U.S. patent application number 14/224751 was filed with the patent office on 2014-07-24 for energy-saving measurement, adjustment and monetization system and method.
This patent application is currently assigned to Energy Resource Management Corp.. The applicant listed for this patent is Energy Resource Management Corp.. Invention is credited to William Campbell, Terry Egnor, Howard Reichmuth.
Application Number | 20140207299 14/224751 |
Document ID | / |
Family ID | 44761620 |
Filed Date | 2014-07-24 |
United States Patent
Application |
20140207299 |
Kind Code |
A1 |
Reichmuth; Howard ; et
al. |
July 24, 2014 |
ENERGY-SAVING MEASUREMENT, ADJUSTMENT AND MONETIZATION SYSTEM AND
METHOD
Abstract
System and method include precisely modeling of a facility's
energy usage over time based on historic data, and precisely
predicting or measuring its actual, reduced energy usage over time
after a redesign, retrofit, or renovation, or other positive change
to the facility. The energy cost savings, whether over a time point
of view (POV) of predicted, real-time, or historic, are creditable
to the intervening remediation or renovation of the facility's
energy footprint. In accordance with one embodiment,
multiple-variable inputs are modeled using arithmetic regression
and steepest-descent convergence arithmetic solutions based in
large part on building-science (construction) data versus outside
average temperature (t) that simplifies the modeling and
measurements. Additionally is thus addressed along with a system
and method that is more accurate, more repeatable, more reliable,
and thus more credible and more readily monetized.
Inventors: |
Reichmuth; Howard; (Hood
River, OR) ; Egnor; Terry; (Portland, OR) ;
Campbell; William; (Portland, OR) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Energy Resource Management Corp. |
Portland |
OR |
US |
|
|
Assignee: |
Energy Resource Management
Corp.
Portland
OR
|
Family ID: |
44761620 |
Appl. No.: |
14/224751 |
Filed: |
March 25, 2014 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
13718752 |
Dec 18, 2012 |
8706308 |
|
|
14224751 |
|
|
|
|
13081855 |
Apr 7, 2011 |
8355827 |
|
|
13718752 |
|
|
|
|
61342165 |
Apr 8, 2010 |
|
|
|
Current U.S.
Class: |
700/291 |
Current CPC
Class: |
G05B 19/02 20130101;
G06Q 50/06 20130101; Y02P 90/845 20151101; G06Q 40/12 20131203;
Y02P 90/84 20151101 |
Class at
Publication: |
700/291 |
International
Class: |
G06Q 50/06 20060101
G06Q050/06 |
Claims
1. An energy efficiency meter, comprising: an energy load meter
configured to measure one or more energy streams into a facility,
wherein the one or more energy streams collectively comprise a
total energy load of the facility; one or more non-transitory,
machine-readable data storage media operably coupled with the
energy load meter suitably to receive from the meter and to store
energy load data collectively representing the total energy load of
the facility throughout a first time period, the one or more data
storage media further having stored thereon device-executable
energy efficiency instructions; and data processing circuitry
operably coupled with the one or more data storage media suitably
to access each of the energy load data and the energy efficiency
instructions, wherein the data processing circuitry is configured
to process the energy load data by executing the energy efficiency
instructions, and the processing includes: aggregating the
collective energy load data for the first time period; processing
the aggregated energy load data together with thermal data
representing an average external environmental temperature of the
facility during the first time period, disaggregating plural
identified end-use energy load portions from the processed energy
load data; and producing a first analog facility model comprising
the plural identified end-use energy load portions corresponding to
the external environmental temperature during the first time
period.
2. The energy efficiency meter of claim 1, further comprising: a
thermal meter suitable to measure the external environmental
temperature during the first time period and to output thermal data
corresponding to the measured external environmental
temperature.
3. The energy efficiency meter of claim 1, wherein the energy load
meter comprises plural energy load measurement portions each
configured to measure one of the one or more energy streams.
4. The energy efficiency meter of claim 3, wherein a first of the
energy streams is an electrical energy stream.
5. The energy efficiency meter of claim 4, wherein a second of the
energy streams is a flammable gas stream, and wherein the flammable
gas of the stream is selected from the group consisting of natural
gas, propane, butane, and liquefied petroleum gas.
6. The energy efficiency meter of claim 1, wherein the first period
of time is selected from the group consisting of an hour, a day, a
week, a month, and a year.
7. The energy efficiency meter of claim 1, wherein the processing
the aggregated energy load data together with thermal data
comprises executing an iterative steepest-descent
solution-convergence algorithm.
8. The energy efficiency meter of claim 7, wherein the iterative
steepest-descent solution-convergence algorithm utilizes assumed
values for each of an external electric gain parameter and a
heating efficiency parameter, and iteratively solves for each of
internal electric gain, normalized aggregate heating loss, cooling
efficiency, service water heating, heat intercept, and cool
intercept parameters.
9. The energy efficiency meter of claim 8, wherein executing the
iterative steepest-descent solution-convergence algorithm
comprises: adjusting the values of each of the external electric
gain parameter and the heating efficiency parameter by an increment
of 1/1000.sup.th of their initial assumed value in each successive
iteration; solving for the values of each of internal electric
gain, normalized aggregate heating loss, cooling efficiency,
service water heating, heat intercept, and cool intercept
parameters; and evaluating whether a fit indicator indicates that
executing the algorithm with the adjusted values produced a better
fit between the model and measured facility energy load data for
the first time period.
Description
RELATED APPLICATIONS
[0001] This application is a continuation of and claims the benefit
of priority to co-pending U.S. Non-Provisional application Ser. No.
13/718,752, filed on Dec. 18, 2012; which in turn is a continuation
of and claims the benefit of priority to U.S. Non-Provisional
application Ser. No. 13/081,855, filed on Apr. 7, 2011 and issued
as U.S. Pat. No. 8,355,827 on Jan. 15, 2013; which in turn claims
the benefit of priority to U.S. Provisional application No.
61/342,165, filed on Apr. 8, 2010, the contents of each of which
are hereby incorporated herein in their entirety by this
reference.
FIELD OF THE INVENTION
[0002] The invention relates generally to the field, of energy
savings. More particularly, the invention relates to more
accurately modeling past and future use, measuring current use and
savings, diagnosing current problems and making adjustments where
sensible, and monetizing energy savings.
BACKGROUND OF THE INVENTION
[0003] Conventional energy savings calculations typically rely on
energy use baseline projections derived from built up engineering
models or statistical regression models acting upon historic-use
data. At the core is the ability to distinguish between energy
reductions attributable to better energy systems design and
modifications (savings) versus those attributable to business as
usual. The former are thought to have additional value, including
carbon emission credits or offsets, while the latter do not. The
ability to accurately and confidently discriminate between the two
and to quantify future energy use in a credible, transparent, and
transactable manner has been problematic.
[0004] Conventional approaches include complex, statistical
regression models as well as engineering models that analyze
multiple energy consumption subsystems and attempt to aggregate
component data into a statistical future-use model system. Such
aggregated data are compared to ongoing, metered use data for the
modeled and aggregated subsystems to determine energy savings that
might flow, for example, from converting the central heating plant
component of an HVAC system to more energy-efficient,
plural-distributed-space heating subsystems.
[0005] The prior art takes two basic approaches. The first involves
using a regression fit to the data that can be made reasonably
accurate if a full before-and-after data base exists. The weakness
is that it employs statistical fit parameters that do not represent
physical reality and therefore may not be able to represent any
physical or operational changes in the performance of the site
going forward. The second approach is an engineering modeling
approach that, according to the literature, models actual energy
usage to an accuracy that is typically only approximately pins or
minus thirty percent (.+-.30%) unless done in a research context
where the cost and time involved is out of proportion to the value
of the savings.
[0006] FIG. 1 is a graph that illustrates a prior art approach to
regression modeling of a single fuel based upon historic data to
predict future energy use (upper trace "before" case), as well as
predicted or actual energy consumption (lower trace "after" case),
over time before and after a potentially creditable redesign or
renovation of an existing facility. The horizontal axis represents
the passage of time, whether measured in hours, days, months, or
years. The upper smooth trace illustrates the historic baseline
energy consumption typically derived by statistical modeling from
utility bill or model data. The continued dashed line extends that
historic baseline as adjusted for routine variables such as
temperature. This represents what energy the building would have
consumed absent the efficiency improvements. The diagonal
descending line illustrates the change in use during the
installation of the efficiency measures. The lower smooth trace
illustrates the predicted value of the adjusted as-improved energy
consumption (which of course is lower if the improvement is truly
so) based upon a second regression or engineering analysis that
attempts to capture the projected impact of the improvements. This
is used by the building operator to identify anomalous behavior for
fault detection and diagnostics. The rectangular points
superimposed on the lower trace illustrate the measured, actual
consumption of the as-improved building as measured by a
utility-grade meter(s).
[0007] The difference at any point in time between the upper
adjusted historical trace and the lower meter points of FIG. 1
represents the improvement-based energy savings or cost avoidance,
whether measured in power, energy, carbon, or cash value. Thus, if
the upper trace was accurate and, more importantly, credible, then
the energy savings based upon the improvement would be clear.
Unfortunately, as will be discussed further below, typically the
trace is neither accurate nor credible. Thus, there remains no cost
effective credible basis for energy credits or carbon offsets based
on conventional modeling and metering technologies or methods.
