U.S. patent application number 14/942849 was filed with the patent office on 2016-03-10 for real time provisional evaluation of utility program performance.
The applicant listed for this patent is EnergySavvy Inc.. Invention is credited to Brett Clouser, Charles David Ellis, Mark Louis Ghazal, Aaron Ross Goldfeder, Leonid Borisovich Shklovskii.
Application Number | 20160071125 14/942849 |
Document ID | / |
Family ID | 52626450 |
Filed Date | 2016-03-10 |
United States Patent
Application |
20160071125 |
Kind Code |
A1 |
Ellis; Charles David ; et
al. |
March 10, 2016 |
REAL TIME PROVISIONAL EVALUATION OF UTILITY PROGRAM PERFORMANCE
Abstract
Embodiments are directed towards automatic provisional
evaluation of utility program performance. Treatment facilities may
be determined depending characteristics of a project. A performance
profile corresponding to each treatment facility may be generated
based on historical information corresponding to each treatment
facility. Current usage information and current weather information
for each treatment facility may be determined. Pre-treatment usage
information that corresponds to each treatment facility may be
determined based on the performance profile and the current usage
information and the current weather information. A program
evaluation report may be generated that includes program
realization information. Program realization information may be
based on an aggregation of project savings information that
corresponds to the treatment facilities. Project savings
information may be modified based on confidence weights. A program
evaluation report may include information from one or more
comparison facilities.
Inventors: |
Ellis; Charles David;
(Seattle, WA) ; Shklovskii; Leonid Borisovich;
(Seattle, WA) ; Ghazal; Mark Louis; (Seattle,
WA) ; Clouser; Brett; (Seattle, WA) ;
Goldfeder; Aaron Ross; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
EnergySavvy Inc. |
Seattle |
WA |
US |
|
|
Family ID: |
52626450 |
Appl. No.: |
14/942849 |
Filed: |
November 16, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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14106363 |
Dec 13, 2013 |
9189766 |
|
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14942849 |
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61876083 |
Sep 10, 2013 |
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Current U.S.
Class: |
705/7.31 |
Current CPC
Class: |
Y02P 90/82 20151101;
G06Q 30/0283 20130101; G06Q 50/06 20130101; G06Q 10/06393 20130101;
G06N 5/00 20130101; G06Q 10/0639 20130101; G06Q 30/0202
20130101 |
International
Class: |
G06Q 30/02 20060101
G06Q030/02; G06Q 50/06 20060101 G06Q050/06; G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A method for evaluating reduction of utility service demand for
one or more facilities using a computer that includes one or more
processors that execute logic to perform actions, comprising:
employing a real time evaluation application to perform actions,
including: providing one or more projects to treat the one or more
facilities based on one or more characteristics of the one or more
facilities; providing one or more predictions of post-treatment
utility service demand usage and confidence information for each
location of the one or more facilities based on previous utility
service demand usage, previous operational information, previous
treatment information, and historical weather information based on
one or more of signals provided by a global positioning system
(GPS) sensor and previous weather data for each location; providing
pre-treatment utility service demand information for the one or
more facilities, wherein the pre-treatment utility service demand
information is based on current usage information and one or more
performance profiles for the one or more facilities, wherein the
one or more performance profiles is based on previous utility
service demand usage, previous operational information, and
historical weather information; and providing project savings
information for a reduction in utility service demand for the one
or more facilities, wherein the project savings information is
based on completion of at least one of the one or more projects for
the one or more of the facilities.
2. The method of claim 1, wherein the real time evaluation
application performs further actions, comprising: providing one or
more comparison facilities based on the one or more characteristics
of the one or more facilities; and updating the project savings
information for the one or more facilities based on a comparison of
the reduction in utility service demand to other utility service
demand for the one or more comparison facilities.
3. The method of claim 1, wherein the real time evaluation
application performs further actions comprising: providing another
performance profile that corresponds to one or more comparison
facilities, wherein the other performance profile includes previous
utility service demand usage and previous weather information for
each location of the one or more comparison facilities; providing
comparison savings information based on the other performance
profile and the current weather information; and updating the
project savings information based on the comparison savings
information.
4. The method of claim 1, wherein the real time evaluation
application performs further actions, comprising providing a report
based on the execution of the one or more projects for the one or
more facilities, wherein the report includes realization
information based on an aggregation of project savings information
for the one or more facilities treated by the one or more
projects.
5. The method of claim 1, wherein the realization information
includes pre-treatment utility service demand information and
actual post-treatment utility service demand information.
6. The method of claim 1, wherein the previous operational
information and the previous treatment information is provided by
one or more of a current utility service provider, a previous
utility service provider, or a third party resource.
7. The method of claim 1, wherein the real time evaluation
application performs further actions, comprising providing one or
more recommendations for one or more new projects to treat the one
or more facilities, wherein the one or more recommendations include
one or more of a type, a duration, or a cost to treat the one or
more facilities.
8. The method of claim 1, wherein the real time evaluation
application performs further actions, updating predictions of
post-treatment utility service demand usage based on one or more of
new current usage information or new current weather
information.
9. A network computer for evaluating reduction of utility service
demand for one or more facilities, comprising: a transceiver for
communicating over the network; a memory for storing at least
instructions; one or more processors that execute logic to perform
actions, including: employing a real time evaluation application to
perform actions, including: providing one or more projects to treat
the one or more facilities based on one or more characteristics of
the one or more facilities; providing one or more predictions of
post-treatment utility service demand usage and confidence
information for each location of the one or more facilities based
on previous utility service demand usage, previous operational
information, previous treatment information, and historical weather
information based on one or more of signals provided by a global
positioning system (GPS) sensor and previous weather data for each
location; providing pre-treatment utility service demand
information for the one or more facilities, wherein the
pre-treatment utility service demand information is based on
current usage information and one or more performance profiles for
the one or more facilities, wherein the one or more performance
profiles is based on previous utility service demand usage,
previous operational information, and historical weather
information; and providing project savings information for a
reduction in utility service demand for the one or more facilities,
wherein the project savings information is based on completion of
at least one of the one or more projects for the one or more of the
facilities.
10. The network computer of claim 9, wherein the real time
evaluation application performs further actions, comprising:
providing one or more comparison facilities based on the one or
more characteristics of the one or more facilities; and updating
the project savings information for the one or more facilities
based on a comparison of the reduction in utility service demand to
other utility service demand for the one or more comparison
facilities.
11. The network computer of claim 9, wherein the real time
evaluation application performs further actions, comprising:
providing another performance profile that corresponds to one or
more comparison facilities, wherein the other performance profile
includes previous utility service demand usage and previous weather
information for each location of the one or more comparison
facilities; providing comparison savings information based on the
other performance profile and the current weather information; and
updating the project savings information based on the comparison
savings information.
12. The network computer of claim 9, wherein the real time
evaluation application performs further actions, comprising
providing a report on a display based on the execution of the one
or more projects for the one or more facilities, wherein the report
includes realization information based on an aggregation of project
savings information for the one or more facilities treated by the
one or more projects.
13. The network computer of claim 9, wherein the realization
information includes pre-treatment utility service demand
information and actual post-treatment utility service demand
information.
14. The network computer of claim 9, wherein the previous
operational information and the previous treatment information is
provided by one or more of a current utility service provider, a
previous utility service provider, or a third party resource.
15. The network computer of claim 9, wherein the real time
evaluation application performs further actions, comprising
providing one or more recommendations for one or more new projects
to treat the one or more facilities, wherein the one or more
recommendations include one or more of a type, a duration, or a
cost to treat the one or more facilities.
16. The network computer of claim 9, wherein the real time
evaluation application performs further actions, including updating
predictions of post-treatment utility service demand usage based on
one or more of new current usage information or new current weather
information.
17. A processor readable non-transitive storage media that includes
instructions for evaluating reduction of utility service demand for
one or more facilities, wherein a network computer that employs one
or more processors to execute the instructions performs actions,
comprising: employing a real time evaluation application to perform
actions, including: providing one or more projects to treat the one
or more facilities based on one or more characteristics of the one
or more facilities; providing one or more predictions of
post-treatment utility service demand usage and confidence
information for each location of the one or more facilities based
on previous utility service demand usage, previous operational
information, previous treatment information, and historical weather
information based on one or more of signals provided by a global
positioning system (GPS) sensor and previous weather data for each
location; providing pre-treatment utility service demand
information for the one or more facilities, wherein the
pre-treatment utility service demand information is based on
current usage information and one or more performance profiles for
the one or more facilities, wherein the one or more performance
profiles is based on previous utility service demand usage,
previous operational information, and historical weather
information; and providing project savings information for a
reduction in utility service demand for the one or more facilities,
wherein the project savings information is based on completion of
at least one of the one or more projects for the one or more of the
facilities.
18. The media of claim 17, wherein the real time evaluation
application performs further actions, comprising: providing one or
more comparison facilities based on the one or more characteristics
of the one or more facilities; and updating the project savings
information for the one or more facilities based on a comparison of
the reduction in utility service demand to other utility service
demand for the one or more comparison facilities.
19. The media of claim 17, wherein the real time evaluation
application performs further actions, comprising: providing another
performance profile that corresponds to one or more comparison
facilities, wherein the other performance profile includes previous
utility service demand usage and previous weather information for
each location of the one or more comparison facilities; providing
comparison savings information based on the other performance
profile and the current weather information; and updating the
project savings information based on the comparison savings
information.
20. The media of claim 17, wherein the real time evaluation
application performs further actions, comprising providing a report
on a display based on the execution of the one or more projects for
the one or more facilities, wherein the report includes realization
information based on an aggregation of project savings information
for the one or more facilities treated by the one or more
projects.
21. The media of claim 17, wherein the realization information
includes pre-treatment utility service demand information and
actual post-treatment utility service demand information.
22. The media of claim 17, wherein the previous operational
information and the previous treatment information is provided by
one or more of a current utility service provider, a previous
utility service provider, or a third party resource.
23. The media of claim 17, wherein the real time evaluation
application performs further actions, comprising providing one or
more recommendations for one or more new projects to treat the one
or more facilities, wherein the one or more recommendations include
one or more of a type, a duration, or a cost to treat the one or
more facilities.
24. The media of claim 17, wherein the real time evaluation
application performs further actions, including updating
predictions of post-treatment utility service demand usage based on
one or more of new current usage information or new current weather
information.
25. A system for evaluating reduction of utility service demand for
one or more facilities, comprising: a network computer, including:
a transceiver for communicating over the network; a memory for
storing at least instructions; one or more processors that is
operative to execute instructions that perform actions, including:
employing a real time evaluation application to perform actions,
including: providing one or more projects to treat the one or more
facilities based on one or more characteristics of the one or more
facilities; providing one or more predictions of post-treatment
utility service demand usage and confidence information for each
location of the one or more facilities based on previous utility
service demand usage, previous operational information, previous
treatment information, and historical weather information based on
one or more of signals provided by a global positioning system
(GPS) sensor and previous weather data for each location; providing
pre-treatment utility service demand information for the one or
more facilities, wherein the pre-treatment utility service demand
information is based on current usage information and one or more
performance profiles for the one or more facilities, wherein the
one or more performance profiles is based on previous utility
service demand usage, previous operational information, and
historical weather information; and providing project savings
information for a reduction in utility service demand for the one
or more facilities, wherein the project savings information is
based on completion of at least one of the one or more projects for
the one or more of the facilities.
26. The system of claim 25, wherein the real time evaluation
application performs further actions, comprising: providing one or
more comparison facilities based on the one or more characteristics
of the one or more facilities; and updating the project savings
information for the one or more facilities based on a comparison of
the reduction in utility service demand to other utility service
demand for the one or more comparison facilities.
27. The system of claim 25, wherein providing the report, further
comprises: providing another performance profile that corresponds
to one or more comparison facilities, wherein the other performance
profile includes previous utility service demand usage and previous
weather information for each location of the one or more comparison
facilities; providing comparison savings information based on the
other performance profile and the current weather information; and
updating the project savings information based on the comparison
savings information.
28. The system of claim 25, wherein the real time evaluation
application performs further actions, comprising, updating the
project savings information based on one or more weighted values
for the confidence information.
29. The system of claim 25, wherein the real time evaluation
application performs further actions, comprising providing one or
more recommendations for one or more new projects to treat the one
or more facilities, wherein the one or more recommendations include
one or more of a type, a duration, or a cost to treat the one or
more facilities.
