energy consumed by the fan. The fan's achievable efficiency is then
calculated by dividing the energy consumed by the energy that
should have been consumed by the highest performing fan available
operating under the same conditions of upstream pressure,
downstream pressure, fan speed, flow temperature, volumetric flow
rate, or mass flow rate through the fan.
[0103] In a further example, the design efficiency metric for a fan
can be calculated from a measurement, estimation or calculation of
one or all of the following: fan upstream pressure, fan downstream
pressure, flow temperature, fan speed, mass flow rate through the
fan, volumetric flow rate through the fan, and/or energy consumed
by the fan. The fan's design efficiency is then calculated by
dividing the energy consumed by the energy that should have been
consumed by the fan operating per the manufacturer's design
specifications under the same conditions of upstream pressure,
downstream pressure, fan speed, flow temperature, volumetric flow
rate, or mass flow rate through the fan.
[0104] In another example, the theoretical efficiency of a facility
envelope on hot days can be calculated assuming a perfectly
insulated facility envelope that blocks all radiant heat transfer
into the facility 104, all convective heat transfer into the
facility 104, and all infiltration of mass in and out of the
facility 104. The heat generated in the facility 104 is calculated
from a measurement, estimate, or calculation of the energy consumed
in the facility 104, such as indoor lighting, plug load, heating
gas, etc., and the heat generated by occupants and processes in the
facility 104. The heat removed from the facility 104 can be
calculated from a measurement, estimate, or calculation of the
difference between enthalpy of the ventilation and cooling air
leaving the envelope and the enthalpy of the ventilation and
cooling air entering the envelope. The facility 104 can be assumed
to be at constant operating temperature. For a perfectly insulated
envelope at a constant internal temperature, the heat removed from
the facility 104 is equal to the heat generated in the facility
104. The envelope efficiency on a hot day can be calculated as the
ratio of the heat generated in the facility 104 divided by the heat
removed from the facility 104.
[0105] In a further example, the design efficiency of a roof top
packaged HVAC unit can be calculated by measuring the enthalpy
(temperature, humidity and flow rate) of the air entering the HVAC
system (fraction of return air+fraction of outside air), the
enthalpy of the air leaving the HVAC system (supplied to the
facility 104) and the energy consumed by the HVAC system. The
energy consumed by the HVAC is divided by the [enthalpy of air
entering the HVAC--enthalpy of air leaving the HVAC] to yield a
measured coefficient of performance (COP). The measured COP is then
divided by the design COP specified by the HVAC manufacturer at the
given HVAC load and the resulting ratio comprises the design
efficiency of the HVAC system.
[0106] FIG. 7 is a flow chart of an exemplary energy search engine
process 500 for demand side analysis. In one embodiment, the energy
search engine 102 matches varying demand side load requirements to
supply side generating and grid capability to establish relative
value energy pricing.
[0107] The exemplary process 700 quantifies the effectiveness of
energy improving measures, such as rating the energy efficiency and
carbon footprint of the facility 104 and/or any or all of its
systems 104a (individually or collectively) to determine
opportunities to introduce energy efficiency, install retrofits,
commission facilities for monitoring based commissioning (MBCx),
retrocommissioning (RCx), continuous commissioning, and the like,
and provide peak reduction and demand response strategies and
actions.
[0108] Beginning at block 710, the process 700 obtains an energy
usage benchmark for the facility 104. In an embodiment, the energy
usage benchmark is an energy usage point of reference across a
network of facilities that share something in common against which
the energy usage of the facility 104 may be compared. For example,
the benchmark could be one of an Energy Star.RTM. rating,
historical energy use of the facility 104, energy performance of
buildings in a specific geographic area, energy performance of
buildings of a certain size, energy performance of buildings of a
certain activity, energy performance of buildings with a certain
type of cooling technology, energy performance of buildings with a
certain type of heating technology, energy performance of buildings
with a certain type of construction material, energy performance of
buildings with a certain brand and model of EMS, energy performance
of buildings with a certain energy certification, energy
performance of buildings with a certain energy rating, energy
performance of buildings with a certain local energy source (e.g.
solar PV), or the like.
[0109] At block 712, the process 700 receives substantially
continuous measurements of actual energy consumption for the
facility 104 before installation of energy improvement measures. In
an embodiment, the measurements are obtained from the measurement
devices 104b.
[0110] At block 714, the process 700 establishes a first baseline
of energy usage for the facility 104 based at least in part on the
measurements of actual energy consumption received before the
installation of the energy improvement measures. In an embodiment,
establishing the baseline before installation comprises obtaining
an expected energy usage for the facility which has installed
systems, subsystems, and components 104c, and a load profile. The
baseline is established by recording the energy performance of the
facility over a period of time, e.g. 3 months or 1 year. Multiple
baselines can be calculated, such as for night hours on work days,
morning hours on work days, afternoon hours on work days, night
hours on off days, morning hours on off days, afternoon hours on
off days.
[0111] Different types of baselines can be calculated, including,
but not limited to total energy consumption (kWhr), electrical
energy consumption (kWhr), electrical demand (kW), gas consumption
(therms), energy used for lighting (kWhr), demand due to lighting
(kW), energy used for cooling (kWhr), demand due to cooling (kW),
and the like. Baselines can also be normalized with ambient weather
conditions, cooling degree hours, heating degree hours, facility
usage levels, facility occupancy, etc. For example, the baseline of
energy consumed (kWhr) during morning hours on work days, could be
the average consumption during morning hours for all workdays of a
certain segment or entire segment of the baseline calculation
period. The consumption baseline can be normalized to cooling
degree hours by dividing the average consumption for each of the
morning hours on work days with the average cooling degree hour for
each of those hours.