[0008] Conventional approaches also make simplifying assumptions
and use simplistic approaches to both regression modeling to
predict future baseline energy use and to accurate measuring of
current energy use. The net effect of these simplifications is
inaccuracy and uncertainty, e.g. lack, of credibility, in modeling
and measurement. Creditable energy savings, e.g. carbon offsets or
hard cash, often end up in the wrong pocket. This is because
presumably loose energy savings performance and/or measurement
standards (e.g. energy savings must be 10% or more on the energy
bill) are typically built into contractual agreements that favor
one party to an energy credit or monetization transaction over
another (e.g. a utility over a customer). For example, an energy
provider or distributor might presume that difficult to measure
energy cost savings are only 10% and will be willing to pay only
for such a conservative savings presumption, while the actual
savings over time are significantly greater. Often the presumption
is expressed: if a customer installs a particular energy-savings
package, then the customer will be "deemed" to have saved a
quantity of energy, and there is no "need" for precision in
measurement of energy savings or indeed any measurement at all.
Thus, precision measurement is obviated and energy generation is
relegated to a utility's administrative or customer service line
item instead of being properly ascribed as a saleable (or otherwise
monetizable) product of energy conservation.
[0009] In 2007, the Efficiency Valuation Organization (EVO)
published the international Performance Measurement and
Verification Protocol (IPMVP). The IPMVP purports to establish
criteria for measuring and accounting for energy savings based upon
a variety of assumptions, metrics, and guidelines. It further
suggests the importance of modeling only relevant independent
variables and not modeling irrelevant variables. It identifies many
such supposedly relevant independent variables. The IPMVP fails to
identify any reality-based, i.e. building science-based, variables
as part of its guidelines or proposals.
[0010] IPMVP notwithstanding, there is no widely accepted cost
effective "meter" for measuring energy use reductions attributable
to energy efficiency improvements that can be routinely deployed in
a business context. Part of the reason may be that utility
companies have their own cultural focus, even the most progressive
and decoupled of them. That focus is energy sales rather than
energy efficiency with its virtues including coincident factor, no
transmission cost, local economy improvement and competitiveness,
lower first cost, stable long-term costs, etc. Thus, the widespread
use of Option C metering as described by IPMVP has been largely
ignored in favor of central power station project developments by
utilities.
[0011] Building science has taught us that each building has a
"signature" that describes the building's reaction to variable
temperature throughout the year whether measured in seasons,
months, 24-hour periods, hours or some other time base. A typical
building signature graph is shown in FIG. 2. Those of skill in the
art will understand, that the ELECTRICITY curve goes up with
increasing temperature, in large part due to air
conditioning/ventilation demand, while the GAS curve goes up with
decreasing temperature, in large part due to heating demand. These
are somewhat idealized curves, but they are an accurate signature
of a building's power consumption reaction, to changes in average
outside temperature. See also FIG. 2A, which illustrates the
area-normalized energy components of an end-use energy model versus
average-month temperature, and which identifies the major
contributors to energy consumption in a typical facility, e.g. a
building.
[0012] H. Reichmuth, P E, A Method for Deriving an Empirical Hourly
Base Load Shape from Utility Hourly Total Load Records, published
by the American Council for an Energy-Efficient Economy (ACEEE or
ACE.sup.3), (August, 2008), is also background to the present
invention. That article describes the use of non-heating/cooling
base load shapes to derive heating and cooling end-use load shapes,
the use of locus minimum load shapes to tame the data, and a way of
truing the demand to arrive at an empirical hourly base load shape.
The article addresses only the aggregate whole utility
(utility-wide) planning from the utility's point of view (POV). It
does not address specific site or facility energy tracking from the
POV of the facility's carbon footprint, improvement, and metering
of energy cost-avoidance.
[0013] A conventional way of viewing energy cost savings or credits
is expressed in the following familiar formula:
S=C.sub.H-C.sub.C.+-.Adj,
wherein S is the energy cost Savings in dollars, C.sub.H is the
Historic energy Cost (at current rates), C.sub.C is the Current
energy Cost, and Adj are adjustments, all units being currency
units such as US dollars. The problem with this formulation is that
most parties to an energy credit agreement agree in large part with
the formula and the cost factors but disagree strongly about the
adjustments that might be made under the contract. This is because
under the conventional approach, the adjustments are an arbitrary
attempt to link a purely statistical variation to one or more real
world changes. For example, did an increase in use come from
inefficiencies in the improvements or from additional use of office
equipment? Conventional statistical modeling introduces certain
error into such a seemingly simple calculation. These and other
uncertainties about, the calculation of energy credits remain
unaddressed and unresolved thereby undermining the value of the
energy efficiency,
[0014] In brief summary, the prior art fails to teach either
appropriate metrics (techniques) or meters (`metering instrument`)
for reliably, accurately, repeatably, and thus credibly predicting
and/or measuring energy savings in a way that can be applied cost
effectively in a routine business context. Moreover, the prior art
fails to teach integrated systems and methods that reliably,
accurately, repeatably, and thus credibly, account for energy cost
avoidance as a systemic solution in a way that can be applied cost
effectively in a routine business context.
BRIEF DESCRIPTION OF THE DRAWINGS
[0015] FIG. 1 is a graph illustrating the tracking of a facility's
historical energy consumption predicted for the current conditions
versus its actual metered energy consumption over the same time
interval.
[0016] FIG. 2 is a graph illustrating a building's `signature`
reaction to temperature as being characterized by two distinct
electricity and gas consumption curves versus average outdoor
temperature (T).
[0017] FIG. 2A is a patterned graph illustrating the normalized,
model end-use energy components versus mean- or average-month
temperature, wherein each color represents a different component
within and around a facility such as a building.
[0018] FIG. 3 is a system block diagram illustrating one embodiment
of the invented efficiency-generator system.
[0019] FIG. 4 is a system block and process flow diagram
illustrating another aspect of the invention in terms of building
science-based energy-efficiency generation systems and monetization
thereof.
[0020] FIG. 5 is a flow diagram depicting an embodiment of a method
for quantifying energy redirected from an energy-consuming facility
to a metered utility grid,
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
[0021] Embodiments of the invention adhere to the principles of the
International Performance Measurement and Verification Protocol
(IPMVP) while incorporating three significant enhancements to the
IPMVP-adherent process, which makes it much more robust. Two such
enhancements are the result of the use of the analog building model
(also referred to herein as an `analog facility mode`, or ABM),
which is a combined physical/statistical model based on measured
energy use and coincident temperature data: (1) the ABM provides a
singular fit to the data that is constrained by physical laws as
well as mathematics and is therefore uniquely representative of the
actual building, and (2) the process allows the disaggregation of
the energy use into component end-uses (heating, cooling, etc.)
that aids in both initial diagnoses and in adjusting for
non-routine changes in the building over time. A third enhancement
is the incorporation of statistical uncertainty in the estimation
of savings which provides quality control of the required data,
[0022] The term `non-routine changes` herein refers to changes to
the design, materials, configuration, structure, energy-consuming
appliances, etc, within a facility that have the potential to lead
to a lasting shift in energy load patterns. These can include
installing more energy efficient appliances, insulating walls,
installing solar panels to produce power, and other lasting, energy
load-altering changes. Ordinary and expected changes such as
building occupancy, seasonal changes in energy use or the types of
energy used, ordinary variations in energy consumption due to daily
human activities, etc. are generally considered `routine
changes.`
[0023] These enhancements provide two important benefits; (1) a
clear process for handling common changes that occur in buildings
and which often create challenges for more typical measurement and
verification (M&V) approaches, and (2) greater accuracy and
certainty in contracted savings estimates. With these enhancements,
the invention not only meets the requirements for adherence to
IPMVP, but it is even more rigorous, includes more features, and is
a clearer, more prescriptive process than has been implemented
previously.
[0024] The enhanced process embodiments are sufficiently robust to
meet the requirements to be part of a utility provider's energy
supply portfolio, and to provide building operators an improved
basis for performance. Indeed, we refer to the output from this
enhanced M&V process as a virtual meter, and refer to the
savings calculated by the virtual meter as efficiency generation
(EG).
[0025] Key features of the invented embodiments include but are not
limited to: (1) providing more rigorous M&V than required, or
conventionally applied, for adherence to IPMVP; (2) including a
combined physical/mathematical model (the ABM), calibrated to
actual utility bills, to ground the results from the regression
model in reality, and to prepare for analysis of any future
non-routine adjustments; (3) accounting for routine adjustments,
following best practice modeling procedures, including provision of
the statistical properties of the regression models, such as
uncertainty, and (4) quantifying the required amount of data
necessary to obtain a robust baseline model of the historical
(pre-project) conditions, based on desired confidence level and
anticipated savings.
[0026] From a programmatic standpoint one or more embodiments
provide several additional benefits. Because the resulting model is
tied to a real building, the results are less subjective and highly
replicable. Also, the process is automated, reducing analysis time
and costs substantially.
[0027] The present invention is useful in energy savings
measurement, diagnosis, adjustment, audit-trail, and credit or
monetization by virtue of an increase of accuracy and ease of use
in modeling future baseline use based upon historic use data and
measuring, recording, reporting actual energy-use savings and
linking both to actual building characteristics. While the invented
embodiments are IPMVP Option C adherent, they also include several
enhancements, as will be recognized by an ordinarily skilled
artisan based on the description provided herein. Unlike prior art
methods, one or more of the invented embodiments comprehend the
energy use impact of non-routine changes to a facility, including
energy-conserving improvements. The invention thus "levels the
playing field" among various developers, contractors, project
designers, suppliers, engineers, lenders, fossil-based (carbon)
suppliers and distributors, and credit-worthy brokers, e.g. energy
service companies (ESCOs), thereamong.
[0028] The energy streams into a building, for example, are
understood to be characterized by a carbon footprint that is
increasingly well understood and measurable because they directly
or indirectly are tied to dwindling fossil fuel sources. The carbon
emissions from one or more buildings, plants (e.g., factory), or
campuses, or any combination thereof (referred to herein generally
and intentionally broadly as a "facility," whether individually or
collectively) can be derived therefrom. Therefore, embodiments of
the invention comprehend application to buildings whether
individually, or plurally but aggregated as an actual or conceptual
facility. Thus, efficiency gains from designing-to-conserve or from
renovation of existing facilities now can be better quantified and
can better answer the important question of additionality.