30. The system of claim 25, wherein the real time evaluation
application performs further actions, comprising providing a report
on a display based on the execution of the one or more projects for
the one or more facilities, wherein the report includes realization
information based on an aggregation of project savings information
for the one or more facilities treated by the one or more projects.
Description
CROSS-REFERENCE TO RELATED APPLICATION(S)
[0001] This Utility patent application is a Continuation of U.S.
patent application Ser. No. 14/106,363 filed on Dec. 13, 2013, now
U.S. Pat. No. 9,189,766 issued on Nov. 17, 2015, which is based on
a previously filed U.S. Provisional Patent Application Ser. No.
61/876,083 filed on Sep. 10, 2013, the benefits of which are
claimed under 35 U.S.C. .sctn.120 and .sctn.119(e), and which are
each further incorporated by reference in their entireties.
TECHNICAL FIELD
[0002] The present invention relates generally to performance
evaluation of utility programs, and more particularly, but not
exclusively to impact evaluation of efficiency programs.
BACKGROUND
[0003] Utilities, such as, electrical utilities, water utilities,
or the like, are often required to implement efficiency programs,
such as energy efficiency programs that may be designed to reduce
the energy consumption or peak demand of their consumers. Often the
utilities are required to commit to specified efficiency (e.g.,
energy reduction) targets. In some cases, utilities that fail to
meet these reduction targets may be subject to sanctions and/or
fines imposed by one or more regulatory agencies.
[0004] Efficiency programs, such as energy efficiency programs may
take many forms and may involve multiple projects. Some of these
projects may include retrofitting existing facilities to reduce
demand for and/or consumption of services, such as electricity,
water, natural gas, or the like. Projects may be directed to some
or all of the various types of facilities that receive services
from the utility, such as, single family housing, multi-family
housing, commercial buildings, manufacturing facilities, or the
like. Efficiency programs may be required to undergo an evaluation
to determine if they met the efficiency targets that were promised
or expected by the utility or required by the body regulating the
utility. If the programs fail to meet the promised or expected
reduction targets, the utility may be subject to additional
regulatory scrutiny or potentially even fines or other
sanctions.
[0005] Given current approaches, it may be difficult for utilities
to evaluate programs while the programs are being implemented.
Thus, utilities usually wait until a program is completed before
determining if the program's targets have been met. Accordingly, it
may be difficult for utilities to determine whether their programs
need to be modified to meet their targets during the
implementation, or how they might improve the performance of
programs at meeting the relevant targets. Thus, utilities may be
liable for severe fees and sanctions if the programs fall short, or
they may over-engineer the program/projects to mitigate the risk of
falling short of their targets. Thus, it is with respect to these
considerations and others that the invention has been made.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] Non-limiting and non-exhaustive embodiments of the present
innovations are described with reference to the following drawings.
In the drawings, like reference numerals refer to like parts
throughout the various figures unless otherwise specified. For a
better understanding of the present innovations, reference will be
made to the following Description of the Various Embodiments, which
is to be read in association with the accompanying drawings,
wherein:
[0007] FIG. 1 illustrates a system diagram showing components of an
environment in which at least one of the various embodiments may be
practiced;
[0008] FIG. 2 shows one embodiment of a client computer that may be
included in a system;
[0009] FIG. 3 illustrates one embodiment of a network computer that
may be included in a system;
[0010] FIG. 4 shows a logical diagram of a system for real-time
provisional evaluation of realization in accordance with at least
one of the various embodiments
[0011] FIG. 5 shows a graph illustrating pre-treatment usage
predicted by a pre-treatment performance profile and current usage
information in accordance with at least one of the various
embodiments;
[0012] FIGS. 6A-6C show portions of an overview flowchart a process
for real time provisional evaluation of utility programs in
accordance with at least one of the embodiments;
[0013] FIG. 7 shows a flowchart for a process for determining
comparison group members in accordance with at least one of the
various embodiments;
[0014] FIG. 8 shows a flowchart for a process for generating
pre-treatment performance profiles in accordance with at least one
of the various embodiments;
[0015] FIG. 9 shows a flowchart for a process for generating
comparison savings in accordance with at least one of the various
embodiments;
[0016] FIG. 10 shows a flowchart for a process for generating
program performance reports in accordance with at least one of the
various embodiments.
DESCRIPTION OF VARIOUS EMBODIMENTS
[0017] The present innovations now will be described more fully
hereinafter with reference to the accompanying drawings, which form
a part hereof, and which show, by way of illustration, specific
embodiments by which the invention may be practiced. These
innovations may, however, be embodied in many different forms and
should not be construed as limited to the embodiments set forth
herein; rather, these embodiments are provided so that this
disclosure will be thorough and complete, and will fully convey the
scope of the invention to those skilled in the art. Among other
things, the present innovations may be embodied as methods,
computers, or devices. Accordingly, the present innovations may
take the form of an entirely hardware embodiment, an entirely
software embodiment or an embodiment combining software and
hardware aspects. The following detailed description is, therefore,
not to be taken in a limiting sense.
[0018] Throughout the specification and claims, the following terms
take the meanings explicitly associated herein, unless the context
clearly dictates otherwise. The phrase "In one of the embodiments"
or "in at least one of the various embodiments" as used herein does
not necessarily refer to the same embodiment, though it may.
Furthermore, the phrase "in another embodiment" as used herein does
not necessarily refer to a different embodiment, although it may.
Thus, as described below, various embodiments of the innovations
may be readily combined, without departing from the scope or spirit
of the innovations.
[0019] In addition, as used herein, the term "or" is an inclusive
"or" operator, and is equivalent to the term "and/or," unless the
context clearly dictates otherwise. The term "based on" is not
exclusive and allows for being based on additional factors not
described, unless the context clearly dictates otherwise. In
addition, throughout the specification, the meaning of "a," "an,"
and "the" include plural references. The meaning of "in" includes
"in" and "on."
[0020] As used herein, the term "utility" refers to entities that
are involved in one or more of generation, transmission, sales, or
delivery of utility services, such as, electric power, natural gas,
water, sewage, waste management, recycling, or the like. Utilities
may often be quasi-public corporations, operating under a grant or
license from a local or regional government these may be referred
to as public utilities. Utilities are usually organized based on
the type of energy they provide, such as, electricity, natural gas,
or the like. In some cases, they are organized and/or described
based on the type of power plants they use to generate energy, such
as, coal, nuclear, steam, hydro-power, or the like. In the interest
of clarity, the embodiments disclosed herein may be described in
terms of electric power utilities. However, the innovations are not
so limited. Thus, utilities providing services other than
electrical power are contemplated as being within the scope of the
innovations described and claimed herein.
[0021] As used herein, the terms "service," and "services" refers
to the services provided by a utility. For example, the service
provided by energy utilities may be power, such as, electricity,
natural gas, steam, or the like. Likewise, the service provided by
water utilities is water, and so on, for other types of
utilities.
[0022] As used herein, the term "facility" refers to commercial or
residential buildings or structures that are receiving services
from a utility. There may be several type of facilities, including
residential (including single-family housing and multi-family
housing), commercial, retail, industrial, agricultural, public
(such as federal, state, or municipal), or the like. A facility may
be considered to be a component of a larger structure and/or
facility. For example, in some cases, a factory may have several
large energy consuming machines that are metered individually. In
this type of case, each machine may be considered a facility.
Generally, if the utility services demanded by a component may be
metered that component may be considered a facility for the
purposes of utility performance purposes. A treatment facility is a
facility that is or will undergo treatment as part of project. A
comparison facility is a facility that is assigned to a comparison
group.
[0023] As used herein, the terms "program," "conservation program,"
or "efficiency program", refer to intentional efforts by a utility
to reduce the demand from its subscribers/consumers for provided
services, such as energy. A program may include a set of one or
more projects and/or treatments designed to reduce service
consumption. Programs may have a specified duration and a specified
set of service demand reduction targets. Programs may be evaluated
to determine if they are meeting, or have met, their service demand
reduction targets. For example, in at least one of the various
embodiments, an electric power utility may implement a two-year
program to reduce energy consumption through the retrofitting of
existing homes with additional insulation with a 1 gigawatt hour
demand reduction target. Examples of classes of service demand
reduction targets include reducing the total quantity of a resource
or service used (e.g. energy, water, or waste removal), reducing
the demand for the service at peak service utilization or at other
periods of service or resource scarcity, or reducing the total
quantity or service demand in a particular geographic location or
among a certain type of service or customers.
[0024] As used herein, the term "project" refers to actions by a
utility to enable its subscribers, consumers, and/or user base to
reduce their consumption of services at a single facility, using a
collection of specific treatments. Accordingly, for energy
utilities, a project may be designed to reduce the energy demand of
a subscriber who may be the owner or operator of a facility.
Projects may include improving the service efficiency of a new or
existing facility that is serviced by the utility. Improving
efficiency of an existing facility may include retrofitting the
facility to reduce service demand. For example, for energy
utilities, a project may be designed to retrofit a building to
improve the energy efficiency of a facility within the building.
Examples of energy efficiency projects may include adding
insulation, replacing insulation, roof repair/replacement,
replacing outdated appliances with newer energy efficient
appliances, replacing single pane windows with multi-pane windows,
installing set back thermostat controls, enrolling consumers into
smart-grid programs, replacing inefficient water heaters, replacing
inefficient furnaces, replacing inefficient air conditioners, light
bulb replacement or the like, or combinations thereof. Also, some
projects may be educational/behavioral in nature, such as,
providing classes on how to reduce service consumption,
peak/off-peak pricing, public awareness campaigns, public service
commercials, or the like, or combination thereof. In at least one
of the various embodiments, project performance may be tracked
separately for each facility that is treated as part of a project.
For example, a program may include providing tax credits to
subscribers that replace their facility's roofs using energy saving
materials. In this case, each facility that has a roof replaced
under the program may be considered to be a separate project.
[0025] As used herein, the term "weather data source" refers to
various sources of weather information that may be available to a
utility. Weather data may include real-time information, historical
information, forecast information, or the like. Weather data may be
segmented and/or aggregated based on one or more political or
geographical regions, such as, countries, portions of countries
(e.g., Eastern, Western, Southern, Northern, Central, or the like),
states, portion of states, postal codes, cities, neighborhoods, or
the like. Weather data may be provided by one or more international
or domestic public or private agencies, such as, the United States
National Weather Service (NWS), National Oceanic and Atmospheric
Administration (NOAA), Environment Canada's Meteorological Service
of Canada, regional agencies, state agencies, or the like. Further,
weather data may be collected and/or generated by the utility
itself using various weather data collection instruments. Weather
data may include raw and/or normalized data. Also, weather data may
include values computed in terms of heating degree days, cooling
degree days, or the like.
[0026] As used herein, the term "heating degree day (HDD)" refers
to a well-known measurement of weather that may be used to model
demand for services relating to the heating of a facility that are
less efficient, and thus consume more services, below a certain
temperature point. The number of HDD for a given location and a
given period is defined to be the length of the period in days
times the total number of degrees below a given base temperature
each day that occur at the given location during the given period.
The heating demand for a given facility at a specific location may
be modeled as being proportional to the number of HDD at that
location. HDDs are defined relative to a base temperature that
represents the outside temperature above which a facility has no
demand for heating services. The appropriate base temperature for
facilities within a particular building depend on the target
temperature to which the building is heated and the ability of the
building to maintain temperature irrespective of external
temperature. Accordingly, the more HDDs a facility is exposed to,
the higher the usage demand for heating.
[0027] As used herein, the "cooling degree day (CDD)" refers to a
well-known measurement of weather that may used to model demand for
services relating to the cooling of a facility or that are less
efficient, and thus consume more services, above a certain
temperature. CDD is similar to HDD except it is a measurement of
the degree days of cooling required to lower the temperature to a
base temperature.
[0028] As used herein, the term "usage data source" refers to a
source or sources of information regarding the demand for service
(i.e. the usage or services) at facilities serviced by a utility
(e.g., utility service demand information). For energy utilities,
usage data typically consists of the amount of energy used and
power demanded by the facility over various periods of time. Usage
data may be provided for various lengths of time periods, such as,
monthly, weekly, daily, hourly or the like, depending the
availability and precision of service metering devices. In some
embodiments, the utility's own metering infrastructure and data
systems, including the systems that are used for customer billing,
may be the primary usage data source. In other embodiments, the
usage data may come from a meter or other source of usage
information maintained by the operator of the facility or a third
party.