[0112] At block 716, the process 700 compares the benchmark with
the baseline established before the installation of the at least
one energy improvement measure.
[0113] At block 718, the process 700 receives substantially
continuous measurements of actual energy consumption for the
facility 104 after installation of at least one energy improvement
measure. In an embodiment, the measurements are obtained from the
measurement devices 104b.
[0114] Examples of energy improvement measures are, but not limited
to, installing a renewable energy system, retrofitting equipment,
commissioning, load shifting, load shedding, installing energy
storage, installing LED lighting systems, installing variable
frequency drive (VFD) systems, installing new windows, installing
new wall insulation, replacing inefficient boilers, upgrading the
insulation of hot and cold water pipes, installing economizer
systems, installing evaporative cooling systems, adding circulation
fans, changing the location of supply and return ventilation air
ducts, installing pool covers, installing thermal storage systems,
installing shades and awnings, and the like.
[0115] At block 720, the process 700 establishes a second baseline
based at least in part on the measurements of actual energy
consumption received after the installation of the energy
improvement measures. In an embodiment, establishing the baseline
after installation comprises obtaining an energy usage for the
facility which has installed systems, subsystems, and components
104c, and a load profile.
[0116] At block 722, the process 700 compares the first baseline
established before installation of the at least one energy
improvement measure with the baseline established after
installation of the at least one energy improvement measure.
[0117] At block 724, the process 500 compares the benchmark with
the baseline established after installation of the at least one
energy improvement measure.
[0118] At block 726, the process 700 quantifies the effectiveness
of the at least one energy improvement measure. In an embodiment,
the process 700 determines the impact of the at least one energy
improvement measure against the first baseline of energy usage for
the facility 104. In another embodiment, the process 700 determines
the impact of the at least one energy improvement measure against
the benchmark for the facility 104. In an embodiment, quantifying
the effectiveness of the at least one energy improvement comprises
determining one or more of a payback calculation, a payment of an
incentive, a valuation of real property for purposes that include
projecting the value of operational strategies and behaviors or
equipment replacements that result in altered real property
valuations, a carbon offset used in carbon trading, and the
like.
[0119] FIG. 8 is an exemplary substantially continuous energy
performance assessment 800. The assessment 800 includes information
about the current energy usage of the facility 104, rating of
energy metrics, and targeted energy usage. For example, the metrics
for current energy usage include the total gas and electric energy
cost per square foot of the facility 104; low and high ranges for
the annual estimated energy wasted, the percent of wasted energy
out of the total energy used, the cost of the annual energy wasted;
and carbon footprint metrics, such as the carbon footprint of the
electrical energy used, the carbon footprint of the gas energy
used, and total carbon footprint for the facility 104. Energy
metrics, such as the baseline energy rating, the peak electrical
energy rating, the gas energy rating, the efficiency of the gas
used for heating, the occurrence of simultaneous heating and
cooling, the nighttime power index, the weekend power index, the
energy management system scheduling, and the full time loading
levels, are rated on a poor, average or good scale. Finally, the
assessment 600 includes a comparison of energy usage, energy
savings, and carbon reductions for the current energy
implementation of the facility with at least two suggested energy
reduction implementations to provide a better target and a best
target.
[0120] In an embodiment, the energy search engine 102 provides the
energy performance of a network of facilities 104 on a
multi-dimensional map. The multi-dimensional map plots one or more
metrics on a geographical map with colors, coordinates and shapes.
For example, a metric proportional to the fraction of a facility's
energy demand that can be shed (reduced) at any given time can be
plotted on a geographical map of the area. Every facility 104 can
be indicated by a circle, with the size of the circle proportional
to the levels of demand (in kW) that can be shed at each facility
104 and the color of the circle indicating the percent facility
demand that can be shed. Similar maps can be used to animate the
effect of a cloud passing on the potential for demand reduction
across a network of facilities 104 based on demand projections
calculated using the energy search engine 102.
[0121] In another example, several metrics can be plotted on a
single map with every metric represented by a layer of a unique
color, with the shades of each color proportional to the level of
the metric (e.g. light shades of a color indicating smaller values
of the metric and darker shades of the same color indicating larger
values of the same metric).
[0122] FIG. 9 is an exemplary continuous yearly key performance
indicator chart 900 and FIG. 10 is an exemplary continuous monthly
key performance indicator chart 1000. Both charts 900 and 1000
track key performance indicators, including but not limited to
baseline electric use index, peak electrical energy use index, gas
energy use index, heating gas efficiency consistency, simultaneous
heating and cooling, nighttime power index, weekend power index,
EMS scheduling, and full-time loading, as described above. Chart
900 indicates the year to date energy performance, while chart 1000
indicates the energy performance for a month. In an embodiment, the
indicator bars are in color to represent score quality/level where
red indicates a poor score, green indicates a good score, and
yellow indicates a mediocre score that could be improved.
[0123] In an embodiment, one or more alerts are associated with one
or more of the metrics. Exceeding, or dropping below a specified
metric value and for a specified amount of time will signal an
alert and cause an action to be taken. The action may include the
closure of relays, a command to be sent via wired or wireless
Ethernet.RTM., RF module, machine interface, wired connection, or
the like. The action may also include a message to be sent to a
computer desktop, a mobile device, a mobile application, a
Facebook.RTM. page, a Twitter.RTM. account, a message board, a
broadcast system, or the like.