[0029] Energy streams into a building include direct fuel types
(natural gas) and indirect fuel types (coal, gas, oil mix in the
electricity generation, chilled water, hot water or steam flow), so
when one is more accurately measuring such inflows, one is more
accurately measuring carbon footprint. The savings to the more
accurately measured carbon footprint flow from more accurately
reduced carbon emissions, and assist the historically problematic
additionality determination. These savings also produce worthy
credits or other monetized rewards like earned income or even
well-deserved profits to those involved in creating the energy cost
avoidance.
[0030] The ability to accurately predict future or present energy
savings or cost avoidance is a result of the invented system's
greater ability to distinguish between changes in consumption
caused by use increases or reductions associated with a building's
primary purpose (which generally do not represent additionality)
and changes in consumption caused by better building and energy
systems design (which generally do represent additionality). So, a
building that is subject to such modeling and measurement as
provided by the present invention can potentially qualify for
building carbon credits or offsets that would be heretofore
unrealizable. Further, the invented system provides sufficient
audit and records transparency that a new standard will emerge that
can meet domestic and international norms for additionality and
market transactability of energy conservation and monetizable cost
avoidance,
[0031] Referring now to FIG. 3, invented energy efficiency
generator system 10 in one embodiment will be described. "Energy
efficiency generator" refers to the invented mechanism for
realizing energy cost avoidance (a new energy form, or at least a
new way of defining energy) "supplied" by the system to the earth's
energy grid. It may be thought of as a precision instrument
providing quantification of such energy cost avoidance. Some in
this field refer to the unit power and energy savings as
"Negawatts" or "Negawatt-hours," respectively. A typical embodiment
of the invented system 10 includes one or more variables or factors
regression modelers 100, a scale and aggregate mechanism 102, a
utility grade "smart" meter 104, an energy conservation calculator
106, and a data sets/forms/reports (reporting) mechanism 108.
Modeler 100 will be understood to comprehend one or more relevant
variables including plug loads, weather, site, surrounds,
occupancy, space usage, hours of operation, and building
science.
[0032] It is noted that in FIG. 3, building science is indicated by
a solid line, while the remaining factors are indicated by dashed
lines. This is to highlight the important contribution of building
science to the invented system.
[0033] Scale and aggregate mechanism 102 can take the form of a
simple effect-multiplier that takes into account multiple modeling
inputs from one or more subsystems or buildings. The scale and
aggregate mechanism 102, or `data aggregator,` is generally coupled
with the calculator 106, and additionally with either or both of
the modeler 100 and the "smart" meter 104. In a typical embodiment,
the data aggregator includes device-readable instructions
configured when executed by data processing circuitry, to cause the
data aggregator to aggregate a time-based facility energy load data
from two or more buildings. The aggregated data are then considered
to represent a single facility that encompasses the two or more
buildings.
[0034] Meter 104 is preferably a smart meter that precisely
measures facility energy loads in real time and supplies precise
load data to calculator 106. More broadly, however, a `suitable`
meter 104 can be nearly any known energy meter apparatus configured
to measure a load (e.g., usage, flow, consumption, demand) on an
energy source (e.g., electricity, gas, etc.) utilized by one or
more sub-systems and/or end uses of a facility (e.g., building,
grouping of buildings, etc.).
[0035] Calculator (or comparator, or differencer) 106 effectively
determines the real-time or run-time difference or delta (.DELTA.)
between two time-based inputs.
[0036] Data sets reporting mechanism 108 can output data received
from calculator 106, whether the data are raw, processed,
tabulated, graphed, or in other forms useful to a user. Users might
include, for example, an energy service company (ESCO) that is
attempting to monetize energy conservation measures (ECMs) perhaps
under an energy savings performance contract (ESPC). Those of skill
in the art will appreciate that an embodiment of a data sets
reporting mechanism 108 can produce semi- or fully-automated
billings, e.g. mechanism 108 can directly generate art invoice
representing an earned energy credit or offset. Such report in
accordance with the invention can take any-suitable form, e.g.
hardcopy or electronic.
[0037] Additional embodiments of the above described modeler 100,
scale and aggregate mechanism 102, meter 104, calculator 106, and
data sets reporting mechanism 108 are discussed further with
respect to FIG. 4, and are likewise contemplated as being within
the scope of those features according to the embodiment depicted in
FIG. 3.
[0038] Data sets 108 can also be manually or automatically reviewed
by design personnel at design-to-conserve functional block 110, the
output of which can be re-modeled or originally modeled by
regression modeler 100 in what will be referred to herein as a
closed-loop design and meter system 100. Thus, original facility
design can be performed at block 110 with conservation in mind, and
the result of such best design practices can be monitored for its
effectiveness in accordance with the invention. Alternatively,
modified facility design (design modifications) can be performed at
block 110 to realize greater energy cost avoidance.
[0039] Smart meter 104 can also include a controller portion that
effectively controls a building's heating and cooling subsystems,
for example, in real-time response to performance measurements. For
example, it can use a proportional-integral-derivative (PID)
approach to data analysis, thus to account not only for first-order
power demand but also for second- and third-order demand. Thus,
power usage, rate of power usage (first derivative), and even rate
of change of power usage (second derivative) can be monitored for
more effective and responsive control of a facility's heating,
cooling, and other energy-consuming subsystems.
[0040] Conventional statistical regression cannot be easily applied
to models expressed in physical building parameters rather than
statistical coefficients. Therefore, those of skill in the art will
appreciate the important inclusion (at the possible exclusion of
other variables) of temperature plus building (design, engineering,
construction, building materials, etc.) science-based mathematical
reaction approach that avoids conventional statistical regression
and that uses software-implemented mathematical algorithms
including a novel steepest-descent solution-convergence technique
that improves modeling by allowing the ascribing of physical
attributes to the data.
[0041] For any given set of time-based (e.g., periodic) usage data,
the resulting whole building model is termed here an equivalent
"Analog Building Model" (ABM). In a real sense the ABM is an
inverse model: it is a model of a hypothetical very simple building
that produces the same pattern of periodic (e.g., monthly) energy
bills as the real building. Hie approach to building science-based
modeling is described at some length below.
[0042] In general, the larger the building or group of buildings,
the more accurate the signature. The functional relationships shown
in FIG. 1 are based on average monthly temperatures and are
sufficient to quantify the monthly performance of the building or
group of buildings on an average monthly basis. This level of
quantification has been determined in accordance with the invention
to be sufficient to support, contractual relationships that deal in
monthly energy savings without regard for the precise timing of the
savings.
[0043] In practice, the value of the savings is often very time
dependent, such as savings on a hot summer afternoon are more
valuable to the utility that must purchase costly extra power at
this time. Therefore, most energy contracts are structured on a
time of day basis with energy valuation differentiated into hourly
categories (on-peak, off-peak, monthly maximum peak, etc). In order
to align with the common contractual basis for valuing energy and
savings, the estimates of average daily energy use or savings found
in FIG. 2 must be developed further by extending the estimates of
average daily energy use to estimates of the associated average
hourly energy use. This is accomplished by using the same hourly
load measurements as were aggregated to produce the average daily
or monthly energy usage functions as are portrayed in FIG. 2.
[0044] The first step is to disaggregate the energy signature shown
in FIG. 2 into its constituent end-uses. The energy signature
should be seen as the sum of several fundamental end-uses as
illustrated in FIG. 2A.
[0045] The fundamental end-uses are an inherent result of the
physical parameters that have been fitted to and derived from the
monthly utility billing data (representing the whole building
energy usage data including all fuels) and the associated average
monthly temperatures. The need to develop models of the constituent
energy end-uses is one of the most significant reasons to fit a
physical model to the monthly utility data instead of a simple
statistical model. The prior art typically fits simple statistical
models separately to the electric energy and the fuel (gas) energy.
In the prior art, these separately derived models make no use of
the energy balance of the binding or facility, which is the
essential information that enables the accurate determination of
the constituent end-uses. Good engineering practice requires that
building models refer to the energy balance of a building in order
to estimate accurately the interactions between energy uses. For
example, the heat generated by electric lighting can reduce the
amount of gas heat necessary to heat a space, or electric lighting
and computer loads become also a cooling load.
[0046] By contrast, this invention fits a simple physical model of
the building or group of buildings to both the electric and gas
billing data simultaneously, thereby using the analytical advantage
provided by reference to the building energy balance. In this
invention, the physical model of the building is the sum of the
several constituent energy end-use models so that a function of
each constituent energy end-use versus average monthly temperature
is an inherent outcome of the model.
[0047] For each of the constituent energy end-uses, the hourly load
measurements are used to develop "hourly load factors," where an
hourly load factor for a particular hour is a fraction less than 1
that describes the portion of the average daily energy for that
constituent end-use that is used in that particular hour.
Naturally, the load factors for all 24 hours of the day will sum to
1. There will be a set of 24 hourly load factors for each
constituent, end-use. For some constituent end-uses it may be
necessary to develop a different set of hourly load factors for
each month. The constituent end-uses align broadly to the energy
use categories of heating, cooling, base-load, domestic hot water
(DHW), and any known or metered end-use.