[0029] As used herein, the term "operations data source" refers to
sources of information about the operational requirements of one or
more facilities that may be relevant to the service demands of the
facility or facilities. This may include operational/manufacturing
data from commercial facilities, and may be obtained from a
supervisory control and data acquisition (SCADA) system or similar.
Such information may include information that may be associated
with the service demands, such as energy usage of the facility,
because service demands may increase when the operation of the
building requires additional hours or intensity of operation. For
example, an automobile factory facility may provide information
that includes the number of automobiles produced over a time
period. This type of information may be useful for evaluating
utility efficiency projects over the same time period. For example,
a change in energy demand may be caused by the change in
manufacturing activity rather than being influenced by the
efficiency of the energy use in the facility.
[0030] As used herein, the term "project data source" refers to
sources of information about the goals, target, progress,
completion, or the like, of each project that make up one or more
utility efficiency programs. Project data sources may include one
or more databases that include information regarding various
aspects of one or more of projects. This may include, facility
information (e.g., asset information, geographic information,
occupancy information, or the like), project updates,
retrofit/installation updates (e.g., appliances replaced,
insulation improvement, windows replaced, or the like),
target/expected efficiency savings information, or the like. In at
least one of the various embodiments, project information may be
collected using one or more project tracking applications that may
enable those administering the program or implementing the project
to provide project status information as actions related to
projects are completed on a facility.
[0031] As used herein, the term "asset information" refers to
information regarding the systems, including structural systems,
HVAC systems, water systems, energy systems, waste systems, etc.,
or installation information for systems, that impact the service
demands of a facility or facilities, and which may be added,
altered, or otherwise improved upon as a part of a project and/or
program. Asset information may include details that may be employed
to compute one or more service demand or efficiency values for a
particular facility. Such information may include construction
materials, foundation type, heating and cooling plant information,
number and types of pump employed, and operational settings of each
system, or the like.
[0032] As used herein, the term "treatment group" refers to the
collection of facilities that have participated in a project during
a given program implementation period.
[0033] As used herein, the term "comparison group" refers to the
collection of facilities to which the treatment group is compared
in order to more accurately ascertain the impact of participation
in the program, and consists of facilities which have not
participated in the program during the implementation period. The
comparison group can provide a more effective comparison for the
treatment group facilities (and hence a more accurate picture of
the effect of program participation) if the comparison group
members have been selected so as to have similar characteristics
and make up as the facilities that make up the treatment group. For
example, if a treatment facility for a project is a three bedroom
home occupied by a family of four built before 1970 and with
single-pane windows, it is typically most effective to include
facilities having similar characteristics in the comparison
group.
[0034] As used herein, the terms "profile," "performance profile,"
and "pre-treatment performance profile" each refer to the model of
the relationship between the weather and operational data at a
facility and the service demands of that facility. Profiles may be
comprised of one or more models that represent a particular
facility. In at least one of the various embodiments, profiles may
be generated based on generating curves fitted to historical
service usage information and historical weather and operational
information for a given facility. Pre-treatment performance
profiles may be generated to model facility responsiveness based on
how the facility is expected to perform before a treatment
associated with a project has been performed.
[0035] As used herein, the terms "observed savings" or "observable
savings" refer to a measurement of the amount post-treatment
service reduction observed at a treatment facility over a time
period. Observed savings may be expressed in units appropriate for
the utility service that is being measured, such as kilowatt hours
(KWh) for energy services, gallons for water services, etc.
[0036] As used herein, the term "typical year savings" refers to
the savings which obtain for a project during an idealized year
that includes typical weather and operational patterns. Typical
year savings may be used to measure utility service demand
reduction programs in order to avoid weather or operational
dependence in utility program evaluation. Typical year savings may
be calculated as occurring in an idealized year with typical
weather and typical operational use over the course of a year.
[0037] As used herein, the terms "savings confidence" or
"confidence rate" refer to a measurement of the amount of a typical
year's worth of utility service demand reduction that should be
observable during the post-treatment period for a given project at
a given facility given the time period for which data is available.
A savings confidence of 100% or greater, therefore, may be obtained
for some or all projects for which at least a full typical year of
service demand reduction should be observable. Note that savings
confidence refers to how much of the savings should be observable,
as opposed to how much savings are observed. Hence, in at least one
of the various embodiments, a savings confidence of 25% for a
project corresponds with only having data for a period of time
during which a quarter of a typical year's savings observable, as
opposed to corresponding to a period during which the observed
savings were 25% of what was expected during a typical year.
[0038] As used herein, the term "annualized savings" refers to a
measurement of the amount of post-treatment reduction in service at
a treatment facility that would be seen over a typical year.
Annualized savings may differ from observed savings in that
observed savings occur in specific weather and operational
patterns, and over a specified period, which is not necessarily a
year, whereas annualized savings occur are adjusted to represent a
full year with typical weather and operational use patterns
Annualized savings may be used in reporting savings to customers,
including, facility owners, facility occupants, facility managers,
facility operators, or the like, or for regulatory purposes where
utilities should not be penalized or benefit from weather or
operational patterns over which they have no control Annualized
savings may be expressed in the same units as observed savings.
[0039] As used herein, the term "expected savings" refers to a
measurement of the amount of post-treatment reduction in service at
a treatment facility that is expected over the course of a typical
year. Expected savings are typically calculated as a part of
performing and inspecting a treatment, and they assume typical
weather and operations over the course of a year. Expected savings
may be expressed in the same units as observed savings.
[0040] As used herein, the term, "comparison savings" refers to a
measurement of the amount service reduction observed at a
comparison facility over time period. In some cases, comparison
savings may be used to determine if there may be performance
profile bias for a particular facility and/or project. Comparison
savings may be expressed in units appropriate for the utility
service that is being measured, such as, kilowatt hours (kWh) for
energy services.
[0041] As used herein, the term "realization rate" refers to the
ratio of the actual project or program savings (e.g., the actual
utility service demand reduction) to the expected and/or predicted
project or program savings (e.g., utility service demand
reduction). The realization rate may be determined for a project or
for a program. When determining the realization rate of a program,
the determination may be made based on some or all of the projects
in a utility program.
[0042] Briefly stated, various embodiments are directed towards
real time provisional evaluation of utility program performance in
terms of actual reduction in utility service demand as is or would
be observable by utility service metering. In at least one of the
various embodiments, actual utility service demand reduction is
determined by observing the ratio of the actual savings to the
expected savings for projects whose savings are observable, then
using that ratio to calculate the likely actual savings for all
projects in a program, regardless of the observability of their
savings.
[0043] In at least one of the various embodiments, observable
project savings may be determined by comparing a prediction of
usage based on the pre-treatment relationship between weather,
operations, and usage to the actual usage in the post-treatment
period. In at least one of the various embodiments, pre-treatment
utility service demand information (e.g. pre-treatment energy
usage, in the case of an energy utility), pre-treatment operational
data, and pre-treatment weather information may be used to create a
performance profile that can be used to predict utility service
demand based on weather and operations. In at least one of the
various embodiments, the historical information used to generate
the performance profile may be based on at least a portion of
historical usage information and at least a portion of the
historical weather information and the historical operational
information that corresponds to each respective treatment facility.
In at least one of the various embodiments, the predicted usage in
the post-treatment period is calculated by combining the
performance profile with the current weather and operational
information to predict service demand in the post-treatment period.
In at least one of the various embodiments, the observable savings
during the post-treatment period can then be determined by
comparing the predicted usage to the actual usage.
[0044] In at least one of the various embodiments, expected savings
may be aggregated using the savings confidence as a weighting so as
to factor projects whose service demand reduction should be more
observable more heavily than projects which are not as observable.
In at least one of the various embodiments, the savings confidence
may be calculated for each project with observable savings
information by comparing observable weather and operational use to
the weather and operational use in a typical year. In at least one
of the various embodiments, the confidence may be multiplied by the
expected annual savings, and the weighted values are added when
obtaining the expected program savings. The observable savings,
which may be normally aggregated (added), may then be compared to
this weighted value which reflects the portion of the typical year
that has been observed.
[0045] In at least one of the various embodiments, project savings
information for a reduction in utility service demand for each
treatment facility may be generated such that the project savings
information may be based on at least completion of at least one
project for a treatment facility and includes a comparison of the
reduced utility service demand for the treatment facility to
utility service demand for at least one comparison facility. In at
least one of the various embodiments, the project savings
information may be generated based on a comparison of the at least
pre-treatment usage information and the at least portion of current
usage information. In at least one of the various embodiments, the
project savings information may be modified to account for the
comparison savings value. Also, in at least one of the various
embodiments, the project savings information may be modified based
on a comparison savings value that has been adjusted by a
comparison weighting value.
[0046] In at least one of the various embodiments, a program
evaluation report that may be based on execution of at least a
portion of the projects for at least a portion of the treatment
facilities may be generated, wherein the program evaluation report
may include at least program realization information that may be
based on at least an aggregation of project savings information
that may be generated for each treatment facility treated by each
project.
[0047] Also, in at least one of the various embodiments, generating
a program evaluation report may include using one or more
comparison facilities. Accordingly, in at least one of the various
embodiments, another performance profile may be generated for each
comparison facility. Also, in at least one of the various
embodiments, comparison savings information may be generated based
on the other performance profile and the at least portion of
current weather information. In at least one of the various
embodiments, the project savings information may be modified to
account for the comparison savings value.
[0048] In at least one of the various embodiments, one or more
comparison facilities corresponding to the treatment facility may
be determined based on one or more characteristics of the treatment
facility. In at least one of the various embodiments, realization
rate information, including a realization rate, may be generated
based on at least expected program savings information and observed
or net program savings information. Also, in at least one of the
various embodiments, at least a portion of project information may
be updated based on a program recommendation, wherein the program
recommendation may be based on at least the historical information
and the at least current usage information.
Illustrative Operating Environment
[0049] FIG. 1 shows components of one embodiment of an environment
in which embodiments of the invention may be practiced. Not all of
the components may be required to practice the invention, and
variations in the arrangement and type of the components may be
made without departing from the spirit or scope of the invention.
As shown, system 100 of FIG. 1 includes local area networks
(LANs)/wide area networks (WANs)-(network) 110, wireless network
108, client computers 102-106, Real-time Provisioning Evaluation
(RPE) Server Computer 112, and Source Data Server Computers
114-116.
[0050] At least one embodiment of client computers 102-106 is
described in more detail below in conjunction with FIG. 2. In one
embodiment, at least some of client computers 102-106 may operate
over a wired and/or wireless network, such as networks 110 and/or
108. Generally, client computers 102-106 may include virtually any
computing computer capable of communicating over a network to send
and receive information, perform various online activities, offline
actions, or the like. In one embodiment, one or more of client
computers 102-106 may be configured to operate within a business or
other entity to perform a variety of services for the business or
other entity. For example, client computers 102-106 may be
configured to operate as a web server, an accounting server, a
billing server, a usage metering server, or the like. However,
client computers 102-106 are not constrained to these services and
may also be employed, for example, as an end-user computing node,
in other embodiments. It should be recognized that more or less
client computers may be included within a system such as described
herein, and embodiments are therefore not constrained by the number
or type of client computers employed.
[0051] Computers that may operate as client computer 102 may
include computers that typically connect using a wired or wireless
communications medium such as personal computers, multiprocessor
systems, microprocessor-based or programmable electronic devices,
network PCs, or the like. In some embodiments, client computers
102-106 may include virtually any portable computer capable of
connecting to another computer and receiving information such as,
laptop computer 103, smart mobile telephone 104, tablet computers
105, usage metering computer 106, or the like. However, portable
computers are not so limited and may also include other portable
computers such as cellular telephones, display pagers, radio
frequency (RF) devices, infrared (IR) devices, Personal Digital
Assistants (PDAs), handheld computers, wearable computers,
integrated devices combining one or more of the preceding
computers, or the like. As such, client computers 102-106 typically
range widely in terms of capabilities and features. Moreover,
client computers 102-106 may access various computing applications,
including a browser, or other web-based application.