[0124] In an embodiment, one or more alerts are associated with one
or more of the metrics. The alerts can be initiated when a metric
exceeds or drops below a certain value for a specified amount of
time if one or more other metrics, measured variables, such as, for
example, temperature, pressure, humidity, flow, current, or the
like, estimated variables, calculated variables, and/or any
mathematical combination of the metrics and/or variables are at,
above, or below a specific value for a specified amount of time,
where the specific value can be a function of one or more metrics,
measured variables, estimated variables, calculated variables, any
mathematical combination of the variables, and/or any mathematical
combination of the metrics.
[0125] Other embodiments of the energy search engine 102 can report
a daily maintenance task list, where the energy search engine 102
generates a list of required maintenance tasks sorted by
criticality, energy performance impact, cost performance impact,
impact on carbon footprint, cost to fix, and the like. Further, an
embodiment of the energy search engine 102 can report equipment
diagnostics such that the energy search engine will continuously
diagnose and rate the performance of each of the building's
equipment 104a.
[0126] Further embodiments of the energy search engine 102
determines and reports on when and where a building or its energy
consuming or energy generating subsystems are operating properly or
are in need of maintenance, tuning, load shifting, load shedding,
or equipment replacement based at least in part on data level flags
that correspond with incremental level changes in energy consumed
or generated.
[0127] In an embodiment, the energy search engine 102 uses day
ahead projected hourly weather data and projected facility schedule
and usage to predict energy performance (e.g. kWhr consumption, kW
demand, efficiency metrics, etc.) based on a calculated baseline
for the facility 104, and correlations of energy baseline with
ambient weather, facility schedule and facility usage.
[0128] In another embodiment, the energy search engine 102 uses day
ahead projected hourly weather data, projected facility schedule,
projected facility usage, utility rate information, day ahead
utility pricing information, day ahead grid information, day ahead
energy cost information, to predict energy cost (kW demand cost,
kWhr consumption cost, gas therm cost, water gallons cost, etc.)
based on a calculated baseline for the facility 104, and
correlations of energy baseline with ambient weather, facility
schedule and facility usage.
[0129] In yet another embodiment, the energy search engine 102
provides data output that are useful to benchmark and evaluate real
property energy consumption and carbon emissions/footprint for
purposes that include projecting the value of operational
strategies and behaviors or equipment replacements that result in
altered real property valuations, and the like.
[0130] In yet further embodiments, the energy search engine 102
provides data output that are useful for auditing compliance with
existing and emerging legislation on energy use, carbon emissions,
and for achieving energy reduction and carbon emission reduction
goals at facilities.
[0131] In other embodiments, the energy search engine 102 provides
preplanned or instant actionable energy optimization strategies
through activation of systems and circuits that shed and/or shift
energy consumption and/or provide supplemental energy resources.
The energy search engine 102 can provide data output to Energy
Management Systems (EMS) and/or Building Management Systems (BMS)
and/or load control relays to respond with either instant or
preprogrammed load shedding strategies, load shifting strategies,
and supplemental energy supply strategies, according to certain
embodiments.
[0132] An embodiment of the energy search engine 102 can use cloud
based NOC Baseline energy management to determine instant peak
demand load shedding, load shifting, supplemental local generating
resources, or the like. Embodiments of the energy search engine 102
can determine and/or control the demand side potential for
adjusting loads and local energy supply for each facility, campus,
or n aggregate of geographically dispersed facilities.
[0133] FIGS. 11, 12 and 13 illustrate exemplary block diagrams of a
system that integrates universally interoperable "smart grid
envisioned" digital energy measurement, energy use analysis, carbon
footprint analysis, greenhouse gas emission analysis, energy
quality and availability analysis, data correction algorithms, data
reduction algorithms, data encryption algorithms, data storage,
data communication, and the digital and/or analog control of energy
used and generated or the carbon footprint or greenhouse gas
emissions that are generated in such processes. Implementations may
selectively include one or more of the modules described in FIGS.
11, 12, and 13.
[0134] One embodiment of the system provides universal real time,
continuous, and autonomous digital measurement of local or remotely
located and energized or de-energized electric circuits that range
in voltage from 0 Volts, a de-energized state, to energized states
of up to 600 VAC or VDC to electric grid circuits that employ
voltage levels in the kilovolt range and electric circuits in phase
configurations, such as single phase, split phase, three phase
Delta, three phase Wye, and the like.
[0135] The measured and analyzed electric circuit data by Module
1301 ADC and Module 1301C Energy Calculations comprises, for
example, line-to-line and line-to-current voltage, powers (total,
active, reactive), line-to-line and line-to-neutral current, power
factor, fundamental and harmonic total energy per phase and for the
sum of phases, fundamental and harmonic active energy per phase and
for the sum of phases, fundamental and harmonic reactive energy per
phase and for the sum of phases, frequency, and harmonics in 1-n
circuits, and the like.
[0136] In one embodiment, the real time digital energy and carbon
footprint data reporting rates are varied and substantially lower
than the energy data sampling rates from Module 1301 ADC in FIG.
13. This is due to application of an energy or environmental data
reduction/data compression technique an embodiment of which is
illustrated in FIG. 14 and applied to a typical circuit embodiment,
such as one embodiment shown in FIG. 12, comprising Data Gateway
Module 1303, Data Analysis Module 1304, and Data Validation and
Error Correction Module 1305; and another embodiment shown in FIG.