[0048] The use of such broad categories in this invention is a
major savings in time and overhead cost that would otherwise be
employed in compiling details that ultimately have little bearing
on the final result. These very broad categories in fact aggregate
many more detailed and significant end-use distinctions such as
lighting, plugs, fans etc. Often the use of such broad categories,
without more detailed distinctions, is a source of error. Errors
due to this broad characterization are minimized in accordance with
the present invention by using the same hourly load data to derive
both the aggregate physical models as illustrated in FIG. 2 and the
hourly load factors for the constituent end-uses. The errors due to
broad characterization are also minimized in this invention by
using exactly the same constituent end-use categories in the hourly
load factor derivation as were used in the physical model fitted to
the data.
[0049] The buildings and groups of buildings most suited to this
invention will have an orderly temperature dependency as shown in
FIG. 2. This is essentially a seasonal dependency with maximum
energy use in the summer for cooling and in the winter for heating.
The mid seasons approximately April and September will have the
least heating and cooling and some days may have essentially no
heating or cooling other than the minimal auxiliary energy use
necessary to maintain the heating and cooling systems in a standby
state, which is considered here to be a portion of the constituent
end-use energy designated as base-load energy. The hourly load
factors for the base-load are developed with reference to the
hourly load measurements for the months of March-April and
September-October.
[0050] A separate set of hourly load factors may need be developed
for different day types (occupied, unoccupied, etc.), but in most
cases that is unnecessary. The hourly load measurements for the
particular day type are sorted by hour of day and the minimum load
for each of the 24 hours is established. These 24 hours of monthly
minimum load constitute a 24 hour load shape that is the locus of
minimum load, and this is taken as the load shape of the base-load.
This minimum load shape may contain sub-portions with a well known
or measured load shape, such as for exterior lighting, which can be
a separately designated constituent end-use. In this manner, the
present invention uses well-known information when it is available.
The hourly load factors for the well-known load are not developed,
because these are already known. The load factors are then
developed from the difference between the locus of minimum load and
the well known loads that are part of it. The hourly load factors
per the definition are that fraction of the daily load that occurs
in each separate hour.
[0051] The hourly load factors for the heating and cooling
constituent end-uses are then developed with reference to the
hourly load measurements for the heating and cooling seasons. For
both heating and cooling, the peak load day for each heating or
cooling month is identified, and the load shape from this peak day
becomes the load shape from which the heating and cooling hourly
load factors for each heating or cooling month are derived. The
peak day load shape, rather than an average day load shape, is used
as a reference because it has been found to be the clearest
expression of the human and control behavior that drives that load.
The hourly load factors for the constituent heating and cooling
energy end-uses are then derived from the shape of the hourly load
defined by the difference between the total peak day load for
heating or cooling days and the base-load, which has been
previously calculated, and any other known loads.
[0052] The above discussion of the importance of building
science-based modeling does not undermine the importance of FIG.
3's inclusion of multiple factors into modeler 100. The other
factors that are described and illustrated herein will be
appreciated by those of skill in the art to represent second-tier
factors of secondary importance that can be used to overlay the
building-science data. In other words, base-lining should first be
building science-based and can then be adjusted as desired or
needed in the margin with the other factors.
[0053] The analog building modeler 100 is defined herein as an
apparatus that is configured to produce an `analog building mode`
(also referred to herein as variables or factors regression model,
an equivalent analog model, or an analog facility model) according
to the end-use equations described herein.
[0054] The modeler 100 calculates the metered total building energy
use as the sura of the primary building energy end-uses. A
relatively small set of parameters has proven to be comprehensive
enough to support a reasonable energy balance and yet sufficiently
independent to allow a defensible regression solution. The modeler
uses a set of just eight key parameters--internal and external
gain, aggregate normalized UA, heating and cooling efficiency,
service water heating, heat intercept and cool intercept--to
produce robust and repeatable energy signatures in gas-heated
office buildings. These few parameters/variables can then be worked
into estimates of the building energy end-uses.
[0055] The modeler operates with the monthly average temperature as
the primary independent variable. At this monthly level of energy
aggregation, the short term thermal transients are averaged out,
leaving the seasonal temperature changes as the primary driver. At
this high level of aggregation, an end-use building model, that can
reasonably fit the observed monthly data, becomes algebraically
quite simple. The variables used in this model are listed and
discussed in Table 1 and in the accompanying discussion.
TABLE-US-00001 TABLE 1 Equivalent Analog Building Parameters
Parameter, symbol Units Notes Normalized Aggregate UA, UA.sub.n
BTU/deghr/ft.sup.2 Solved Internal Gain, Q.sub.in W/ft.sup.2 Solved
External Energy, Q.sub.ext W/ft.sup.2 Fixed percentage of the
internal gain Normalized SWH, SWH Gal/day/ft2 Solved Heat
Intercept, H.sub.t T deg F. Solved Cool intercept, C.sub.t T deg F.
Solved Heating Efficiency, E.sub.h No units Assumed to be .75
Cooling Efficiency, COP No units Solved *Normalized to the floor
area of conditioned space in the subject building.
[0056] The parameters in Table 1 have been found to be sufficient
to support a general end-use energy model of a commercial building.
Note in Table 1 that two of the parameters are assumed while the
other six parameters are all solved from the data. Most of the
energy use estimated by this model, (the baseload, the heating and
cooling energy), is based on unique building parameters that are
derived by regression from the data.
[0057] The parameters of Table 1 all play a role in the building
energy model indirectly through their role in the calculation of
the energy end-uses. Therefore this discussion of these parameters
may refer to their role in the various energy end-use equations
that follow.
[0058] Aggregate UA, UA.sub.n--This is the aggregate heat loss
parameter of the building. It includes the building thermal,
losses/gains and the ventilation losses/gains that are also
temperature sensitive, all normalized per square foot of floor
area. This variable algebraically represents the aggregate
temperature dependent characteristic of the building including the
thermal losses and ventilation losses, but not including the
heating or cooling efficiency. Such an aggregate value would be
very difficult to calculate directly by combinations of individual
measurements. But in the context of the inverse model, the
aggregate effect of all these thermal and ventilation factors is
relatively straightforward to determine, and is useful in
characterizing results. In general this temperature slope is
slightly different from the visible slope in the building energy
signature. In the end-use equations, it plays a role in estimating
both the heating and cooling end-uses.
[0059] Internal Gain, Q.sub.in, is the portion of the baseload that
plays a role in the net load used to calculate the heating and
cooling end-uses. In this model part of the baseload is an external
gain that plays no role in the heating and cooling loads.
[0060] External Energy, Q.sub.ext, is assumed to be a small fixed
percentage of the Internal Gain; typically approximately 5%, but
can vary from building to building. The main component of External
Energy is typically, but not exclusively, outdoor lighting and
signage. Notable exceptions include large data centers essentially
located external to a building, large parking structures, etc.
[0061] Service Water Heating, SWH, is the average of the
non-space-heat gas energy use that occurs in July-September
expressed in units of heated gallons of water/day, which allows the
scrutiny of this variable relative to a plausible hot water use for
a building of that type. If there is no summer gas use and
therefore no gas SWH, then electric SWH is assumed to be 0.002
gallons/day/ft.sup.2. Estimates of the SWH end-use are based on a
seasonally varying inlet water temperature and thus have a slight
seasonal variation. There are many cases where this parameter is
much larger than is plausible for hot water heating alone because
it contains other significant summer gas usage such as for
distribution loops or reheat.
[0062] Heat Intercept, H.sub.f, is the highest temperature at which
heating is observed, and it is assumed in the end-use equations
that the heat load will linearly increase at temperatures below
this. While this temperature is influenced by the interior set
temperature, it also is affected by the internal gain and the
control errors. In practice this temperature will be lower than the
interior temperature when the internal gain is contributing to the
heating, but many cases have been observed where this temperature
is higher than the interior temperature. These suggest excess
heating or re-heat. This heating intercept temperature is a strong
indicator of potential control errors.
[0063] Cooling Intercept, C.sub.t, is the lowest temperature at
which cooling is observed, and it is assumed in the end-use
equations that the cooling load will linearly increase at
temperatures above this. While this temperature is influenced, by
the interior set tempera lure, it also is affected by the internal
gain and the control errors, in practice this temperature will be
lower than the interior temperature when the internal gain is
contributing to the cooling load. This variable essentially
partitions the electric energy between internal gain and cooling,
and in mild cooling dominated climates (such as Southern
California), there is no visible balance point temperature on the
energy signature as to at what temperature the cooling begins. The
cooling begins at the lowest observed temperature and increases
linearly above that temperature. In these ambiguous cases, the
cooling intercept is not well defined, and the model may
incorrectly solve for an unreasonably low cooling intercept, which
leads to an exaggerated cooling load and an unreasonably low
internal gain. Therefore, this variable is constrained to be no
lower than one degree Fahrenheit (approximately 0.6.degree. C.)
less than the minimum input temperature. This constraint
essentially allows the reasonable maximum internal gain to be
applied to the ambiguous cases.
[0064] Heating Efficiency, E.sub.b, is the assumed heating
efficiency. In principal it could be a solved instead of an assumed
variable. However it is close to co-linear with the temperature
sensitive Aggregate UA, and it lends some instability to the
overall regression. While heating efficiency may vary from building
to building, it will typically be in the range of 70-85%. In this
work, the heating efficiency is assumed to be 75%. A few rare cases
have been observed where the actual efficiency was considerably
less than the assumed, and these cases were revealed in an
unusually high aggregate UA and cooling efficiency COP.
[0065] Coating Efficiency, COP, is the apparent coefficient of
performance (COP) of cooling energy; i.e., the ratio of the thermal
energy content of the cooled air relative to the electrical energy
used to cool the air, as expressed in the same units of
measurement. It assumes that the Aggregate UA for cooling is the
same as the Aggregate UA used for heating. In practice this may not
be the case as there may be more ventilation during a cooling
season or some other thermal characteristic may change seasonally.
The cooling COP includes the actual COP of the cooling, and it
includes the effects of seasonal changes in the thermal
characteristics.