[0052] A web-enabled client computer may include a browser
application that is configured to receive and to send web pages,
web-based messages, and the like. The browser application may be
configured to receive and display graphics, text, multimedia, and
the like, employing virtually any web-based language, including a
wireless application protocol messages (WAP), and the like. In one
embodiment, the browser application is enabled to employ Handheld
Device Markup Language (HDML), Wireless Markup Language (WML),
WMLScript, JavaScript, Standard Generalized Markup Language (SGML),
HyperText Markup Language (HTML), eXtensible Markup Language (XML),
JavaScript Object Notation (JSON), or the like, to display and send
a message. In one embodiment, a user of the client computer may
employ the browser application to perform various activities over a
network (online). However, another application may also be used to
perform various online activities.
[0053] Client computers 102-106 also may include at least one other
client application that is configured to receive and/or send
content between another computer. The client application may
include a capability to send and/or receive content, or the like.
The client application may further provide information that
identifies itself, including a type, capability, name, and the
like. In one embodiment, client computers 102-106 may uniquely
identify themselves through any of a variety of mechanisms,
including an Internet Protocol (IP) address, a phone number, Mobile
Identification Number (MIN), an electronic serial number (ESN), or
other device identifier. Such information may be provided in a
network packet, or the like, sent between other client computers,
RPE Service Computer 112, Source Data Server Computer 114 and 116,
or other computers.
[0054] Client computers 102-106 may further be configured to
include a client application that enables an end-user to log into
an end-user account that may be managed by another computer, such
as RPE server computer 112, source data server computers 114-116,
or the like. Such an end-user account, in one non-limiting example,
may be configured to enable the end-user to manage one or more
online activities, including in one non-limiting example, search
activities, social networking activities, browse various websites,
communicate with other users, or the like. However, participation
in such online activities may also be performed without logging
into the end-user account.
[0055] Wireless network 108 is configured to couple client
computers 103-106 and its components with network 110. Wireless
network 108 may include any of a variety of wireless sub-networks
that may further overlay stand-alone ad-hoc networks, and the like,
to provide an infrastructure-oriented connection for client
computers 103-106. Such sub-networks may include mesh networks,
Wireless LAN (WLAN) networks, cellular networks, and the like. In
one embodiment, the system may include more than one wireless
network.
[0056] Wireless network 108 may further include an autonomous
system of terminals, gateways, routers, and the like connected by
wireless radio links, and the like. These connectors may be
configured to move freely and randomly and organize themselves
arbitrarily, such that the topology of wireless network 108 may
change rapidly.
[0057] Wireless network 108 may further employ a plurality of
access technologies including 2nd (2G), 3rd (3G), 4th (4G) 5th (5G)
generation radio access for cellular systems, WLAN, Wireless Router
(WR) mesh, and the like. Access technologies such as 2G, 3G, 4G,
5G, and future access networks may enable wide area coverage for
mobile computers, such as client computers 103-105 with various
degrees of mobility. In one non-limiting example, wireless network
108 may enable a radio connection through a radio network access
such as Global System for Mobil communication (GSM), General Packet
Radio Services (GPRS), Enhanced Data GSM Environment (EDGE), code
division multiple access (CDMA), time division multiple access
(TDMA), Wideband Code Division Multiple Access (WCDMA), High Speed
Downlink Packet Access (HSDPA), Long Term Evolution (LTE), and the
like. In essence, wireless network 108 may include virtually any
wireless communication mechanism by which information may travel
between client computers 103-105 and another computer, network, and
the like.
[0058] Network 110 is configured to couple network computers with
other computers, including, RPE server computer 112, source data
server computers 114-116, client computer 102, usage metering
computer 106, client computers 103-105 through wireless network
108, or the like.
[0059] Network 110 is enabled to employ any form of computer
readable media for communicating information from one electronic
device to another. Also, network 110 can include the Internet in
addition to local area networks (LANs), wide area networks (WANs),
direct connections, such as through a universal serial bus (USB)
port, other forms of computer-readable media, or any combination
thereof. On an interconnected set of LANs, including those based on
differing architectures and protocols, a router acts as a link
between LANs, enabling messages to be sent from one to another. In
addition, communication links within LANs typically include twisted
wire pair or coaxial cable, while communication links between
networks may utilize analog telephone lines, full or fractional
dedicated digital lines including T1, T2, T3, and T4, and/or other
carrier mechanisms including, for example, E-carriers, Integrated
Services Digital Networks (ISDNs), Digital Subscriber Lines (DSLs),
wireless links including satellite links, or other communications
links known to those skilled in the art. Moreover, communication
links may further employ any of a variety of digital signaling
technologies, including without limit, for example, DS-0, DS-1,
DS-2, DS-3, DS-4, OC-3, OC-12, OC-48, or the like. Furthermore,
remote computers and other related electronic devices could be
remotely connected to either LANs or WANs via a modem and temporary
telephone link. In one embodiment, network 110 may be configured to
transport information of an Internet Protocol (IP).
[0060] Additionally, communication media typically embodies
computer readable instructions, data structures, program modules,
or other transport mechanism and includes any information delivery
media. By way of example, communication media includes wired media
such as twisted pair, coaxial cable, fiber optics, wave guides, and
other wired media and wireless media such as acoustic, RF,
infrared, and other wireless media.
[0061] One embodiment of RPE server computer 112 is described in
more detail below in conjunction with FIG. 3. Briefly, however, RPE
server computer 112 includes virtually any network computer capable
of generating realization rate information for one or more utility
service efficiency programs. Such realization rate information may
be based on source data provided from multiple sources such as data
provided by source data server computers 114-116. Computers that
may be arranged to operate as RPE server computer 112 include
various network computers, including, but not limited to personal
computers, desktop computers, multiprocessor systems,
microprocessor-based or programmable consumer electronics, network
PCs, server computers, network appliances, and the like.
[0062] Although FIG. 1 illustrates RPE server computer 112 as a
single computer, the invention is not so limited. For example, one
or more functions of the RPE server computer 112 may be distributed
across one or more distinct network computers. Moreover, RPE server
computer 112 is not limited to a particular configuration. Thus, in
one embodiment, RPE server computer 112 may contain a plurality of
network computers. In another embodiment, RPE server computer 112
may represent a plurality of network computers that operate using a
master/slave approach, where one of the plurality of network
computers of RPE server computer 112 operates to manage and/or
otherwise coordinate operations of the other network computers. In
other embodiments, the RPE server computer 112 may operate as a
plurality of network computers within a cluster architecture, a
peer-to-peer architecture, and/or even within a cloud architecture.
Thus, the invention is not to be construed as being limited to a
single environment, and other configurations, and architectures are
also envisaged.
[0063] One embodiment of source data server computer 114 is
described in more detail below in conjunction with FIG. 3. Briefly,
however, source data server computer 114 includes virtually any
network computer capable of providing source data to RPE server
computer 112. Source data server computer 114 can be any computer
arranged to provide utility service data, which may include, but is
not limited to, energy usage data, weather data, project data,
manufacturing data, or the like. Computers that may be arranged to
operate as source data server computer 114 may include various
network computers, including, but not limited to personal
computers, desktop computers, multiprocessor systems,
microprocessor-based or programmable consumer electronics, network
PCs, server computers, network appliances, and the like.
[0064] Although FIG. 1 illustrates source data server computer 114
as a single computer, the invention is not so limited. For example,
one or more functions of the source data server computer 114 may be
distributed across one or more distinct network computers.
Moreover, source data server computer 114 is not limited to a
particular configuration. Thus, in one embodiment, source data
server computer 114 may contain a plurality of network computers.
In another embodiment, source data server computer 114 may
represent a plurality of network computers where one of the
plurality of network computers operates to manage and/or otherwise
coordinate operations of the other network computers. In other
embodiments, the source data server computer 114 may operate as a
plurality of network computers within a cluster architecture, a
peer-to-peer architecture, and/or even within a cloud architecture.
Thus, the invention is not to be construed as being limited to a
single environment, and other configurations, and architectures are
also envisaged. Also, source data server computer 116 may be
similar to source data server computer 116. While source data
server computer 116 may provide data from different sources than
source data server computer 114, in least one of the various
embodiments, its description may substantially similar to source
data server computer 114.
Illustrative Client Computer
[0065] FIG. 2 shows one embodiment of client computer 200 that may
be included in a system implementing embodiments of the invention.
Client computer 200 may include many more or less components than
those shown in FIG. 2. However, the components shown are sufficient
to disclose an illustrative embodiment for practicing the present
invention. Client computer 200 may represent, for example, one
embodiment of at least one of client computers 102-106 of FIG.
1.
[0066] As shown in the figure, client computer 200 includes a
processor 202 in communication with a mass memory 226 via a bus
234. In some embodiments, processor 202 may include one or more
central processing units (CPU). Client computer 200 also includes a
power supply 228, one or more network interfaces 236, an audio
interface 238, a display 240, a keypad 242, an illuminator 244, a
video interface 246, an input/output interface 248, a haptic
interface 250, and a global positioning system (GPS) receiver
232.
[0067] Power supply 228 provides power to client computer 200. A
rechargeable or non-rechargeable battery may be used to provide
power. The power may also be provided by an external power source,
such as an alternating current (AC) adapter or a powered docking
cradle that supplements and/or recharges a battery.
[0068] Client computer 200 may optionally communicate with a base
station (not shown), or directly with another computer. Network
interface 236 includes circuitry for coupling client computer 200
to one or more networks, and is constructed for use with one or
more communication protocols and technologies including, but not
limited to, GSM, CDMA, TDMA, GPRS, EDGE, WCDMA, HSDPA, LTE, user
datagram protocol (UDP), transmission control protocol/Internet
protocol (TCP/IP), short message service (SMS), WAP, ultra wide
band (UWB), IEEE 802.16 Worldwide Interoperability for Microwave
Access (WiMax), session initiated protocol/real-time transport
protocol (SIP/RTP), or any of a variety of other wireless
communication protocols. Network interface 236 is sometimes known
as a transceiver, transceiving device, or network interface card
(NIC).
[0069] Audio interface 238 is arranged to produce and receive audio
signals such as the sound of a human voice. For example, audio
interface 238 may be coupled to a speaker and microphone (not
shown) to enable telecommunication with others and/or generate an
audio acknowledgement for some action.
[0070] Display 240 may be a liquid crystal display (LCD), gas
plasma, light emitting diode (LED), organic LED, or any other type
of display used with a computer. Display 240 may also include a
touch sensitive screen arranged to receive input from an object
such as a stylus or a digit from a human hand.
[0071] Keypad 242 may comprise any input device arranged to receive
input from a user. For example, keypad 242 may include a push
button numeric dial, or a keyboard. Keypad 242 may also include
command buttons that are associated with selecting and sending
images.
[0072] Illuminator 244 may provide a status indication and/or
provide light. Illuminator 244 may remain active for specific
periods of time or in response to events. For example, when
illuminator 244 is active, it may backlight the buttons on keypad
242 and stay on while the client computer is powered. Also,
illuminator 244 may backlight these buttons in various patterns
when particular actions are performed, such as dialing another
client computer. Illuminator 244 may also cause light sources
positioned within a transparent or translucent case of the client
computer to illuminate in response to actions.
[0073] Video interface 246 is arranged to capture video images,
such as a still photo, a video segment, an infrared video, or the
like. For example, video interface 246 may be coupled to a digital
video camera, a web-camera, or the like. Video interface 246 may
comprise a lens, an image sensor, and other electronics. Image
sensors may include a complementary metal-oxide-semiconductor
(CMOS) integrated circuit, charge-coupled device (CCD), or any
other integrated circuit for sensing light.
[0074] Client computer 200 also comprises input/output interface
248 for communicating with external devices, such as a headset, or
other input or output devices not shown in FIG. 2. Input/output
interface 248 can utilize one or more communication technologies,
such as USB, infrared, Bluetooth.TM., or the like.
[0075] Haptic interface 250 is arranged to provide tactile feedback
to a user of the client computer. For example, the haptic interface
250 may be employed to vibrate client computer 200 in a particular
way when another user of a computer is calling. In some
embodiments, haptic interface 250 may be optional.