13, comprising Data Gateway Module 1303, Data Analysis Module 1304,
and Data Validation and Error Correction Module 1305. One
embodiment of the energy or environmental data reduction/data
compression technique enables use of low, medium, or high speed
data communication channels to deliver accurate real time energy
use and carbon footprint reporting about electric circuit energy or
environmental conditions that are digitally measured through the
use of a higher data sampling rate that typically ranges between
approximately 10 samples/second and approximately 24,000
samples/second and on 1 to n measured electric circuits, devices,
or appliances. In other embodiments, energy or environmental data
sampling rates can be higher or lower.
[0137] FIG. 13 illustrates an embodiment of the energy measurement,
analysis, communication, and control system. The system comprises
Digital Energy Measurement Modules 1301, 1301B, 1301 Phase;
Current/power/voltage Measurement Modules 1301C for energy
calculation or energy data processing; and Modules 1303, 1304, 1305
for enabling energy data quantity reduction and data error
correction without compromising the value of the measured data.
[0138] The energy data quantity reduction is based, at least in
part, on application of an embodiment of the data reduction/data
compression technique illustrated in FIG. 14 and filtering
parameters, such as change in measured energy (+-x %), rate of
change of measured energy (t), and the like.
[0139] Data Analysis Module 1304 comprises ports where analog I/O
analog sensor input and output control signals are exchanged with
external sensors, and devices and digital I/O modules where
external digital sensor input and control information are exchanged
with outside sensors and devices.
[0140] Module 1306 provides compensated inputs for on-board and/or
remote temperature sensors. Module 1306A provides inputs from other
on-board and/or remote sensors such as pressure sensors, light
sensors, acceleration sensors, tension-meters, flow sensors, gas
sensors, microphones, and others.
[0141] Module 1307 provides PWM controller outputs Class D or Class
E PWM control signals for the efficient control of external
electric loads through direct connection with high speed electronic
switches such as Triac's, MosFets, or IGFET's.
[0142] Module 1308 enables optional digital data encryption. Module
1309 is used for digital data storage. Module 1311 provides
communications and control commands. Module 1310 provides global
positioning or location sensing (GPS). Module 1312 is a web server
and Module 1313 provides a human machine interface that can be an
LCD display and keypad or keyboard, or the like.
Digital Energy Measurement
[0143] Modules 1301, 1301B, 1301 Phase, 1301C, and 1302 perform
digital energy measurement and calculations in electric circuits
that are located in any residence, building, commercial or
industrial facility, in electric circuits that are used for
powering electric powered transportation systems and/or charging
electric vehicles, and energy that is provided or delivered by any
and all electric generating power systems including solar, wind,
fuel cell, micro turbines, or other types of electric generating
devices and systems.
[0144] Measurement devices 1302 are associated with an electrical
circuit and acquire an analog measurement of the current, voltage
or power in the associated electrical circuit. Measurement devices
1302 can be, for example, current transformers (CT) or the like.
When current in a circuit is too high to directly apply to
measuring instruments, a current transformer 1302 produces a
reduced current accurately proportional to the current in the
circuit, which can be conveniently connected to measuring and
recording instruments. The current transformer 1302 also isolates
the measuring instruments from what may be very high voltage in the
monitored circuit.
[0145] Current transformers 1302 couple to analog to digital
converter (ADC) Modules 1301 through CT Polarity Correction Modules
1301B. Modules 1301B comprise a latching double pole double throw
gate and permit the automatic correction for the polarity of the
measured current, should the current transformer 1302 be
incorrectly installed. The position of latching DPDT gate in Module
1301B is determined by algorithms that operate on Modules 1305 and
1306 to evaluate when the voltage phase from Phase ADC Module 1301
and the current phase from Module 1301 of a given measurement
circuit are separated by more than approximately 90 degrees and
less than approximately 270 degrees. When this condition exists,
the CT 1302 polarity is deemed incorrect and the position of
latching DPDT gate is switched to the alternate position.
[0146] Other examples of measurement devices 1302 are Rogowski
coils, DC shunts, external digital current sensors, and external
analog current sensors. In one embodiment, the system provides for
the intermixed use of clamp on current measuring toroid
transformers (CTs), Rogowski coils, DC shunts, or other current
measuring devices.
[0147] ADC Modules 1301 convert the analog measurement from the
measurement devices 1302 into a digital measurement for use in the
system. ADC Modules 1301 comprise an analog to digital converter,
and at least one jumper. In one embodiment, the analog to digital
converter is an Analog Devices IC, part number ADC 5169, or the
like.
[0148] In an embodiment, the jumper configuration of the ADC Module
1301 is field selectable for the accurate measurement of 1 to n
electric circuit phase configurations including single phase, split
phase, three phase Delta, and three phase Wye. Phase configuration
and association of the ADC module with its respective voltage phase
can also be done in software in another embodiment.
[0149] Phase ADC Module 1301 couples to energized circuits with
phase A, B, and/or C through resistive voltage dividers to
digitally measure voltage amplitude and phase information.
[0150] The digital measurement information collected by the Phase
ADC Module 1301 and the ADC Modules 1301 for 1 to n measured
electrical circuits is sent to the Energy Calculation Module 1301C.
The data sample rate ranges between approximately 10 samples/second
to approximately 24 kilo samples/second.