[0066] The full analog building model comprises the sum of several
energy end-use models which specify the energy for the specific
end-uses as a function of average monthly temperature. Each
equation or set of equations below for deriving and/or attributing
a portion of the total monthly building energy usage to a specific
end-use is defined herein as an `end-use model equation`. A result
derived from executing an end-use model equation or set of
equations for a particular end-use is defined herein as an `end-use
model`. For example, an end-use model derived from the Internal
Gain end-use model equation is defined herein as an `Internal Gain
model,` an end-use model derived from a `Space Heat` gas end-use
modeler is defined herein as a `Space Heat model,` and so on for
each equation or set of equations for each end-use described
below.
[0067] The whole system of end-use equations consists of functions
of the average monthly temperature and the model parameters in
Table 1, and reduces in essence to a simultaneous equation in six
unknowns. This system of equations is not readily solved by
conventional linear regression, and is instead solved by means of
an iterative steepest-descent convergence algorithm.
[0068] This type of mathematical approach rests on the assumption
that there is a unique combination of real building model
parameters that leads to a best fit to the data, and that the
solution will not converge on a false set of parameters that also
may lead to a good fit. Structuring the convergence path is always
important in this type of mathematics, and it is usually done by
carefully tailoring the initial conditions, and by controlling the
iteration steps. The current building model has proved to be stable
and repeatable for buildings with electric and gas energy.
[0069] The solution for the five analog building parameters is
essentially a problem in five unknowns with about 12-24 items of
information. Ideally this might be done algebraically, but it would
be very tedious and it would be difficult to change as the process
might be adapted to new situations.
[0070] A method that can avoid the algebraic complexity is referred
to here as the method of steepest descent. This approach has been
described in mathematical textbooks and is beyond the scope of this
discussion to repeat in detail. A particular application is
referred to as the Fletcher-Powell method. Generally this is an
iterative method commonly used in complex problems. It starts by
establishing a goodness of fit Indicator, such as a CHI square or R
square (here called the `fit indicator`), that is proportional to
the difference between the energy use data and estimates of the
same data as derived from the analog building model.
[0071] Briefly the process starts with an assumed solution for all
the unknown variables, and iteratively changes the values of all
the unknowns at once by a small amount, always seeking changes that
lead to a closer fit between the data and the model. The small
changes of the unknowns are not at random, but for each of the
iterations, each variable is changed in such a way as to lead to a
slightly better fit.
[0072] The small incremental changes to each variable are derived
by evaluating the model at two conditions: one with the current
value of the variable and the second with the variable changed by a
small fixed amount called the "fixed change in variable". These two
evaluations of the model will produce two slightly different fit
indicators. If the fit indicator increases, it indicates that the
model fits the data better after the variable is changed. If the
fit indicator decreases, it indicates that the fit is worse after
the variable change. The underlying question for changing each
variable is, "will this slightly changed variable make the fit
better or worse?"
[0073] For each iteration, each variable is changed by an amount
proportional to what is referred to here as the gradient for that
variable, and specified by:
(Fit indicator 1-fit indicator 2)/(frxed change in variable), where
the fixed change in variable is a different fixed number for each
different variable.
[0074] For the analysis of building energy data, it has been found
that the introduced change for each variable should typically be
approximately 1/1000 of the initial value of the variable. In
general, the gradient for each variable will change with tire value
of that variable and the other variables with each iteration. Thus
a new gradient must be calculated for each unknown variable for
each iteration.
[0075] The gradient for each variable is only proportional to the
whole of the change to be applied to each variable. The full
incremental change to be applied to each unknown variable will be
based on the gradient multiplied by an activity factor. The
activity factor in this analysis is generally a different constant
number for each variable, and unlike the gradient, the activity
factor does not change with each iteration.
[0076] Methods such as these require that the assumed starting
point be reasonably close to the final solution, and that the
iterative changes to the variables bear certain relationships to
one another, in any particular problem, the success of the method
depends on making good choices as to the starting point and the
relative rates at which the variables are changed. If these choices
are not good enough, then no satisfactory solution for the unknown
variables will be found because the variables will ail change in an
uncoordinated way with each iteration, such that the difference
between the data and the model will never be reduced, and in fact
may increase substantially. This is referred to as a "failure to
converge."
[0077] Getting the process to converge is the focal challenge in
this type of mathematics. A good physical and mathematical
understanding of a particular class of problems is required to make
the method work for that class of problem. In this case the process
is informed by the analysis of energy data from many hundreds of
buildings, and this information has been used to devise a process
that will reliably work with building energy data as discussed
below.
[0078] The particular problem of interest here pertains to creating
a model of a building's energy use. This is a particularly complex
problem because the energy use data for a building can be strongly
influenced, by erratic occupancy behavior and non-constant
malfunctioning controls, which may not be describable by any sort
of model. In addition, even if the occupants and machinery are
performing correctly and regularly, the model of the building
energy use is at best a highly simplified approximation of a much
more complex real situation. In short, there is a significant and
unavoidable amount of error between any sort of building model and
the data, and this difference can interfere with or alter the
convergence of any data analysis or regression process, including
this steepest descent method.
[0079] In spite of these significant sources of error, the energy
use data for the majority of buildings shows very evident patterns.
Therefore, for this particular class of problem, the selection of
the initial conditions and the variable's incremental changes have
been tailored to the physical nature of building energy use and the
errors associated with it.
[0080] There is literally an infinite variety of combinations of
initial variable conditions and activity factors, and many will
lead to good results, though some combinations will need more
iterations to converge successfully, and some combinations may not
converge.
[0081] Among the combinations that can converge successfully, the
value of a final solved variable will differ somewhat depending on
the initial conditions, the activity factors, and even, the size of
the fixed variable change. The final values will be close
regardless of the path leading to them, however, because this
process is applied in the context of a "utility conservation
meter," it is important to be specific as to the initial conditions
and activity factors so that the calculation is rigorously
repeatable.
[0082] Experience establishes that the values of the initial
conditions and activity factors set forth in Table 2 will lead to a
solution of the unknown variables.
TABLE-US-00002 TABLE 2 Initial Values and Activity Factors Variable
Initial Value Activity Factor Normalized Aggregate UA .1 BTU/deg F.
hr/ft.sup.2 1/500 Intemal Gain .2 W/ft2 1/50 Heat Intercept 80 deg
F. 30 Cool Intercept 55 deg F. 60 Cooling Efficiency, COP 2.2 1
[0083] Note the wide range in the activity factors. These have been
chosen based on the application of this method to hundreds of
buildings to lead to a satisfactory solution within 1000
iterations. The values of the unknown variables for the iteration
with the highest fit indicator within the first thousand iterations
are considered the solved values.
[0084] Typically a good solution will be evident after the first
hundred or so iterations, and the fit indicator and the variables
will remain almost constant for all later iterations. As an
ordinarily skilled artisan will recognize, the large number of
iterations and calculations especially lends itself to employment
of the computer-based methods and system described according to one
or more of the invented embodiments.
[0085] There are three common ways that this method can fail if not
properly set up:
(1) the incremental changes in a variable may be too large, and the
variable will overshoot, and then overcorrect itself in the next
iteration (in some cases this leads to a trap for the regression as
it oscillates between over and under shooting, for an indefinite
number of iterations); (2) the incremental changes for a variable
may become very small so that it may take large numbers (e.g.,
thousands) of iterations to come to equilibrium; and (3) an
overshooting variable may cause other related variables to over or
undershoot and the process may require large numbers of iterations
for the errors to die out, or it may lead to an instability.
[0086] The conventional mathematical approach would have the
activity factors all relate to one another as if they were the
orthogonal components of a steepest descent vector, hopefully
toward a final equilibrium. But this problem does not use a smooth
mathematical function; there are break points in the function at
the heating and cooling intercepts, and errors may make it even
more irregular so that a conventional steepest descent approach can
easily be derailed. The approach leading to the invented
embodiments started as an attempt at a steepest descent, but then
evolved into a layered approach in order to deal with the
peculiarities of whole building energy data.
[0087] The invented approach utilizes the fact that, in an energy
versus temperature space, the energy use in a building takes a
common pattern for almost all buildings as in FIG. 2A. As presented
in FIG. 2A, the differences between almost all commercial buildings
are a matter of degree in the size and positioning of the several
end use curves. This regularity of end use relationships is a very
significant benefit to the invented process, and allows the fitting
process to be controlled so that progress to final equilibrium does
not involve a significant amount of de-stabilizing overshooting and
undershooting.
[0088] In the fitting process, the UA and Internal gain are
dominant variables, i.e., most able to influence the overall, fit.
But there are important differences. The UA variable is closely
linked geometrically to the heating and cooling intercepts and to
the COP. So when UA overshoots, three other variables also change,
resulting in a significant change of equilibrium. Our experience
indicates that the most stable results are achieved when UA slowly
approaches its equilibrium value from below. Accordingly, the
iterative `speed` of this variable is retarded by a very low
activity factor of 1/500, and a starting position that represents a
very low value for this variable. In this way, the UA variable
approaches the equilibrium almost exponentially with a minimum of
overshoot. In essence, this variable sets the stage and the other
variables dance upon it.
[0089] The Internal gain variable exhibits some independence; not
so directly linked to the other variables. In the regression
process, its initial value is a typical value for the class of
buildings, and it has an activity factor of 1/50. This is a strong
variable, but its overshoot is stable and muted.
[0090] The other variables, heat intercept, cool intercept, and
COP, have relatively high activity factors in the range of 1 to 60.
In a typical regression, these variables change in the later
iterations after the dominant variables, UA and internal gain, have
diminished most of the difference between the model, and the data.