[0076] Client computer 200 may also include GPS transceiver 232 to
determine the physical coordinates of client computer 200 on the
surface of the Earth. GPS transceiver 232, in some embodiments, may
be optional. GPS transceiver 232 typically outputs a location as
latitude and longitude values. However, GPS transceiver 232 can
also employ other geo-positioning mechanisms, including, but not
limited to, triangulation, assisted GPS (AGPS), Enhanced Observed
Time Difference (E-OTD), Cell Identifier (CI), Service Area
Identifier (SAI), Enhanced Timing Advance (ETA), Base Station
Subsystem (BSS), or the like, to further determine the physical
location of client computer 200 on the surface of the Earth. It is
understood that under different conditions, GPS transceiver 232 can
determine a physical location within millimeters for client
computer 200; and in other cases, the determined physical location
may be less precise, such as within a meter or significantly
greater distances. In one embodiment, however, client computer 200
may through other components, provide other information that may be
employed to determine a physical location of the computer,
including for example, a Media Access Control (MAC) address, IP
address, or the like.
[0077] Mass memory 226 includes a Random Access Memory (RAM) 204, a
Read-only Memory (ROM) 222, and other storage means. Mass memory
226 illustrates an example of computer readable storage media
(devices) for storage of information such as computer readable
instructions, data structures, program modules or other data. Mass
memory 226 stores a basic input/output system (BIOS) 224 for
controlling low-level operation of client computer 200. The mass
memory also stores an operating system 206 for controlling the
operation of client computer 200. It will be appreciated that this
component may include a general-purpose operating system such as a
version of UNIX, or LINUX.TM., or a specialized client
communication operating system such as Microsoft Corporation's
Windows Mobile.TM., Apple Corporation's iOS.TM., Google
Corporation's Android.TM. or the Symbian.RTM. operating system. The
operating system may include, or interface with a Java virtual
machine module that enables control of hardware components and/or
operating system operations via Java application programs.
[0078] Mass memory 226 further includes one or more data storage
208, which can be utilized by client computer 200 to store, among
other things, applications 214 and/or other data. For example, data
storage 208 may also be employed to store information that
describes various capabilities of client computer 200. The
information may then be provided to another computer based on any
of a variety of events, including being sent as part of a header
during a communication, sent upon request, or the like. Data
storage 208 may also be employed to store social networking
information including address books, buddy lists, aliases, user
profile information, or the like. Further, data storage 208 may
also store messages, web page content, or any of a variety of user
generated content. At least a portion of the information stored in
data storage 208 may also be stored on another component of client
computer 200, including, but not limited to processor readable
storage media 230, a disk drive or other computer readable storage
devices (not shown) within client computer 200.
[0079] Processor readable storage media 230 may include volatile,
nonvolatile, removable, and non-removable media implemented in any
method or technology for storage of information, such as computer-
or processor-readable instructions, data structures, program
modules, or other data. Examples of computer readable storage media
include RAM, ROM, Electrically Erasable Programmable Read-only
Memory (EEPROM), flash memory or other memory technology, Compact
Disc Read-only Memory (CD-ROM), digital versatile disks (DVD) or
other optical storage, magnetic cassettes, magnetic tape, magnetic
disk storage or other magnetic storage devices, or any other
physical medium which can be used to store the desired information
and which can be accessed by a computer. Processor readable storage
media 230 may also be referred to herein as computer readable
storage media and/or computer readable storage device.
[0080] Applications 214 may include computer executable
instructions which, when executed by client computer 200, transmit,
receive, and/or otherwise process network data. Network data may
include, but is not limited to, messages (e.g. SMS, Multimedia
Message Service (MMS), instant message (IM), email, and/or other
messages), audio, video, and enable telecommunication with another
user of another client computer. Applications 214 may include, for
example, a browser 218, and other applications 220.
[0081] Browser 218 may include virtually any application configured
to receive and display graphics, text, multimedia, messages, and
the like, employing virtually any web based language. In one
embodiment, the browser application is enabled to employ HDML, WML,
WMLScript, JavaScript, SGML, HTML, XML, and the like, to display
and send a message. However, any of a variety of other web-based
programming languages may be employed. In one embodiment, browser
218 may enable a user of client computer 200 to communicate with
another network computer, such as RPE server computer 112 of FIG.
1.
[0082] Other applications 220 may include, but are not limited to,
calendars, search programs, email clients, IM applications, SMS
applications, voice over Internet Protocol (VOIP) applications,
contact managers, task managers, transcoders, database programs,
word processing programs, security applications, spreadsheet
programs, games, search programs, and so forth.
Illustrative Network Computer
[0083] FIG. 3 shows one embodiment of a network computer 300,
according to one embodiment of the invention. Network computer 300
may include many more or less components than those shown. The
components shown, however, are sufficient to disclose an
illustrative embodiment for practicing the invention. Network
computer 300 may be configured to operate as a server, client,
peer, a host, node, or any other computer. Network computer 300 may
represent, for example RPE server computer 112, source data server
computer 114, and/or source data server computer 116 of FIG. 1,
and/or other network computers.
[0084] Network computer 300 includes processor 302, processor
readable storage media 328, network interface unit 330, an
input/output interface 332, hard disk drive 334, video display
adapter 336, and memory 326, all in communication with each other
via bus 338. In some embodiments, processor 302 may include one or
more central processing units.
[0085] As illustrated in FIG. 3, network computer 300 also can
communicate with the Internet, or some other communications
network, via network interface unit 330, which is constructed for
use with various communication protocols including the TCP/IP
protocol. Network interface unit 330 is sometimes known as a
transceiver, transceiving device, or network interface card
(NIC).
[0086] Network computer 300 also comprises input/output interface
332 for communicating with external devices, such as a keyboard, or
other input or output devices not shown in FIG. 3. Input/output
interface 332 can utilize one or more communication technologies,
such as USB, infrared, Bluetooth.TM., or the like.
[0087] Memory 326 generally includes RAM 304, ROM 322 and one or
more permanent mass storage devices, such as hard disk drive 334,
tape drive, optical drive, and/or floppy disk drive. Memory 326
stores operating system 306 for controlling the operation of
network computer 300. Any general-purpose operating system may be
employed. Basic input/output system (BIOS) 324 is also provided for
controlling the low-level operation of network computer 300.
[0088] Although illustrated separately, memory 326 may include
processor readable storage media 328. Processor readable storage
media 328 may be referred to and/or include computer readable
media, computer readable storage media, and/or processor readable
storage device. Processor readable storage media 328 may include
volatile, nonvolatile, removable, and non-removable media
implemented in any method or technology for storage of information,
such as computer readable instructions, data structures, program
modules, or other data. Examples of processor readable storage
media include RAM, ROM, EEPROM, flash memory or other memory
technology, CD-ROM, digital versatile disks (DVD) or other optical
storage, magnetic cassettes, magnetic tape, magnetic disk storage
or other magnetic storage devices, or any other media which can be
used to store the desired information and which can be accessed by
a computer.
[0089] Memory 326 further includes one or more data storage 308,
which can be utilized by network computer 300 to store, among other
things, applications 314 and/or other data. For example, data
storage 308 may also be employed to store information that
describes various capabilities of network computer 300. The
information may then be provided to another computer based on any
of a variety of events, including being sent as part of a header
during a communication, sent upon request, or the like. Data
storage 308 may also be employed to store messages, web page
content, or the like. At least a portion of the information may
also be stored on another component of network computer 300,
including, but not limited to processor readable storage media 328,
hard disk drive 334, or other computer readable storage medias (not
shown) within network computer 300.
[0090] Data storage 308 may include a database, text, spreadsheet,
folder, file, or the like, that may be configured to maintain and
store user account identifiers, user profiles, email addresses, IM
addresses, and/or other network addresses; or the like. Data
storage 308 may further include program code, data, algorithms, and
the like, for use by a processor, such as processor 302 to execute
and perform actions. In one embodiment, at least some of data store
308 might also be stored on another component of network computer
300, including, but not limited to processor-readable storage media
328, hard disk drive 334, or the like.
[0091] Data storage 308 may include project data 380, population
data 382, usage data 384, weather data 386, and/or operations data
388. Project data 380 may include project details, timeline, and
expected utility service demand reduction data for one or more
facilities. Population data 382 may include information related to
candidate facilities which may be used as comparison facilities,
including attributes which may be used to determine similarity to
treatment facilities. Usage data 384 may include information
related to the utility service demand for some or all treatment
facilities and candidate comparison facilities. Weather data 386
may include information about the weather experienced by treatment
facilities and comparison facilities. Operations data 388
(operational data) may include information from one or more sources
of data about aspects of the operations of treatment facilities and
comparison facilities that may impact their service demand
requirements.
[0092] Applications 314 may include computer executable
instructions, which may be loaded into mass memory and run on
operating system 306. Examples of application programs may include
transcoders, schedulers, calendars, database programs, word
processing programs, Hypertext Transfer Protocol (HTTP) programs,
customizable user interface programs, IPSec applications,
encryption programs, security programs, SMS message servers, IM
message servers, email servers, account managers, and so forth.
Applications 314 may also include website server 318, real-time
provisioning evaluation (RPE) application 319, usage metering
application 392, profile application 320, or project management
application 323.
[0093] Website server 318 may represents any of a variety of
information and services that are configured to provide content,
including messages, over a network to another computer. Thus,
website server 318 can include, for example, a web server, a File
Transfer Protocol (FTP) server, a database server, a content
server, or the like. Website server 318 may provide the content
including messages over the network using any of a variety of
formats including, but not limited to WAP, HDML, WML, SGML, HTML,
XML, Compact HTML (cHTML), Extensible HTML (xHTML), or the
like.
Illustrative Logical System
[0094] FIG. 4 shows a logical diagram of system 400 for real-time
provisional evaluation of realization in accordance with at least
one of the various embodiments.
[0095] In at least one of the various embodiments, usage
information from usage data sources 402, project information from
project data sources 404, weather information from weather data
sources 406, and optionally other information from other data
sources 408, may be provided over network 410 to program treatment
group component 412 and program comparison group component 414.
[0096] In at least one of the various embodiments, as facilities
are determined to be eligible for one or more treatments that may
be associated with a project, the RPE application may be arranged
to associate them with one or more program treatment groups, such
as, program treatment group 412, for the each particular project.
In at least one of the various embodiments, RPE application 319
and/or project management application 323 may be arranged to
maintain information sufficient for determining the time a project
treatment occurred. Such information may be stored in a database or
other storage, such as, project data 380. In at least one of the
various embodiments, information related to the specifics of the
project and its performance may be included in project information
from project data sources 404.
[0097] In at least one of the various embodiments, for each
facility associated with the program treatment group 412, a
pre-treatment performance profile may be generated by pre-treatment
performance profile component 420 and/or profile application 320.
In at least one of the various embodiments, each pre-treatment
performance profile may be stored in project data 380, one or more
databases, or any other form of data storage. In at least one of
the various embodiments, the pre-treatment performance profile for
a facility may be generated based on at least historical usage
information from usage data sources 402 and historical weather
information from weather data sources 406.
[0098] In at least one of the various embodiments, observable
savings component 422 may be arranged to generate and/or store one
or more values that may represent the demand service reduction
(savings) for each facility that may be attributable to the
treatment. In at least one of the various embodiments, savings
confidence component 452 may be arranged to generate and/or store
one or more values that may represent the savings confidence for
the observable savings 422. In at least one of the various
embodiments, RPE application 319 may be arranged to perform some or
all of the actions associated with observable savings component 422
and savings confidence 452. In at least one of the various
embodiments, expected savings component 450 may be arranged to
generate and/or store one or more values that may represent the
demand service reduction (savings) that are expected for each
project at each facility as a result of the treatment. Additional
components in system 400 may contribute to the generation of a
treatment savings value, such as, usage data sources 402, weather
data sources 406, project data sources 404, operations data sources
408, pre-treatment performance profile 420, or the like. In at
least one of the various embodiments, an annualized savings
component 454 may generate and/or store a typical annual savings
amount associated with the project, which may be based on the
observable savings 422 and savings confidence 452.
[0099] In at least one of the various embodiments, if a facility is
associated with the program treatment group 412, one or more
comparison facilities that correspond to the treatment facility may
be determined. In at least one of the various embodiments,
comparison facilities may be determined and/or grouped by
comparison group members 424.
[0100] In at least one of the various embodiments, the utility
service usage of each comparison facility associated with a
treatment facility may be split into pre-treatment comparison
component 426 and post-treatment comparison component 428 based on
the date of the treatment of the corresponding treatment facility.
Thus, if a comparison facility's corresponding treatment facility
is associated with pre-treatment component 416 prior to a given
date, the comparison facility may be associated with pre-treatment
comparison component 426 from prior to the same date. Likewise, if
a comparison facility's corresponding treatment facility is
associated with post-treatment component 418 after a given date,
the comparison facility may be associated with post-treatment
comparison component 428 after the same date.