[0151] Data reduction processes comprises Modules 1303, 1304, and
1305 utilizing an embodiment of the data reduction/data compression
technique illustrated in FIG. 14 to substantially reduce the
quantity of measured energy data that will be reported in real
time, stored in memory data, or "pushed" to remote or cloud data
base or "pulled" from a user inquiry. The reduced quantity of
energy data is based on previously defined or user defined data
filtering parameters such as amount of change or rate of change of
measured or calculated energy data. The energy or environmental
data quantity reduction technique that is shown in embodiments is
broadly applicable to any energy or environmental device or network
of devices. Module 1303 samples the measured and calculated energy
data from 1 to n ADCs at frequencies up to approximately 24,000
samples per second and sends the data to the Data Validation and
Correction Module 1305 for determination of data accuracy. Module
1304 analyses the energy data and also receives input from other
internal and external sensors, Modules 1306 and 1306A. The Analysis
Module 1304 also contains algorithm for reducing the number of
points passed from Module 1305 and sends the formatted and
substantially reduced quantity of measurement data to at least
Modules 1311, 1312, 1313 and 1309. Based on comparison of this
measured energy data and input from external environmental sensors,
a control signal is sent to external devices for load control
through the Analog I/O, Digital I/O, or PWM Controller Module
1307.
Digital Data Analysis with Automated Error Correction
[0152] Data Validation and Correction Module 1305 and Data Analysis
Module 1304 for 1-n electric circuits provide real time digital
analysis, validation, and auto correction of measured energy use
and the quality of energy that is available at a power generating
system that is found on the grid, smart grid, micro grid,
residence, building, commercial facility, or for electric vehicle
and electric powered transportation systems, according to certain
embodiments. Embodiments include electrical circuits powered by
solar, wind, fuel cells, and any type of electric energy
generator.
[0153] In one embodiment, Module 1305 generates signals to control
the Data Analysis Module 1304 when the voltage phase and the
current phase of a given ADC Module 1301 exhibits more than
approximately 90 degrees and less than approximately 270 degrees of
phase differential. Software used by the Data Analysis Module 1305
automatically identifies the correct phase that is associated with
ADC Module 1301 and attaches this phase information to the correct
energy information from ADC Module 1301 in the Data Validation and
Error Correction Module 1305.
[0154] In an embodiment, Module 1306 does not configure the ADC
Module 1301. Instead the output data from a specific ADC Module
1301 is correctly attached to the correct phase data (A, B, C, . .
. n) from Phase ADC Module 1301 in Module 1305.
[0155] In one embodiment, Data Validation and Correction Module
1305 analyzes energy spikes to determine whether the spike is valid
or is noise or corrupted data by acquiring additional samples at
approximately the same time as the energy spike from Module 1303
which provides a data gateway. If the energy spike is a valid data
measurement, the amplitude of the later acquired sample will be
proportional to the energy spike. If the amplitude of the later
acquired data is substantially different than the energy spike,
Module 1305 determines that the energy spike was caused by noise,
and treats the bad data as irrelevant and not worthy of being
passed on for storage or "push" or "pull" communication.
[0156] Data Analysis Module 1304 processes measured energy data and
compares it with external environmental and facility use
information to derive and deliver electric load, device, and
BMS/EMS control signals that are used to reduce or increase the
electric energy in a specific circuit. Data Error Correction Module
1305 processes measured energy data and compares it with prior data
samples to insure that only relevant and accurate data is passed
from Data Gateway Module 1303 to the Communication Module 1311 or
to I/Os or to PWM Controller Module 1307.
Digital Data Encryption
[0157] Data Encryption Module 1308 optionally encrypts the data
that is derived from the measuring of all electric circuits and the
location of circuits and measurement apparatus using secure and
anti-hacking data encryption algorithms. Module 1308 can also be
positioned just downstream of Module 1304. In one embodiment, the
Data Encryption Module 1308 uses anti tamper and anti hacking
handshaking through the use of existing and emerging "smart grid"
security data protocols.
Digital Data Storage
[0158] Data Storage Memory Module 1309 stores the measured and
digitized electric circuit data and measured electric energy
quality data. In an embodiment, the Data Storage Memory Module 1309
provides a data buffer in case communication channel with the local
or remote host is broken. The buffer decouples data sampling rates
and data reporting rates. The data is stored locally at the
required sampling rate until the communication lines are
re-established. The data is then transferred to the host ensuring
no data loss during communication breakdown.
Universally Interoperable Communications and Control
[0159] Data Command and Communication Module 1311 implements
predetermined and automated power reduction steps in energy use
systems, smart appliances, or plug loads, based at least in part on
the sensor data or on external demand response commands, according
to certain embodiments.
[0160] Data Command and Communication Module 1311 provides the
system with a unique address. Module 1311, in one embodiment, can
push data to and/or pull data from 3rd party hardware or software
including but not limited to structured query language (SQL) and/or
SAP databases, Cloud based databases, and/or any type of computing
device.
[0161] Data Command and Communication Module 1311 pushes digitally
measured electric circuit energy use data from 1-n circuits using
protocols, such as, for example, Ethernet, ZigBee, PLC (Power Line
Carrier), WiFi, WiMax, GSM to a remote device for real time
analysis, for real time analysis and control, and/or to a remote
structured query language (SQL), SAP, or cloud data base for
storage, comparison of data, data mining, and data analysis for a
multiplicity of purposes including billing and control of circuit
circuits, smart appliances, electric vehicle and electric
transportation systems. The data can be delivered in XML, JSON,
CSV, ASCII Strings, Binary Strings, and other formats.