Due to these variables relatively higher activity factors, they
continue to change fast enough in the later iterations near the
equilibrium, where all other variables change very slowly if at
all. The initial value for COP is 2.2, a typical value.
[0091] The initial values of heat intercept and cool intercept are
at the extremes of their ranges, with a starting heat intercept at
80 deg F, and a starting cool intercept at 50 or 55 deg F. As the
iterations proceed, these variables change in a manner to follow
the changes in UA in a simple coherent motion without much
overshoot.
[0092] This fitting process transforms a general image to fit the
particular data. The transformation proceeds gracefully, until
arriving at a final signature of the actual whole building energy
usage data including all fuels as fitted. The first few iterations
show major movement, led by changes to UA and internal gain. The
refinements involving the heating and cooling intercepts usually
occur in the later iterations. As the regression proceeds, the
coordinated movement of the variables is smooth and orderly,
proceeding somewhat directly to the equilibrium. The Infernal gain
and COP variables will overshoot and undershoot a bit, but this is
a small ripple on the dominant theme of the process. The best fit
is achieved after about 165 iterations in a typical but not
exclusive embodiment.
[0093] The invented embodiments comprehend three important
concepts:
1) the use of the temperature versus energy analytical space in
which to express the end use functions, which shows the energy
signatures and also provides a good way to review the overall
energy use; 2) the use of a simple analog building model consisting
of the particular eight variables that are used, which may appear
to be an over-simplified aggregate large building model, but which
is able to fit real world monthly energy data with all its common
irregularities; and 3) the use of an analytical process that is
stable when used with the particular modeling variables.
[0094] The end-use models depict simple linear descriptions of the
end-uses, but the underlying situation is more complex because some
end-use may bear an engineering relationship to other end-uses and
to other fuels. For example, the gas heating depends upon the
electric internal gain as does the electric cooling. The heating
and cooling also depend on the building UA.sub.n which is usually
revealed in the temperature slope of the gas heating. The building
parameters are thus constrained to one another by the engineering
relationships that prevail in the aggregate energy balance of a
building.
[0095] Inside the analog building model, the building energy
end-uses are expressed in units of kWh/day for the whole building.
The end-use modeler equations (or simply `end use equations`) are
defined herein as being the equations for each end use presented
below. The end-use equations are all either constants or simple
linear functions of the mean monthly temperature, t, and use the
gross building area, FT.sup.2.
[0096] Service Water Heating (SWH) electric and gas end-use factors
are defined herein by and determined according to the following
equations:
SWH electric, kWh/day=(SWH Setpoint temperature-Inlet Water
temperature)*SWH (gal)*FT.sup.2*8.33/3413/SWH elec eff,
wherein `SWH elec eff`=1. `Inlet water temperature` is derived from
an annual set of mean monthly outdoor temperatures,
SWH gas, kWh/day=(SWH Setpoint temperature-Inlet Water
temperature)*SWH (gal)*FT.sup.2*8.33/3413/SWH gas eff;
wherein `SWH gas eff`=0.65. The fuel type and amount of SWH is
determined by review of summer gas use. If average normalized gas
energy for July is greater than 0.001 W/ft.sup.2, then gas SWH is
assumed.
[0097] The Internal Electric Gain end-use factor is defined herein
by and determined according to the following equation:
Internal gain, kWh/day=Q.sub.in*FT*24/1000,
wherein FT.sup.2 is the area of building conditioned space.
[0098] The External Electric Gain end-use factor is defined herein
by and determined according to the following equation:
External electric, kWh/day=Internal Gain*0.05
[0099] The Space Heat Gas end-use factor is defined herein by and
determined according to the following equations:
First, gross space heat load, H gross, is calculated.
H gross=0 if t>H.sub.t, the heat intercept. Otherwise, for
t<H.sub.t, H gross,
kWh/day=((H.sub.t-t)*UA.sub.n*24-Q.sub.in*RG.sub.h(t)*24*3.413)*FT.sup.2/-
3413,
wherein FT.sup.2 is the area of building conditioned space.
Space heat gas=H gross/E.sub.h, where E.sub.h is assumed=0.75.
[0100] Calculation of Retained Gain: A key part of this calculation
is an estimate of the fraction of internal gain that is part of the
energy balance, RG.sub.h(t). Not all of the Internal gain is
retained in the energy balance because the gain often will not
occur when there is a heat need and it is lost through venting or
cooling. The portion of the internal gain that is retained in the
energy balance is referred to as the "retained gain." In general
the retained gain is dependent on the magnitude of the internal
gain, the timing of the occupancy, the mass of the building, and
the outdoor temperature. The amount of internal gain that finds its
way into the energy balance is quite building specific and
dependent on thermal transients and variable occupancy, i.e., it is
potentially complicated.
[0101] Extensive hourly simulations have shown that the aggregate
monthly retained gain can be represented by a function of
temperature that is unique to each building. However the common
features of the retained gain functions are that at low
temperatures all of the gain is used, and at moderate temperatures
almost none of the gain is used. This work assumes a standard
linear retained gain function with a value of 1 at <=40 deg F,
linearly diminishing to 0 at >80 deg F.
[0102] The Heating Retained gain function(t), RG.sub.h(t) is
defined herein by and is determined to:
[0103] =1, if temperature <=40 deg F;
[0104] =0, if temperature >=80 deg F; and
[0105] =(temperature-40)/(80-40), if 40<temperature <80 deg
F.
[0106] In principle, cooling should be similar to beating, but the
de-facto economizer/infiltration effects flush out and diminish the
retained gain at lower temperatures. The retained gain function
assumed for cooling, or `Cooling Gain Function(t)`, RG.sub.c(t) is
defined herein by and determined to:
[0107] =1, if temperature >80 deg F;
[0108] =0, if temperature <C.sub.t the cooling intercept;
and
[0109] =(temperature-C.sub.t)/(80-C.sub.t) for
C.sub.t<temperature <80
[0110] Space Heat Electric: In buildings predominantly heated by
gas, space heat electric refers to the electric auxiliaries (fans,
pumps, etc) associated with the distribution of the space heat. The
space heat electric end-use factor is defined herein by and
determined according to the following formula:
Space heat electric, kWh/day=H.sub.gross/COP.sub.aux,
wherein COP.sub.aux is the aggregate COP of the electric
auxiliaries, assumed here to be 10.
[0111] Cooling Electric: The Cooling electric, kWh/day is defined
herein by and determined to:
=0, if t<C.sub.t, the cooling intercept, and
=((t-C.sub.t)*24*UA.sub.n+Q.sub.in*RG.sub.c(t)*24*3.413)*FT.sup.2/3413/C-
OP, for t>C.sub.t.
[0112] Those of skill in the art will appreciate that many unique
features of the invention emerge from an understanding of FIG. 3.
First, real-time metering controller or "smart" controller 104 can
make use of PID-based, (formulae-based) inputs to make minute
corrections in real time to heating and cooling subsystems, for
example, such minute corrections generally being less costly than
under-corrections followed by over-corrections. In other words,
controller 104 can be computer-controlled or -assisted and can
operate as a special-purpose machine that executes software
instructions stored in memory to serve the important controller as
well as meter function for a building that features the energy cost
avoidance measures described herein.
[0113] Second, it will be appreciated that energy conservation
calculator (or comparator or so-called "differencer") 106 can
comprehend one or more of multiple factors from modeler 100 and one
or more energy-consuming subsystems from scale-and-aggregate block
102 and a myriad of utility grade metered inputs from "smart" meter
104. Those of skill will appreciate that calculator 108 can also be
computer-controlled or -assisted. Those of skill in the art also
will appreciate that scale and aggregate block 102 captures as wide
as reasonable and possible a data set for inclusion in a given ECM
calculation to attract, big utilities to join the energy-saving
venture or to attract big capital or to attract
legislation/regulation/incentives to attract lending or other
co-development efforts or joint ventures. Such aggregation can be a
simple aggregation of heating, cooling, and other
energy-consumption subsystems within a building; or it can be an
aggregation of the same across multiple facilities, e.g. buildings,
within, a block or campus.
[0114] Third, data sets/forms reporting mechanism 108 can output
hardcopy or electronic reports useful to any party wishing to
monitor the results of such energy conservation measurements, e.g.
an ESCO or an ESPC may require a report rendered in a particularly
useful form to the one or more users of the information. Mechanism
108 also can automate billing to lower reporting/billing costs and
to increase reporting/billing efficiencies. Those of skill will
appreciate that mechanism 108 can also be computer-controlled, or
-assisted.
[0115] Fourth, those of skill in the art will appreciate that
original building design-to-conserve can commence at block 110, be
modeled at block 100, and its energy cost-avoidance can be
calculated at block 106. A report issued at block 108 can either
prove or disprove--but more likely improve--the building design
before it leaves the drawing board, and long before it goes
on-line. Thus, building standards can be raised and designers or
owners of building meeting those higher standards--or the providers
of the design-improvement tools contemplated by the present
invention-can be monetarily rewarded.
[0116] Fifth, provision of real-time (run-time)
diagnostic/adjustment capabilities of the overall system that flows
from realistic and thus more accurate models and possibly anomalous
metering results will be appreciated. For example, if an economizer
controller were to fail and leave the economizer running on a hot
day, it would add considerably to the cooling requirement which, in
turn, would lead to higher consumption than normal. By comparing
the meter data with the projected as-improved baseline, the
operator would immediately see the anomalous values and be able to
diagnose and repair the problem.
[0117] Those of skill in the art will appreciate that the use of
myriad variables or factors in modeling is costly. Those of skill
will also appreciate that many of the variables are dependent upon
one another, rather than being independent, e.g. hours of operation
and occupancy each affects space usage. It will also be appreciated
that the result of such multi-factor modeling is rather easily
discounted. By `discounted` it is meant that the results are
questionable because many such diverse variables tend to cancel
each other out over time.