[0101] In at least one of the various embodiments, for each
comparison facility associated with the program comparison group
414, a comparison pre-treatment performance profile may be
generated by comparison pre-treatment performance profile component
430. In at least one of the various embodiments, each comparison
pre-treatment performance profile ("comparison profile") may be
stored in population data 382, one or more databases, or any other
form of data storage. In at least one of the various embodiments,
the comparison profile for a comparison facility may be generated
based on at least historical usage information from usage data
sources 402 historical weather information from weather data
sources 406 and operations data from operations data sources
408.
[0102] In at least one of the various embodiments, comparison
savings/drift component 432 may be arranged to generate and/or
store one or more values that may represent the usage savings for
each comparison facility. In at least one of the various
embodiments, the comparison weighting component 466 may generate
and/or store one or more values that may represent the portion of a
treatment facility that each comparison facility represents. For
example, if a comparison facility is one of three comparison
facilities associated with a single treatment facility, then the
weight of that comparison facility may be calculated to be 1/3.
Various components in system 400 may contribute to the generation
of a comparison savings value, such as, usage data sources 402,
weather data sources 406, project data sources 404, other data
sources 408, comparison pre-treatment performance profile 430, or
the like.
[0103] In at least one of the various embodiments, initially the
facility may be associated with program comparison group 414. If a
treatment is completed on a facility associated with program
comparison group 414, it may be re-assigned to program treatment
group 412.
[0104] In at least one of the various embodiments, observable
savings 422 associated with each project at each treatment facility
may be aggregated by the observable savings aggregations component
472. In at least one of the various embodiments, expected savings
450 associated with each project at each treatment facility may be
weighted by savings confidence 452 and aggregated by the weighted
expected savings aggregations component 474. In at least one of the
various embodiments, comparison savings 432 associated with each
comparison facility are weighted by the comparison weighting 466
and aggregated by the weighted comparison savings aggregations
component 476.
[0105] In at least one of the various embodiments, program
realization rate component 434 may be arranged to determine at
least one or more realization rates for one or more projects and/or
programs. In at least one of the various embodiments, the
realization rate may be determined and/or updated as the one or
more projects or programs progress. In at least one of the various
embodiments, program realization rates may be calculated as the
ratio of observable savings aggregations 472, adjusted for weighted
comparison savings aggregations 476, to the weighted expected
savings aggregations 474.
[0106] In at least one of the various embodiments, the realization
rate of projects sharing various attributes 482 may be calculated.
In at least one of the various embodiments, diagnosis analytics
component 436 may be arranged to combine the realization rate of
projects sharing various attributes 482 and the program realization
rate 434 to generate and/or store one or more attributes of
projects that may be associated with projects, facilities, or the
like, possessing realization rates substantially higher or
substantially lower than typical for the program.
[0107] In at least one of the various embodiments, RPE application
319, profile application 320, and project management application
323 may be arranged to perform one or more of the actions performed
by the components of system 400.
[0108] FIG. 5 shows graph 500 for comparing pre-treatment usage
predicted by a pre-treatment performance profile and current usage
information for determining treatment savings information for a
project in accordance with at least one of the various embodiments.
The dashed line, line 502, indicates the expected usage of the
facility generated using the pre-treatment performance profile and
the current weather information. The solid line, line 504,
indicates the actual usage for the facility generate based on
current usage information. The treatment savings for the project
may be determine based in part on a comparison of line 502 and line
504. Accordingly, in at least one of the various embodiments, the
treatment savings for the project may be determined based in part
on the volume between line 502 and line 504.
Generalized Operation
[0109] The operation of certain aspects of the invention will now
be described with respect to FIGS. 6-12. In at least one of various
embodiments, processes 600, 700, 800, 900, 1000, 1100, and 1200
described in conjunction with FIGS. 6-12, respectively, may be
implemented by and/or executed on a single network computer, such
as network computer 300 of FIG. 3. In other embodiments, these
processes or portions of these processes may be implemented by
and/or executed on a plurality of network computers, such as
network computer 300 of FIG. 3. However, embodiments are not so
limited and various combinations of network computers, or the like,
may be utilized.
[0110] FIGS. 6A, 6B and 6C show an overview flowchart of process
600 for real time provisional evaluation of utility programs in
accordance with at least one of the embodiments. Starting in FIG.
6A, process 600 may be initiated if new project data or new usage
data is available. After a start block, if new project data 601
becomes available, at block 602, in at least one of the various
embodiments, project information for one or more projects may be
determined. In at least one of the various embodiments, project
information may be determined from various sources, including, for
examples, project data sources 404.
[0111] In at least one of the various embodiments, project
information includes one or more characteristics of a project,
including, but not limited to, program start time, program stop
time, facility attributes used to select appropriate comparison
facilities, attributes used to determine eligibility for inclusion
in provisional evaluation of the effectiveness of the utility
efficiency program, or the like. Attributes used to select
appropriate comparison facilities include facility type and
function, occupant characteristics for residential buildings (e.g.,
number and age of occupants, or the like), structural
characteristics of the facility (e.g. square footage, materials,
window area, or the like), insulation, location, usage history, or
the like. In at least one of the various embodiments, facility
eligibility for inclusion in provisional evaluation of the
effectiveness of the utility efficiency program may include
thresholds for characteristics including availability of
pre-treatment usage data, aspects of occupation or usage of the
facility, availability of pre-treatment operational data, or the
like.
[0112] In at least one of the various embodiments, project
information may also include information such as expected
performance improvements associated with the project, estimated
number of participants, incentive program information associated
with the projects, or the like.
[0113] At block 604, in at least one of the various embodiments,
one or more new candidate treatment facilities may be determined.
New candidate treatment facilities may be facilities receiving
treatment services from the utility as a part of the program which
had not previously been either identified for inclusion in the
treatment group or determined to be ineligible for inclusion in the
provisional evaluation of the effectiveness of the utility
efficiency program. New candidate treatment facilities may be
recorded, tagged and/or marked as a candidate treatment facility,
the record of which may be stored in computer storage (e.g., data
storage 308), a database, or the like, before control flows to
decision block 606.
[0114] At decision block 606, in at least one of the various
embodiments, if the candidate treatment facility may be eligible
for provisional evaluation, control may flow to block 608;
otherwise the candidate treatment facility may be recorded as being
ineligible for inclusion in the provisional evaluation of the
effectiveness of the utility efficiency program, but is otherwise
ignored by system. In at least one of the various embodiments, a
candidate treatment facility may be determined to be eligible for
real time provisional evaluation if it meets certain criteria. Note
that criteria for participating in the program may be separate from
criteria for being eligible for inclusion in the provisional
evaluation of the effectiveness of the utility efficiency
program.
[0115] In at least one of the various embodiments, a candidate
treatment facility may be eligible for inclusion in the provisional
evaluation of the effectiveness of the utility efficiency program
if there is historical usage information sufficient for generating
a pre-treatment performance profile. In at least one of the various
embodiments, eligibility may be based on historical usage
information for the facility for over defined length of time, such
as, one month, three months, one year, two years, or the like.
Also, in at least one of the various embodiments, an eligibility
requirement may include that the historical usage information may
be from the same occupants over the defined length of time. For
example, assuming eligibility requires at least one consecutive
year of historical usage information, if the consecutive one year
of historical information for a facility includes three months of
historical usage information generated for the Smith family and
nine months of historical usage information for generated for the
Lee family, the candidate facility may be excluded from RPE.
[0116] Also, in at least one of the various embodiments, an
eligibility requirement may include that historical operational
information over the defined length of time may be present as well.
Also, in at least one of the various embodiments, an eligibility
requirement may be that historical operational information be from
the same occupants over the defined length of time.
[0117] In at least one of the various embodiments, RPE application
319 may determine if sufficient usage information may be available
by communicating over a network with a computer that may be part of
the utility. In some cases, the RPE application 319 may download
the historical usage information from a utility computer. If the
historical usage information is determined, RPE application 319 may
validate the information and determine if it may be sufficient for
RPE. In at least one of the various embodiments, if the historical
usage information may be insufficient for RPE, the facility may
remain in the program, but it may be excluded from inclusion in the
provisional evaluation.
[0118] At block 608, in at least one of the various embodiments,
facilities determined to be eligible for RPE may be recorded,
marked, and/or tagged as treatment facilities and associated with
the program treatment group associated the program. In at least one
of the various embodiments, RPE application 319 may be arranged to
employ one or more well-known data structures for storing
information related to treatment facilities in a database, or the
like.
[0119] At block 610, in at least one of the various embodiments,
one or more comparison facilities may be determined that correspond
with the treatment facility. In at least one of the various
embodiments, comparison facilities may be determined based on one
or more similarities with the treatment facility. (See, FIG.
7.)
[0120] In at least one of the various embodiments, each time a
program and/or a project is evaluated, different comparison
facilities may be determined to correspond to a treatment facility.
Also, in at least one of the various embodiments, each time a
program and/or a project is evaluated there may be different number
of comparison facilities determined to correspond to a treatment
facility. In some cases, the number of comparison facilities may
change because one or more comparison facilities may be removed
from a comparison group and into the treatment group if they are
determined to become treatment facilities. Next, control may flow
through sheet connector A to the sheet connector A in FIG. 6B.
[0121] In FIG. 6B, at block 612, in at least one of the various
embodiments, pre-treatment performance profiles may be generated
for the treatment facility. In at least one of the various
embodiments, RPE application 319 may be arranged to generate the
pre-treatment performance profile for the treatment facility. (See,
FIG. 8.)
[0122] At block 614, in at least one of the various embodiments,
RPE application 319 may predict post-treatment usage information
based on at least the pre-treatment performance profile and weather
information and/or operational information. The post-treatment
usage prediction may represent the services (e.g., electricity,
water, natural gas, or the like) that the facility would have
consumed absent the treatments associated with the project.
Accordingly, in at least one of the various embodiments, savings
information may be generated based on a comparison of the
pre-treatment usage value and the actual usage value.
[0123] At block 616, in at least one of the various embodiments,
RPE application 319 may determine observable savings information
based on at least the predicted post-treatment usage and the actual
usage information for the post-treatment period. In at least one of
the various embodiments, observable savings information for the
treatment facility may be generated as usage information. The
observable savings value may represent the services (e.g.,
electricity, water, natural gas, or the like) that the facility
would have consumed absent the treatments associated with the
project but did not given the actual usage values.
[0124] At block 618, in at least one of the various embodiments,
RPE application 319 may determine the allocation of the expected
savings among the different load types in a typical year based on
at least the project data. The allocation of the expected savings
among the different load types in a typical year represents the
extent to which a given combination of weather conditions and
operational patterns give rise to the expected savings. This
allocation may be used when comparing the actual post-treatment
weather to a typical year's weather in order to determine how much
savings are observable.
[0125] At block 620, in at least one of the various embodiments,
RPE application 319 may determine the savings confidence based the
load allocation and some combination of the weather information
observed, operational information observed, and time that has
passed during the post-treatment period. The savings confidence
represents the extent to which expected combinations of weather
conditions and operational patterns during a typical year which
give rise to the expected savings have been observed during the
post-treatment period. In at least one of the various embodiments,
the savings confidence may be calculated as:
Savings Confidence=Heating Allocation*(HDD During Post-Treatment
Period/HDD in a typical year)+Cooling Allocation*(CDD During
Post-Treatment Period/CDD in a typical year)+Baseload
Allocation*(length of time during Post-Treatment Period/length of
time in a typical year)
For residential or for treatments which are irrespective of
operational characteristics of the facility.
[0126] For example, a project's treatments may affect a facility
such that service usage is expected to be lower during conditions
that require heating (cold weather) and cooling (hot weather), with
the savings allocation of 25% heating, 75% cooling irrespective of
operational considerations. If the post-treatment period includes
only 1/10.sup.th of the cold weather of a typical year, but
includes three-quarters of the hot weather of a typical year, then
the savings confidence would be.