[0162] In an embodiment, the Data Command and Communication Module
1311 uses data clock synchronization and system clocking via
Ethernet connection. Other system connections include networked
TCP/IP, client-server ModBus, BacNet, mesh network ZigBee wireless,
WiFi, and WiMax that are operating either individually or
concurrently to interact with 3rd party hardware and software.
[0163] The Data Command and Communication Module 1311 can
simultaneously retain a copy of the measured data in onboard memory
so that it can be viewed and accessed through the web server,
according to certain embodiments.
[0164] In one embodiment, the Data Command and Communication Module
1311 can also act as a slave to the acquisition host, such as a PC
or the like, and communicate with the master host in one of several
standard protocols, such as Ethernet protocols including ModBus and
BacNet, for example. The Module 1311 then acts as a translation of
the protocol to serial communication.
[0165] The software Digital I/O module and Analog I/O module
interfaces with the Data and Command Communication Module 1311 and
with the Data Analysis Module 1304 to enable two-way software
commands and interrupts to be exchanged between the Data Analysis
Module 1304 and other BMS, BEMS, electrical vehicle charge
stations, motor control systems, electrical control systems, smart
appliances, programmable logic controllers, and the like.
[0166] In an embodiment, the Temperature Sensor Compensation Module
1306 comprises calibration compensation look up tables to correctly
utilize J or K thermocouple devices or wired or wireless
thermostats for external local or remote measurement of
temperature.
[0167] The PWM Controller Module 1307 is directed either by the
Data Analysis Module 1306 or the Communication and Command Module
1311 to output a signal that consists of variable duty cycle pulses
for load control through external high speed electronic switches
such as high power MOSFETS, IGFETs, or other high speed electronic
switching devices. Such variable width pulses enable an external
high speed electronic switch to control the electric energy and
carbon footprint of any electric circuit or device including
lighting circuits, motor circuits, air handling systems, HVAC
compressor systems, and the like. This embodiment when combined
with an external high speed electronic switch refers to a Class D
or Class E control system design.
[0168] The GPS Location Information Module 1310 interfaces with the
Data and Command Communication Module 1311 and maps the location of
each identified circuit board that has a unique MAC address.
Universally Interoperable Control
[0169] Embodiments of the system of FIG. 11 can interface with "a
smart device" "a smart appliance" "a smart building" "the smart
grid", renewable energy generators, and the like.
[0170] In an embodiment, Analog/digital input/output I/O modules
interface external sensors with the Data Analysis Module 1304 and
the Data Command and Communication Module 1311. Sensors, such as,
for example, temperature sensors, humidity sensors, light sensors,
occupancy sensors, motion sensors, acceleration sensors, vibration
sensors, flow sensors, wind speed, heat sensors, gas sensors, gas
spectrometers, laser sensors, humidity sensors, and other
environmental sensors such as water flow, air flow, and gas flow
provide data, including environmental, fuel type, or other data, to
the Module 1304 or 1311 where the data is analyzed to calculate
energy loads, determine possible energy reduction, identify
malfunctioning systems, and/or the like.
[0171] Data Command and Communication Module 1311 implements
predetermined and automated power reduction steps in energy use
systems, smart appliances, or plug loads, based at least in part on
the sensor data or on external demand response commands, according
to certain embodiments.
[0172] Further, in an embodiment, the analog/digital I/O module
interfaces with analog sensor input or digital input, analog or
digital control circuit input, and output circuits for localized or
remote control of relays, switches, programmable logic controllers,
Building Management Systems (BMS), Building Energy Management
Systems (BEMS), energy management and carbon footprint reporting
systems, or the like. In another embodiment, the analog/digital I/O
module interfaces with pulse counters from natural gas or water
meters to integrate this additional data.
Customer Engagement
[0173] Web server 1312 and Human Machine Interface (HMI) 1313
provide the user with a Web-based user interface to the system of
FIG. 13. Examples of HMI 1313 are 8, 16, or more segment LEDs or
LCD panels, Keypads, Qwerty keyboards, and the like. Embodiments
provide user interface software that is accessible via Ethernet
from personal computers (PCs) on the local area or wide area
network.
[0174] In one embodiment, the user interface allows the user to
define the grouping of circuits to be measured and the locations
for the circuits to be measured. The system provides users with
"drag and drop" functionality of circuits between groups and
locations and "drag and drop" functionality for charting and
reporting in a mobile app. Users can also, in one embodiment, view
real time or stored and "pushed" or "pulled" energy use on Mobile
platforms, such as for example, I-Phone, Android, BlackBerry, and
the like.
[0175] The user can define minimum and maximum alert thresholds on
all measured and calculated metrics, such as, voltage, current,
energies, energy consumption rate, powers, power factor, cost, cost
rate, energy efficiency metric, energy efficiency rating, and the
like, for each circuit, group of circuits and location. Comparative
alert thresholds are used on metrics and for circuits where alerts
are triggered by relative energy signature of circuits, groups and
locations with each other or with established baselines or
benchmarks. Predictive alert thresholds are used on metrics and for
circuits where alerts are triggered by projected energy consumption
of a circuit, group or location. When an alert is triggered, the
system provides the user with an alert through email, text message,
Facebook, Twitter, voicemail, RSS feeds, multi-media message
automatic alerts, and the like. In one embodiment, the alert is
accompanied by a description of the trigger event including charts
and reports on history before alert trigger and projected
consumption and results.