[0118] Moreover, as the model's complexity grows, its credibility
declines even if the model is comprehensive and reality-based.
Thus, in accordance with one embodiment of the invention, the model
is greatly simplified by considering relative few of the available
variables. This is because too granular of a data source (producing
a relatively greater volume of data) leads to an overwhelming
analysis task, whereas too rough a data source (producing a
relatively smaller volume of data) provides inadequate output. In
other words, there is believed to be an optimum balance between
data and information described thereby on which balance the present
invention capitalizes.
[0119] In particular, it has been found that the most, important
variable is based on building science, e.g. the design,
construction, materials, etc. of a facility, characterized by a
building's signature reaction to outside temperature over time.
Such an approach is more reality-based, and is simpler and thus
more cost-effective. More importantly, such an approach is more
credible and thus monetizable. Credibility also provides extensive
value for benchmarking purposes, inasmuch as embodiments of the
invention can be utilized to compare the energy use performance of
different buildings/facilities in different environments and/or
climates.
[0120] Referring to FIG. 4, invented system 10' is illustrated
according to an embodiment that focuses on building science-based
efficiency generators and closed-loop system verification and
improvements. System 10' includes a building science modeler 200
(as described herein that takes into account a building's dynamic
reaction to average outside temperature based upon its design,
construction, layout, materials, etc.); an energy consumption model
database (DB) 202 that stores the prediction results of such
building science-based modeling: a real-time meter 204 that
generates high-precision, real-time measurement data collected from
the building's various heating, cooling, and other energy-consuming
subsystems; a comparator 206 for comparing the modeled data with
the metered data; and a utility/grid 208 into which energy saved is
effectively fed. Utility/grid 208 in turn supplies energy to the
building 210 that was modeled and is now being metered.
[0121] Embodiments of the building science modeler 200 are
variously referred to herein as a `factor(s) regression modeler,`
an "analog building and/or facility modeler,` a `modeling
mechanism,` or simply `modeler,` and correspond with the modeler
100 of FIG. 3. These terms do not, however, necessarily indicate
different structural configurations relative to one another, but
instead indicate the several conceptual aspects of the modeler in
one or more embodiments of the invention.
[0122] The modeler 200 is typically coupled with the metering
portion 204, and configured with device-readable instructions
stored at a non-transitory data storage medium either integral with
or operatively coupled with modeler 200. In an exemplary but
non-exclusive embodiment, the modeler includes a Microsoft
Excel.TM. spreadsheet suitably configured with and/or with access
to the equations, data, and other information described herein.
[0123] A non-transitory device-readable medium, as referred to
throughout this description, can be a magnetic data storage medium
(e.g., hard disc drive, etc.), an optical data storage medium
(e.g., compact disc, etc), a solid state memory medium (e.g.,
random access memory circuit device, etc.), or any combination
thereof. Alternatively, a non-transitory device-readable medium can
be any other suitable memory device not listed here but which would
be known to an ordinarily skilled artisan, and at which data can be
stored and from which data can be retrieved.
[0124] The modeler 200 instructions are configured, when executed
by data processing circuitry (of a computing device, for example)
to cause the modeling mechanism to process each of a first set of
facility energy load data associated with a first time period, and
a first set of external environmental temperature data associated
with the first time period. According to an embodiment of the
invented method and system, processing the data comprises producing
an analog building model. More particularly, the processing
comprises inserting the measured first period temperature into the
equations indicated above, and iteratively solving the equations
for the initially unknown value of each of the parameters in Table
1.
[0125] An analog building model typically represents the total
energy load of a facility--during a first time period and at a
first measured temperature--as a sum of the plural identified
end-uses. A key breakthrough provided by the novel system of
end-use model equations and by the novel processing thereof is that
the resulting analog building model represents the particular
building-science-governed energy load characteristics of the
building. Therefore, the analog building model can subsequently
serve as a quite precise tool for modeling theoretical energy loads
of the facility under alternative tempera hire conditions.
[0126] Initially, the analog building model can be processed
according to an average temperature of each of numerous portions of
the first time period (e.g., monthly average temperatures of a
year-long first time period), to produce a historical baseline
energy load, against which later monthly facility energy load data
can be compared to determine a quantified difference due to, for
example, a non-routine energy conserving change in the
facility.
[0127] Later processing a second set of environmental temperature
data measured during a second, normally later time period, through
the analog building model, comprises calculating an estimate, or
projection, of what the energy load of that same facility would
have been during that same first time period had the temperature
instead been the same as during the second time period.
[0128] A second time period is typically monthly in duration in a
preferred embodiment, but is not so limited, and can be either
shorter or longer in duration. Time periods also need not be
measured according to calendar intervals, but can include any
designated, generally continuous time period. Time-based facility
energy load data for a time period typically represent a total or
substantially total facility energy load measurement for that time
period.
[0129] The real-time meter 204 of invented system 10' is also
referred to herein as a `metering portion.` Metering portion 204
can be a single energy meter apparatus suitable for measuring an
energy load of a facility, whether continuously or incrementally,
or can include plural energy meter apparatuses each suitable to
measure a separate portion of the total energy load of a facility.
For example, metering portion 204 can include one or more
electricity meters and/or one or more gas meters, or a combination
of both.
[0130] Each provided meter can measure an overall energy load of a
facility, or an overall facility load of a specific form of
delivered energy (e.g., electricity, gas, etc.), or an energy load
of a specific portion of a facility (e.g., one portion of a
building, or one building of a plurality of buildings grouped as a
`facility,` etc.), or an energy load of one or more specific
end-uses of a facility, or any combination thereof. Therefore, an
ordinarily skilled artisan will recognize that metering portion 204
encompasses a broad range of configurations according to
alternative embodiments.
[0131] An ordinarily skilled artisan will recognize that different
forms of energy may be measured according to different units of
measurement (e.g., B.T.U., watt/hours, etc.). Terms for measured
units may also be expressed differently in different geographic
regions, different industries, etc. Likewise, temperature can be
measured according to different thermal measurement systems and/or
units, e.g., Fahrenheit, Celsius, Kelvin, etc. Therefore, smart
meter 204 in various embodiments can measure and/or report an
energy load and/or temperature condition according to any one of or
any combination of suitable such units, and/or can convert units of
measurement to other units of measurement. For example, an
embodiment of a meter 204 can measure an energy load in one unit,
of measurement and convert the measured value to another unit of
measurement (e.g., process) for reporting to comparator
(calculator) 206.
[0132] Meter 204 will, in an embodiment, include data processing
circuitry suitably configured to perform such conversions. Meter
204 may likewise include a non-transitory data storage medium
suitably configured to store measurement data, whether raw or
processed/converted, prior to, during, and/or following such,
conversion processes, and to store coded instructions suitably
configured when executed to cause the circuitry to convert the
measurement data as indicated.
[0133] Metering portion 204 can also include one or more thermal
meter apparatuses suitable to measure an environmental temperature
external to a facility. A `suitable` thermal meter apparatus can be
nearly any device configured to precisely measure an ambient
temperature (e.g., external environmental temperature) in real
time, whether continuously or incrementally, and to render the
thermal measurements as data available for use by the comparator
(calculator) 206.
[0134] In at least one embodiment, the energy meter apparatus(es)
and the thermal meter apparatus(es) are combined within a single
(unitary) device. As such, a unitary real-time meter 204 can be
installed at a facility and/or coupled with an energy delivery
conduit. In at least one embodiment, the metering portion includes
a controller portion operatively coupled with either or both of a
heating subsystem and a cooling subsystem of a facility, and the
controller portion includes instructions configured, when executed
by circuitry as discussed above, to affect control of either or
both of the heating and cooling subsystem, helping to avoid
overheating, over cooling, or other excessive variations in
building temperature control.
[0135] In a typical embodiment, the energy consumption model
database (also referred to as an `analog facility model database,`
or simply `database`) 202 comprises a non-transitory data storage
medium coupled with the modeler. Accordingly, the database is
typically configured to receive from the modeler and to retrievably
store an analog facility model.
[0136] The comparator (`calculator`) 206 is coupled with the
database 202, and is configured to retrieve an analog building
model from the database and to compare the analog building model to
facility energy load data collected during a later time period than
was the facility energy load data used to construct the ABM.
Generally, the later collected facility energy load data was
collected during the same time period as was the external
environmental temperature data used by the ABM.
[0137] The calculator typically comprises coded instructions stored
at a non-transitory device-readable medium, in an embodiment, the
instructions are configured, when executed by data processing
circuitry, to cause the circuitry to calculate a sum of the plural
identified end-use energy load portions of the analog facility
model. Additionally, the calculator will typically calculate a
quantified difference between the later collected total energy load
data and the sum of the plural identified end-use energy load
portions of the first analog facility model. This quantified
difference corresponds to an amount of energy returned to a utility
grid by a non-routine change occurring in the facility between the
two facility energy load data measurement time periods.
[0138] Such change could either increase or decrease the energy
load of the facility. However, if the quantified difference
indicates a reduced facility energy load, the change is generally
recognized as resulting from a non-routine energy conserving change
to the facility. If such change occurs under an energy supply
contract that assigns credits for energy load reductions
attributable to conservation measures, for example, the facility
may be deemed to have earned a credit corresponding to the
quantified difference and according to the terms of the
contract.
[0139] As discussed above, an embodiment of the invented system
also includes a data aggregator configured with instructions
designed to cause the aggregator to aggregate energy load data from
multiple buildings, for example, for collective treatment as a
single facility. Although the data aggregator is not shown in FIG.
4, the aggregator should be understood to be an optional portion of
the system. Therefore, it can be present in the system embodiment
of FIG. 4 in much the same way as depicted at 102 in FIG. 3.