[0127] For treatments which are impacted by the operations of the
facility (e.g. a pump or motor used in an industrial or
manufacturing process, or lighting treatment which depends on the
extent to which a facility is used for shift work during a period),
a generalized form of this equation must be used:
Savings Confidence.sub.j=.SIGMA.X.sub.i,j*(Heating
Allocation.sub.j*HDD.sub.i/HDD.sub.typical year+Cooling
Allocation.sub.j*CDD.sub.i/CDD.sub.typical year+Base
Allocation.sub.j*time.sub.i/time.sub.typical year)/X.sub.j,typical
year
Summed from observable post-treatment period 1 through n, and
defined for operational or production types 0 through m, where:
[0128] Savings Confidence.sub.j represents the savings confidence
in the savings associated with the jth type of production or
operation and Savings Confidence.sub.0 represents the savings
confidence associated with savings that are irrespective of
production or operation [0129] n represents the number of
observable post-treatment periods for which data is available
[0130] m represents the number of different types of production or
operation which factor into the prediction of usage and which are
modeled as a part of the pre-treatment performance profile [0131]
X.sub.i,j is the amount of operation or production of the jth type,
during the ith time period and X.sub.i,0 is defined to be 1. [0132]
X.sub.j,typical year is the operation or production of the jth type
during the course of a typical year and X.sub.i,typical year is
defined to be 1. [0133] Heating Allocation.sub.j is defined to be
the allocation of the savings for type of production or operation
of the jth type that is expected to occur under heating conditions
and Heating Allocation.sub.0 is defined to be the portion of the
savings that are expected to obtain irregardless of production or
operation during heating conditions. [0134] HDD.sub.i is defined to
be the number of heating degree days (given the facility-specific
base temperature) which were observed during the ith period at the
facility's location. [0135] HDD.sub.typical year is defined to be
the number of heating degree days (given the facility-specific base
temperature) at the facility's location during a typical year.
[0136] Cooling Allocation.sub.j is defined to be the allocation of
the savings for type of production or operation of the jth type
that is expected to occur under cooling conditions and Cooling
Allocation.sub.0 is defined to be the portion of the savings that
are expected to obtain irregardless of production or operation
during cooling conditions. [0137] CDD.sub.i is defined to be the
number of cooling degree days (given the facility-specific base
temperature) which were observed during the ith period at the
facility's location. [0138] CDD.sub.typical year is defined to be
the number of cooling degree days (given the facility-specific base
temperature) at the facility's location during a typical year.
[0139] Base Allocation.sub.j is defined to be the allocation of the
savings for type of production or operation of the jth type that is
expected to occur regardless of weather conditions and Base
Allocation.sub.0 is defined to be the portion of the savings that
are expected to obtain irregardless of both production or operation
and weather conditions. [0140] time.sub.i is defined to be the
length of time (typically expressed in an appropriate unit, such as
minutes, hours, or days) during the ith period. [0141]
time.sub.typical year is defined to be the length of a typical
year.
[0142] This savings confidence can be used when comparing the
actual post-treatment weather to a typical year's weather in order
to determine how much savings are observable.
[0143] At block 622, in at least one of the various embodiments,
the annualized savings information may be determined. In at least
one of the various embodiments, annualized savings information may
be determined based on the observable savings and savings
confidence Annualized savings for all types of operation or
production that gives rise to savings may be given by:
Annualized Savings=Observable Savings/Savings Confidence
[0144] Annualized savings can be used when presenting the likely
project or program savings to users. Next, control may be returned
to a calling process.
[0145] FIG. 6C, shows a portion of process 600, in accordance with
at least one of the various embodiments. After a start block, at
block 624, new usage data may be provided to process 600. Usage
data may be provided from one or more usage data sources, such as,
usage data sources 402. At block 626, in at least one of the
various embodiments, the usage data the relates to the existing
and/or current treatment group facilities may be determined. Next,
control may flow through sheet connector B back to block 614 in
FIG. 6B.
[0146] FIG. 7 shows a flowchart for process 700 for determining
comparison group members in accordance with at least one of the
various embodiments. After a start block, at block 702, in at least
one of the various embodiments, a treatment facility may be
determined. In at least one of the various embodiments, a facility
may be determined to be a treatment facility if it is selected to
participate in one of more projects associated with one or more
service utility performance programs, such as, energy efficiency
programs.
[0147] At block 704, in at least one of the various embodiments,
the characteristics of the treatment facility may be determined. In
at least one of the various embodiments, project information that
may be located in project data sources 404, may include structural
and static characteristics of the treatment facility. In at least
one of the various embodiments, structural characteristics may
include, window quantity, siding material, roofing material,
structure size (e.g., square footage), number of floors, number of
entrances and exits, type of heating plant, type of cooling plant,
foundation type, geographic location, or the like.
[0148] In at least one of the various embodiments, characteristics
corresponding to the demographics of occupants of the treatment
facility may be determined. In at least one of the various
embodiments, demographic characteristics may include, number and
ages of family members living in the facility, income of occupants,
occupant employment status/careers, education level, or the
like.
[0149] In at least one of the various embodiments, characteristics
corresponding to the utility service usage of the facility may be
determined, such as, water consumption, power consumption, or the
like. In most embodiments, the utility will collect metered usage
information for defined periods of time for each facility that
consumes utility services. For example, an electrical power utility
may track the monthly electrical power demand of a facility using
one or more electrical service meters. Usage information, that may
be the same information used by the utility to generate the
bill/invoice for services, may be used to characterize the facility
based on its service demand and/or service usage.
[0150] At block 706, in at least one of the various embodiments,
one or more comparison facilities may be determined based on one or
more of the characteristics of the treatment facility. In at least
one of the various embodiments, facilities may be selected from the
universe of facilities receiving services from the utility.
Comparison facilities may be determined based on one or more
conditions based on the characteristics of the treatment facility.
In at least one of the various embodiments, comparison facility may
be determined such that they may be similar in physical and service
usage characteristics to their corresponding treatment facility. In
at least one of the various embodiments, a plurality of comparison
facilities may be assigned to a particular treatment facility for a
project.
[0151] In at least one of the various embodiments, RPE application
319 may be arranged to match comparison facilities to treatment
facilities using one or more ranges of values for characteristics
rather than an exact match. For example, if a treatment facility
has 2,450 square feet of living space, candidate comparison
facilities may include facility have living space in the range of
2,100-2,600 square feet, or the like. Also, in at least one of the
various embodiments, RPE application 319 may be arranged to employ
a subset of available treatment facilities for determining
comparison facilities. The subset may be determined using
configuration settings, and/or user input. For example, in at least
one of the various embodiments, if a treatment facility has one
hundred known physical characteristics, RPE application 319 may be
arranged and/or configured to use five, or the like, for
determining matches.
[0152] In at least one of the various embodiments, service usage
characteristics of facilities may be used as criteria for
determining comparison facilities. In at least one of the various
embodiments, service usage characteristics may be defined in terms
of individual values and/or a range of values. In at least one of
the various embodiments, service usage characteristics may be
defined in term of bands or tiers, such as, low, medium, and high,
where each band corresponds to a range of service usage. For
example, facilities having an annualized power demand of 1650 kWhs
may be determined to low users, with 3300 kWhs users and 4950 kWhs
users defined as medium and high users respectively.
[0153] In at least one of the various embodiments, in addition to
sufficiently matching the characteristics of the treatment
facility, comparison facilities may be required to have sufficient
historical usage information that exceeds a defined threshold. In
at least one of the various embodiments, comparison facilities may
be required to have historical usage information for a defined time
period, such as, the previous year, or previous two years. In
addition to having historical usage information for a defined time
period, RPE application 319 may be arranged to restrict eligible
comparison facilities to those facilities that have had the same
occupants over the entire defined historical time period.
[0154] In at least one of the various embodiments, certain
regulatory regimes, such as, privacy rules may restrict access to
detailed asset information and usage information of the candidate
comparison facilities. In this type of situation, the treatment
facility may be employed as a proxy for a comparison facility.
Accordingly, in at least one of the various embodiments, RPE
application 319 may be arranged to use historical usage information
and historical wealth information associated with the treatment
facility rather than separate comparison facility information.
[0155] In at least one of the various embodiments, to generate a
proxy comparison facility, RPE application 319 may determine
historical information for the treatment facility that occurs prior
to the treatment facility's historical information that is used for
generating its pre-treatment performance profile. For example, if
historical information for year 2012 is used to generate a
pre-treatment performance profile for the treatment facility,
historical information for year 2011 may be used to generate a
pre-treatment performance profile as a proxy for a comparison
facility profile.
[0156] At block 708, in at least one of the various embodiments,
candidate comparison facilities may be removed from the comparison
group based on characteristics that make them unsuitable for
inclusion in the provisional evaluation. In at least one of the
various embodiments, facilities may be deemed unsuitable for
inclusion in the provisional evaluation because historical usage
information sufficient to establish a pre-"treatment" performance
profile does not exist. In at least one of the various embodiments,
facilities may be deemed unsuitable for inclusion in the
provisional evaluation because their historical or current usage
are deemed aberrant or otherwise unfit to make the facility's usage
an appropriate comparison similar facilities.
[0157] At block 710, in at least one of the various embodiments,
candidate comparison facilities may be removed from the comparison
group because the facility has been included in the treatment group
or otherwise are known to be participating in utility service
reduction treatment.
[0158] At block 712, in at least one of the various embodiments,
the comparison facilities may be associated with the treatment
facility. In at least one of the various embodiments, RPE
application 319 may be arranged to determine one or more comparison
facilities for each treatment facility. In at least one of the
various embodiments, RPE application 319 may be configured to
select as many matching facilities as possible. Though, in some
cases, a single comparison facility may be associated with a
plurality of treatment facilities.
[0159] At block 714, in at least one of the various embodiments, a
comparison weighting factor is determined for each comparison
facility. The comparison weighting factor represents the portion of
a treatment group member that a given comparison facility
represents and may be used when aggregating comparison savings to
appropriately weight comparison savings.
[0160] In at least one of the various embodiments, if there may be
more than one comparison facility associated with a treatment
facility, each the comparison savings information for each
comparison facility may be divided by the total number of
comparison facilities associated with the treatment facilities. For
example, if a comparison facility is one of ten comparison
facilities associated with a treatment facility, the comparison
savings for each individual comparison facility may be divided by
ten to compute its contribution to the overall comparison savings
information for the treatment facility.
[0161] Next, control may be returned to a calling process.
[0162] FIG. 8 shows a flowchart for process 800 for generating
pre-treatment performance profiles in accordance with at least one
of the various embodiments. After a start block, at block 802, in
at least one of the various embodiments, the historical usage
information for a facility may be determined. In at least one of
the various embodiments, usage information may be determined for a
defined length of time, such as, the previous year, previous
quarter, previous month, or the like.
[0163] At block 804, in at least one of the various embodiments,
weather information and operational information corresponding to
the determined historical usage information may be determined. In
at least one of the various embodiments, weather information and
operational information may be determined for a defined length of
time, such as, the previous year, previous quarter, previous month,
or the like, corresponding to the determined historical usage
information. In at least one of the various embodiments, weather
information may include heating degrees days and/or cooling degree
days for one or more base temperatures (e.g., balance points). In
at least one of the various embodiments, operational information
may include number of units of production of various types, length
of time operating in different configurations, or the like.
[0164] At block 806, in at least one of the various embodiments,
one or more candidate pre-treatment performance profiles for the
facility may be determined based on the historical usage
information and the historical weather information. In at least one
of the various embodiments, pre-performance profiles may be
determined for modeling the pre-treatment response to weather
conditions seen by a facility.
[0165] In at least one of the various embodiments, weather
information may include temperature data for the facility's locale.
The historical weather information may be for a recent previous
time period, such as, the previous year, previous two years, last
six-months or the like. The historical weather information may be
determined from weather information local and/or relevant to the
facility that may be modeled using the performance profile.
[0166] In at least one of the various embodiments, if more than one
set of qualifying historical weather information is available,
configuration settings, or user input may be employed to determine
which historical weather information to use. For example, if a
facility is located in a city, there may be historical weather
information for the city, county, state, or multi-state region
where the facility is located. Thus, RPE application 319 may be
configured to use one of a plurality of different set of historical
weather data. Considerations for choosing a particular set of
historical weather information may include the accuracy and
precision of particular historical weather information set,
cost/availability, consistency across different geographical
regions, or the like.
[0167] In at least one of the various embodiments, historical usage
information may include information from a previous time period
that coincides with the determined historical weather information.
The combination of the historical usage information and the
corresponding historical weather information may be employed in
combination to determine a pre-treatment performance profile for
modeling the responsiveness of facility to changes in weather.