[0176] In another embodiment, through the Web Server 1312 or the
push capability, the user is provided with animated and interactive
desktop and mobile widgets for communicating energy consumption
levels, energy ratings and critical energy conservation measures to
end users. In another embodiment, the system communicates energy
consumption levels, energy ratings and critical energy conservation
measures to end users through RSS feeds with desktop tickers.
[0177] Other embodiments determine the need for air or fluid filter
replacement, belt tension, belt alignment, worn or damaged
bearings, worn or damaged gears, poor lubrication, damaged anchor
or frame, damaged or worn brushes, unbalanced voltage, poor power
quality, and the like based on the electrical signature. In an
embodiment, the electrical signature comprises at least one of a
current and/or voltage waveform, current and/or voltage levels and
peaks, power factor, other sensor information, such as temperature,
vibration, acceleration, rotation, speed, and the like, of any
"downstream" motor or pump.
[0178] An embodiment of the algorithm for Energy Data Reduction
relates to bandwidth issues that will be encountered on the `smart
grid" just as MPEG did for audio and video. With a plethora of
measurement and reporting systems running at high speed, data
collection will overload a network with data. Use of this technique
or method on any chip, device, circuit or computer will reduce the
communication bandwidth and processing requirements of said
devices.
Device Embedded in Power Distribution Panel
[0179] Embodiments can be in the form of an enclosure mounted
adjacent to the power distribution panel and connected to said
panel through a circuit breaker. Current sensors are plugged into
the device. Examples of a power distribution panel are a main
switch board, a sub panel, a distribution panel/box, an MCCR, and
the like.
[0180] An embodiment that is embedded in the power distribution
system permits "plugging in" current sensors directly into the
power distribution system where voltage information can be acquired
internally from the panel's main power distribution bars or through
a connection to a circuit breaker.
[0181] In an embodiment, current sensors can also be embedded in
the circuit breakers and can communicate measured energy data and
control signals via wireless, wired or PLC to the embedded
device.
[0182] In another embodiment, current sensors can be self-powered
from the pickup and rectification of electromagnetic fields or
direct connection to energized circuits with or without
re-chargeable battery backup and can communicate measured energy
data or control signals via wireless, wired or PLC to the embedded
device.
[0183] In a further embodiment, current sensors can be in the form
of shunt resistors modules that are placed in series with the
circuit breakers and can communicate measured circuit data and
command controls via wireless, wired or PLC to the embedded
device.
Device Embedded in Construction of Circuit Breaker
[0184] Embodiments can be installed in a circuit breaker with a
built-in current sensor, CT or shunt, and a wireless, wired or PLC
communication and command module or connect to an adjacent circuit
breaker to send and receive information.
[0185] An embodiment can contain circuit protection and/or circuit
breaking capability. Another embodiment can contain power factor
correction capability. A further embodiment can contain frequency
shifting and switching capability, such currently employed by
variable frequency drives, Class D or Class E control circuits, and
the like that use high speed electronic switching devices such as
TRIAC or MOSFET, for example.
Device Installed in Occupied or Monitored Space
[0186] Embodiments can be in the form of a small enclosure mounted
in the space to be monitored. An embodiment can contain a wireless
or wired communication module, occupancy sensor, occupancy counter,
light sensor, temperature sensor, current sensor, gas sensor, heat
sensor, rechargeable battery backup, solar PV panel for self
powered systems, LED displays and the like.
[0187] Another embodiment can communicate with other devices and/or
instruments in the vicinity, such as, for example, thermostats both
controlling and non-controlling, VAV controllers, mechanical or
electrical shades, automatic door locks, door sensors, card
scanners, RFID devices, generator controller, and the like.
[0188] Other embodiments can be part of a mesh network in
peer-to-peer, client-server, or master-slave configuration and yet
further embodiments can be a Plug & Play, install and forget,
stand alone measurement, communication, and control system.
Device Installed in Motors and Other Electrical Equipment
[0189] Embodiments can be embedded in motors, appliances, pumps,
fans, lighting fixtures, electrical equipment, variable frequency
devices, power supplies, generator controllers, or other electrical
equipment and appliances, such as power outlets, power sockets,
power strips, power extensions, power adapters, light switches, and
the like.
[0190] Other embodiments can contain high speed electronic
switching devices, such as, for example, TRIACs or MOSFETs that
operate in Class D or Class E mode and based on control signals
from device for load control capability. Further embodiments can
contain solid state relay or other high speed high power switching
devices.
[0191] Embodiments can contain one or more wireless or wired
communication modules, occupancy sensors, occupancy counters, light
sensors, temperature sensors, wireless thermostats, current
sensors, gas sensors, heat sensors, rechargeable battery backups,
solar PV panels for self powered systems, LED displays, and the
like. Other embodiments can communicate with other devices and/or
instruments in the vicinity, such as, for example, controlling,
non-controlling, wired or wireless thermostats, variable air volume
controllers, mechanical or electrical shades, automatic door locks,
door sensors, card scanners, RFID devices, and the like.
[0192] Embodiments can measure and analyze data from internal and
external sensors including current, voltage levels and waveforms,
temperature, vibration, motor speed, motor torque and mechanical
load, and the like.
[0193] Other embodiments can calculate and communicate in real time
an efficiency rating of said motor or electrical equipment that may
take into consideration an ambient condition of the motor or
electrical equipment in addition to the measured and analyzed data.