[0140] Generally, the data aggregator is operatively coupled with
one or more of the modeler 200, the metering portion 204, and the
energy consumption model data base 202, to receive data therefrom.
The data aggregator is further coupled with the
calculator/comparator 206 to provide one or more resulting
aggregated data sets for comparison and calculation of a difference
in energy load for the facility. Such aggregation not only
increases the accuracy obtainable via the invented method, but also
more directly represents the large scale interests of various ESCOs
and potential investors in energy-conserving programs and
technologies.
[0141] In an embodiment, the invented system includes at least one
data recording mechanism (not shown in FIG. 4) operatively coupled
with the calculator, and configured to record a quantified
difference between an energy load in an analog facility model and a
measured energy load in a set of time-based facility energy load
data. Such recording can include either of storing machine-readable
data for later retrieval, and/or producing a human-readable output.
Therefore, data can be recorded at either or both of a
non-transitory, machine-readable data storage medium and/or a
human-readable medium, and a data recording mechanism will include
and/or will be coupled with such media.
[0142] Embodiments of a data recording mechanism configured to
produce a human-readable output include a printer, an electronic
display device, or any other device configured to record a
human-readable output, as would be recognized by an ordinarily
skilled artisan.
[0143] In an embodiment, the invented system is coupled with a
utility grid. For example, a utility grid may be coupled with the
metering portion to receive redirected energy from the facility.
Energy may be redirected to the grid by modifying the facility with
an energy consumption-reducing material, device, reconfiguration,
technology, or other non-routine change. Energy redirected from the
facility to the grid pursuant to such change is quantified by the
calculator, and can be reported to a relevant ESCO or other
interested party.
[0144] `Redirected energy` is defined herein as a difference in
facility energy load between an ABM adjusted baseline total energy
load of the facility and a later measured, energy load data for the
facility following a non-routine change. Energy load differentials
due to routine changes, however, are not generally considered
herein to be redirected energy, nor are changes that result in an
increased facility energy load. As described above, one of the key
differences between the invented system and method is that effects
on a facility energy load due to non-routine changes can typically
be segregated from effects due to routine changes, with a
relatively high, degree of specificity.
[0145] Referring again to FIG. 4, an anomalous result may be
detected in real-time at decision block 212, wherein an anomaly
leads to a possible real-time diagnosis and/or repair at block 214.
For example, a trace or plot of metered data might immediately
indicate to a trained analyst that a heater has failed or was
installed improperly. It is also possible that, the anomalous
result can lead to an improvement to the building's design,
modeling, or energy-consuming subsystems at block 216 (such
possibility being indicated by a dashed line).
[0146] To aid in diagnosing an anomalous result, the system is
configured in an embodiment to produce a second analog building
model using the facility energy load data and external
environmental temperature data from the second time period.
[0147] Functional blocks 200, 202, 204 and 206 are referred to
herein as an energy efficiency generator 218, as indicated by a
dashed outline in FIG. 4. Those of skill in the art will appreciate
that energy efficiency generator 218 may also include one or more
of functional blocks 212, 2.14, and 216, since those functional
blocks also add value to the energy efficiency proposition by way
of closed-loop feedback and potentially real-time problem
identification and resolution. Those of skill in the art will
appreciate that any or all of the functional blocks shown in FIG. 4
can be implemented in hardware, firmware, or a combination thereof.
For example, a general-purpose computer or processor can execute
instructions stored in memory such that it acts as a
special-purpose computer or a machine that transforms metered data
into energy cost-avoidance reports and carbon credits that are
tangible.
[0148] FIG. 5 depicts an embodiment of a method 500 for quantifying
efficiency-generated energy resources redirected from an
energy-consuming facility to a metered utility grid utilizing the
invented system. A first set of external environmental temperature
data and a first set of total energy load data of the facility are
collected throughout a first time period at 502. In an optional
operation depicted at 504, wherein the facility comprises plural
buildings, institutions, etc, a data aggregator aggregates the
first time-based facility energy load data from the plural
buildings to create a consolidated energy load data set for the
facility.
[0149] At 506, the exemplary embodiment further includes processing
the first collected sets of external environmental temperature data
and total energy load data of the facility, the processing
producing an analog facility model. In a typical embodiment, the
processing includes executing an iterative steepest-descent
convergence algorithm, using the end use model equations described
above. The produced analog facility model includes plural model
end-use energy load portions, a sum of which represents a total
energy load for the analog facility model.
[0150] At 508, each of a second set of external environmental
temperature data and a second set of total energy load data of the
facility are collected by a metering device throughout a second
time period. Typically, the second time period is chronologically
later than the first time period. As with the operation at 504, a
data aggregator, at 510, optionally aggregates the second
time-based facility energy load data from plural buildings to
create a second consolidated energy load data set for the
facility.
[0151] At 512, the analog building model is run (e.g., processed)
substituting the temperature data from the first period with the
temperature data from the second time period. Then, at 514, an
adjusted baseline total energy load of the facility is calculated
by summing the adjusted end use energy loads resulting from the
operation at 512. The adjusted baseline energy load represents an
estimate of what the total energy load of the facility would have
been during the first time period if the external environmental
temperature during the first time period had been the same as the
external environmental temperature during the second time
period.
[0152] At 518, the exemplary embodiment further includes
calculating (e.g., quantifying) a difference between the second set
of total energy load data and the adjusted baseline total energy
load of the facility. At 518, the difference is recorded at either
or both of a non-transitory, machine-readable data storage medium
and a human-readable medium.
[0153] At 520, the exemplary embodiment further includes
determining a credit due against a contracted cost for energy
corresponding to the quantified, difference between the second set
of total energy load data and the sum of the plural identified
end-use energy load portions of the first analog facility model and
at 522, recording the credit at either or both of a non-transitory,
machine-readable data storage medium, and a human-readable medium.
As used herein, a credit can represent and/or be nearly anything of
value. For example, a credit can include any one or more of a
reduced energy cost/price, an advance against future energy costs,
access to one or more additional resources, access to an additional
and/or premium service or service level, full or partial
satisfaction and/or forgiveness of an obligation, etc.
[0154] One or more of operations 502 through 522 are performed in
an embodiment of the invented method by data processing circuitry
executing device-readable instructions stored at a non-transitory
data storage medium. The circuitry can be a portion of a general
purpose computer (e.g., a solid state microprocessor or another
integrated circuit device), or can be a portion of a specialty
device (e.g., an embodiment of a `smart meter`). Generally, the
circuitry will be operatively coupled with a non-transitory data
storage means including instructions specially configured when
executed by such circuitry to produce an analog facility model
according to the described embodiments, and/or to perform any one
or more of operations 502 through 522.
[0155] It has been found that energy savings over a range of
approximately 15-60% are realizable and monetizable using the
present invention. Thus, it is appreciated by those of skill in the
art that the use of the unique regression model described and
illustrated herein to predict future energy consumption baselines,
a precision `smart` meter to measure current, real-time energy
consumption based upon improved building design and construction,
and a comparator and reporting mechanism can semi-automate or fully
automate the previously difficult task of accurate, repeatable, and
thus reliable determination of the actual energy redirecting
effects of non-routine facility change (e.g., carbon additionally).
It is also appreciated that energy conservation (and energy
conserved by conservation measures) can be sold as a valuable
product whether back into the grid or as a new standard for design
and construction or as a profitable energy conservation
enterprise.
[0156] Those of skill in the art will appreciate that natural
extensions of the science-based factoring and other regression
modeling concepts embodied in the present invention present
themselves. For example, a facility might include a single
building, in which the parties in interest to the energy cost
avoidance include the tenant, the landlord, and the building owner.
Or a facility might include a city block having multiple buildings
each readily modeled, but with an understanding of each building's
impact on the other buildings in the block, e.g. sunlight exposure
to direct or reflected light and heat. Or a facility might be a
teaching hospital, a university, a business, or other institutional
campus covering several city blocks or even acres, in which the
interplay between even remotely located (e.g., widely separated)
buildings might be comprehended by a regression model. For example,
a building that incidentally produces a refrigerated-air stream
by-product to its main purpose can be vented to a nearby building
otherwise requiring an energy-consuming air conditioning subsystem.
Thus, facility is intentionally broadly defined herein to include
any and all such single and multiple, local and remote, building
combinations. Naturally, the advantages of aggregation and scale
discussed above are easier to realize and to monetize for larger
facilities.
[0157] It will be understood that the present invention is not
limited to the method or detail of construction, fabrication,
material, application or use described and illustrated herein.
Indeed, any suitable variation of fabrication, use, or application
is contemplated as an alternative embodiment, and thus is within
the spirit and scope, of the invention.
[0158] It is further intended that any other embodiments of the
present invention that result from any changes in application or
method of use or operation, configuration, method of manufacture,
shape, size, or material, which are not specified within the
detailed written description or illustrations contained herein yet
would be understood by one skilled in the art, are within the scope
of the present invention.
[0159] Finally, those of skill in the art will appreciate that the
invented method, system and apparatus described and illustrated
herein may be implemented in software, firmware or hardware, or any
suitable combination thereof. Preferably, the method system and
apparatus are implemented in a combination of the three, for
purposes of low cost and flexibility. Thus, those of skill in the
art will appreciate that embodiments of the methods and system of
the invention may be implemented by a computer or microprocessor
process in which instructions are executed, the instructions being
stored for execution on a computer-readable medium and being
executed by any suitable instruction processor.
[0160] Accordingly, while the present invention has been shown and
described with reference to the foregoing embodiments of the
invented apparatus, it will be apparent to those skilled in the art
that other changes in form and detail may be made therein without,
departing from the spirit and scope of the invention as defined in
the appended claims.
* * * * *