The ambient condition can be communicated to the device through the
embedded communication module, analogue inputs or digital
inputs.
[0194] Other embodiments can be part of a network in mesh,
peer-to-peer, client-server, or master-slave configuration and yet
further embodiments can be a Plug & Play, install and forget,
stand alone measurement and control system.
[0195] Depending on the embodiment, certain acts, events, or
functions of any of the algorithms described herein can be
performed in a different sequence, can be added, merged, or left
out all together (e.g., not all described acts or events are
necessary for the practice of the algorithm). Moreover, in certain
embodiments, acts or events can be performed concurrently, e.g.,
through multi-threaded processing, interrupt processing, or
multiple processors or processor cores or on other parallel
architectures, rather than sequentially.
[0196] The various illustrative logical blocks, modules, and
algorithm steps described in connection with the embodiments
disclosed herein can be implemented as electronic hardware,
computer software, or combinations of both. To clearly illustrate
this interchangeability of hardware and software, various
illustrative components, blocks, modules, and steps have been
described above generally in terms of their functionality. Whether
such functionality is implemented as hardware or software depends
upon the particular application and design constraints imposed on
the overall system. The described functionality can be implemented
in varying ways for each particular application, but such
implementation decisions should not be interpreted as causing a
departure from the scope of the disclosure.
[0197] The various illustrative logical blocks and modules
described in connection with the embodiments disclosed herein can
be implemented or performed by a machine, such as a general purpose
processor, a digital signal processor (DSP), an ASIC, a FPGA or
other programmable logic device, discrete gate or transistor logic,
discrete hardware components, or any combination thereof designed
to perform the functions described herein. A general purpose
processor can be a microprocessor, but in the alternative, the
processor can be a controller, microcontroller, or state machine,
combinations of the same, or the like. A processor can also be
implemented as a combination of computing devices, e.g., a
combination of a DSP and a microprocessor, a plurality of
microprocessors, one or more microprocessors in conjunction with a
DSP core, or any other such configuration.
[0198] The steps of a method, process, or algorithm described in
connection with the embodiments disclosed herein can be embodied
directly in hardware, in a software module executed by a processor,
or in a combination of the two. A software module can reside in RAM
memory, flash memory, ROM memory, EPROM memory, EEPROM memory,
registers, hard disk, a removable disk, a CD-ROM, or any other form
of computer-readable storage medium known in the art. An exemplary
storage medium can be coupled to the processor such that the
processor can read information from, and write information to, the
storage medium. In the alternative, the storage medium can be
integral to the processor. The processor and the storage medium can
reside in an ASIC.
[0199] The above detailed description of certain embodiments is not
intended to be exhaustive or to limit the invention to the precise
form disclosed above. While specific embodiments of, and examples
for, the invention are described above for illustrative purposes,
various equivalent modifications are possible within the scope of
the invention, as those ordinary skilled in the relevant art will
recognize. For example, while processes or blocks are presented in
a given order, alternative embodiments may perform routines having
steps, or employ systems having blocks, in a different order, and
some processes or blocks may be deleted, moved, added, subdivided,
combined, and/or modified. Each of these processes or blocks may be
implemented in a variety of different ways. Also, while processes
or blocks are at times shown as being performed in series, these
processes or blocks may instead be performed in parallel, or may be
performed at different times.
[0200] Unless the context clearly requires otherwise, throughout
the description and the claims, the words "comprise," "comprising,"
and the like are to be construed in an inclusive sense, as opposed
to an exclusive or exhaustive sense; that is to say, in the sense
of "including, but not limited to." The words "proportional to", as
generally used herein, refer to being based at least in part on.
The words "coupled" or connected", as generally used herein, refer
to two or more elements that may be either directly connected, or
connected by way of one or more intermediate elements.
Additionally, the words "herein," "above," "below," and words of
similar import, when used in this application, shall refer to this
application as a whole and not to any particular portions of this
application. Where the context permits, words in the above Detailed
Description using the singular or plural number may also include
the plural or singular number respectively. The word "or" in
reference to a list of two or more items, that word covers all of
the following interpretations of the word: any of the items in the
list, all of the items in the list, and any combination of the
items in the list.
[0201] Moreover, conditional language used herein, such as, among
others, "can," "could," "might," "may," "e.g.," "for example,"
"such as" and the like, unless specifically stated otherwise, or
otherwise understood within the context as used, is generally
intended to convey that certain embodiments include, while other
embodiments do not include, certain features, elements and/or
states. Thus, such conditional language is not generally intended
to imply that features, elements and/or states are in any way
required for one or more embodiments or that one or more
embodiments necessarily include logic for deciding, with or without
author input or prompting, whether these features, elements and/or
states are included or are to be performed in any particular
embodiment.
[0202] The teachings of the invention provided herein can be
applied to other systems, not necessarily the systems described
above. The elements and acts of the various embodiments described
above can be combined to provide further embodiments.
[0203] While certain embodiments of the inventions have been
described, these embodiments have been presented by way of example
only, and are not intended to limit the scope of the disclosure.
Indeed, the novel methods and systems described herein may be
embodied in a variety of other forms; furthermore, various
omissions, substitutions and changes in the form of the methods and
systems described herein may be made without departing from the
spirit of the disclosure. The accompanying claims and their
equivalents are intended to cover such forms or modifications as
would fall within the scope and spirit of the disclosure.
* * * * *