[0168] In at least one of the various embodiments, a pre-treatment
performance profile may be generated based on with the following
formula:
Energy
Usage=HDD.sub.TempA*Coef.sub.HDD+CDD.sub.TempB*Coef.sub.CDD+Basel-
oad [0169] HDD.sub.TempA is the number of heating degree days for
base temperature A over a given time period. [0170] Coef.sub.HDD is
a coefficient representing the responsiveness to cool weather for a
facility. [0171] CDD.sub.TempB is number cooling degree days for
base temperature B over a given time period. [0172] CoefCDD is a
coefficient representing the responsiveness to hot/warm weather for
a facility. [0173] Baseload represents a baseline of energy usage
for the facility regardless of weather conditions over a given time
period.
[0174] In at least one of the various embodiments, candidate
pre-treatment performance profiles may be generated at various base
temperature values for HDD and CDD, with various coefficient
values, and with various baseload values, or the like, to create a
variety of per-treatment performance models.
[0175] In at least one of the various embodiments, commercial
facilities, such as, factories, retails stores, or the like, may
provide other information that RPE application 319 may employ to
correct treatment saving information to improve the accuracy of its
representation of the savings attributed to the facility receiving
a project treatment.
[0176] In at least one of the various embodiments, RPE application
319 may be arranged to factor in variations in factory production
rates as part of determining treatment savings. In at least one of
the various embodiments, factory facilities may provide other
information that may be sufficient to generate a metric such as,
unit-of-production/kilowatt-hour. Thus, RPE application 319 may be
able to determine the appropriate correction values to account for
service demand variations that are attributable to production
variations rather that the project treatments.
[0177] For example, a light-bulb factory facility may provide
metrics, such as, the number of light-bulbs produced per
kilowatt-hour and the number light-bulbs per day that have been
produced. Thus, continuing with this example, RPE application 319
may be able to account for production variations that lead to
variations in the usage demand for the facility. For example, given
an observed usage increase of 1000 kWhrs over one month, increases
in production quantity may contribute to the usage. If the
kilowatt-hours per light-bulb is known and the number of light-bulb
produced in the month is known, then the RPE application may
generate an offset to apply to the metered current usage
information to produce an accurate representation of the treatment
savings.
[0178] In at least one of the various embodiments, operational
information may include production or operational usage data for
various types of production or operation which correlate with
greater usage of utility services. The historical operational
information may be for a recent previous time period, such as, the
previous year, previous two years, last six-months or the like. The
historical operational information may be determined from
operational information or production data relevant to the facility
that may be modeled using the performance profile. Operational
information may be represented as the number of units of production
of each of various types that correlate with greater usage of
utility services as well as the number of operational activities
(work shifts, hours of operation, service events) that correlate
with greater usage of utility services. These units can be
expressed as some number X.sub.j where X.sub.j represents the
number of units of production of the jth type, or operational
activities of the jth type, or some combination of units of
production and operational activities.
[0179] In at least one of the various embodiments, a pre-treatment
performance profile may be generated based on with the following
formula which accounts for operational data:
Energy
Usage=.SIGMA.H.sub.j(HDD.sub.TempA,j*Coef.sub.HDD,j+cDD.sub.TempB-
,j*Coef.sub.CDD,j+Baseload.sub.j) for all j. [0180] Baseload
represents the utility service usage that results from operations
of the jth type regardless of the weather at a facility. [0181]
Coef.sub.HDD,j is a coefficient representing the additional utility
service associated with operations of the jth type during
conditions at a facility that require heating. [0182]
HDD.sub.TempA,i is the number of heating degree days over a given
period at the base temperature A that is the base temperature
associated with the jth type of production or operation. [0183]
Coef.sub.CDD,j is a coefficient representing the dependence of the
energy associated with operations of the jth type to hot/warm
weather for a facility. [0184] CDD.sub.TempB,j is the number of
cooling degree days over a given period at the base temperature B
that is the base temperature associated with the jth type of
production or operation.
[0185] In at least one of the various embodiments, candidate
pre-treatment performance profiles may be generated at various base
combinations of the base temperature values for temp A, temp B, and
i. Based on these different base temperatures, various values of
the HDD, CDD, coefficient values, and baseload values may be
calculated to create a variety of per-treatment performance
models.
[0186] At block 808, in at least one of the various embodiments,
one or more outlier periods may be identified in the pre-treatment
period, excluded from the pre-treatment data and the pre-treatment
profile may be re-calculated based on the updated subset of
pre-treatment data. In at least one of the various embodiments,
outlier periods in the pre-treatment period may be identified based
on their influence over the overall model of the pre-treatment
response to weather and operational conditions at a facility. In at
least one of the various embodiments, the model may be recalculated
without outlier periods by excluding all data associated with the
outlier period or periods in the pre-treatment period and
generating candidate pre-treatment performance profiles based on
the limited data set.
[0187] At block 810, in at least one of the various embodiments,
the optimal pre-treatment performance profile for the facility may
be determined based on a best fit method. In at least one of the
various embodiments, one or more curve fitting methods, such as
linear regression may be employed to determine the pre-treatment
performance profile that fits the historical performance of the
facility. The historical weather information may provide the
HDDbasetemp/CDDbasetemp, the historical operational data may
provide the X.sub.j, and the historical usage information may
provide the Energy Usage value. Well-known methods such as
multi-variable linear regression may be applied with the HDD and
CDD values as the two independent variables and Energy Usage as the
dependent variable. In at least one of the various embodiments, RPE
application 319 may be arranged to employ linear regression to
determine the relationship between services consumed and weather
seen (degree days) and determine the goodness of fit using least
squares regression. This determined relationship enables service
consumption to be modeled in the future.
[0188] In at least one of the various embodiments, the inputs for
the linear regression curve fitting may be the number of heating
degree days at a given temperature in a time period, the number of
cooling degree days at a given temperature in the same time period
and the facilities service usage in the time period.
[0189] In at least one of the various embodiments, the HDD, CDD,
and Energy Usage may be divided by the number of days in the time
period to get per-day figures. This may normalize data because the
time period may be different ranges at different times (e.g., the
number of days in a month is not constant).
[0190] In at least one of the various embodiments, the Slope,
Intercept, and R.sup.2 (least squares) may be determined for a
range of temperature combinations of base temperature/balance
points. For a given base temperature, there may be a different
number of heating degree days and cooling degree days. The base
temperature combinations that sufficiently conform to the known
heating and cooling characteristics (e.g. historical usage and
weather information) of the facility may be determined.
[0191] In at least one of the various embodiments, additional
modeling/curve fitting techniques may be employed. For example,
least square regression may be supplemented with Bayesian priors to
adjust the fit for a priori knowledge of the likely base
temperatures for heating, cooling, or for the reliance of various
facility operations on the temperature. In at least one of the
various embodiments, other well-known techniques, such as, Bayesian
multivariate linear regression may be used for modeling/curve
fitting. Also, in at least one of the various embodiments,
configuration settings, or other rules, may be in place to filter
and/or establish floors and/or ceilings for the base temperatures
based on a priori knowledge of the weather conditions relevant to
the facility. In at least one of the various embodiments, RPE
application 319 may be arranged to limit candidate base temperature
values to reasonable ranges that may be consistent with actual
weather seen by the facility.
[0192] In at least one of the various embodiments, if least squares
regression may be employed, one or more pre-treatment performance
profiles may have the same least squares error/deviation value,
however if one pre-treatment performance profile includes a base
temperature inconsistent with actual the weather seen for a
facility, it may be excluded from consideration. For example, if a
candidate pre-treatment performance profile for a facility in
Phoenix, Ariz. (i.e., a very hot place in the Summer) include HDD
values for base temp of 45 degrees Fahrenheit, it may be a
candidate for exclusion because even if linear regression
determines a curve that may appear to fit at base temperature 45
degrees Fahrenheit, a base temperature of 45 degree Fahrenheit is
inconsistent with the actual weather seen in Phoenix, Ariz.
[0193] In at least one of the various embodiments, RPE application
319 may be arranged to determine the best fitting pre-treatment
performance profile and associate it with the corresponding
facility. In at least one of the various embodiments, a
pre-treatment performance profile may be generated for each
treatment facility; likewise, a pre-treatment performance profile
may be generated for each comparison facility. Next, control may be
returned to a calling process.
[0194] FIG. 9 shows a flowchart for process 900 for generating
comparison savings in accordance with at least one of the various
embodiments. After a start block, at block 902, in at least one of
the various embodiments, the usage and weather information can be
split into pre- and post-treatment based on the treatment date of
the associated treatment facility. This information may be
determined similarly as the current usage and weather information
is determined for the treatment facility.
[0195] At block 904, in at least one of the various embodiments,
the comparison facility's pre-treatment performance profile may be
generated based on the pre-treatment usage information,
pre-treatment weather information, and pre-treatment operational
information. In at least one of the various embodiments,
determining pre-treatment performance profile for the comparison
facility may be determined similarly as described in FIG. 8.
However, the historical information for the comparison facility is
used rather than the historical information for the treatment
facility.
[0196] At block 906, in at least one of the various embodiments, a
prediction of the post-treatment usage for the facility may be
generated based on pre-treatment usage information and
post-treatment weather and operational information. In at least one
of the various embodiments, the prediction of the post-treatment
usage for the facility may be generated similarly as the prediction
of post-treatment usage for a treatment facility. (See, block 651
in FIG. 6.)
[0197] At block 908, in at least one of the various embodiments,
comparison savings in the post-treatment period for the comparison
facility may be generated based on the prediction of the
post-treatment usage and the actual post-treatment usage. In at
least one of the various embodiments, the comparison savings for
the comparison facility may be generated similarly as the
observable savings for a treatment facility. (See, block 652 in
FIG. 6.)
[0198] At block 910, in at least one of the various embodiments,
the comparison savings weighting is determined based on the
comparison facility weighting and the treatment facility's
confidence. Next, in at least one of the various embodiments,
control may be returned to a calling process.
[0199] FIG. 10 shows a flowchart for process 1000 for generating
program realization rate analysis in accordance with at least one
of the various embodiments. After a start block, at block 1002, in
at least one of the various embodiments, observable savings
information, savings confidence information, and expected savings
information may be determined for each project in the program. In
at least one of the various embodiments, the observable savings
information for each project for each treatment facility may be
determined. In at least one of the various embodiments, the savings
confidence information for each project for each treatment facility
may be determined. In at least one of the various embodiments, the
expected savings information for each project for each treatment
facility may be determined. In at least one of the various
embodiments, the observable savings information, savings confidence
information and expected savings information may be retrieved from
storage, or it may be generated, depending on how RPE application
319 may be configured and/or based on user input.
[0200] At block 1004, in at least one of the various embodiments,
comparison savings information and comparison weighting for all
comparison facilities related to each project in the program may be
determined. In at least one of the various embodiments, the
comparison savings for all comparison facilities associated with
each project may be determined. In at least one of the various
embodiments, the comparison weighting for all comparison facilities
associated with each project may be determined. In at least one of
the various embodiments, the comparison savings information and the
comparison weighting information may be retrieved from storage, or
it may be generated, depending on how RPE application 319 may be
configured and/or based on user input.
[0201] At block 1006, in at least one of the various embodiments,
net project savings information may be generated based on the
observable savings, the comparison savings information, and the
comparison weighting for each project in the program. In at least
one of the various embodiments, for each project, net project
savings information may be generated. In at least one of the
various embodiments, the net project savings may be combination of
the observable savings for a treatment facility and the comparison
saving generated from corresponding comparison group facilities,
weighted by the comparison savings for each corresponding
facility's comparison weighting. For example, if project savings
was 10 kWh and comparison savings was 2 kWh, treatment savings may
be 8 kWhrs, reflecting that the project should not get credit for
savings associated with the comparison group.
[0202] At block 1008, in at least one of the various embodiments,
net project savings information and expected savings information
may be weighted and aggregated across the entire set of projects in
the program and across each attribute of projects which are to be
analyzed for inclusion in an analysis of influence on project
realization rate, based on the savings confidence information. In
at least one of the various embodiments, the net project savings
and the expected savings information for each project in the
program may be weighted by the savings confidence prior to
aggregation. In at least one of the various embodiments, the
weighted net project savings and the weighted expected savings will
be aggregated across all projects. In at least one of the various
embodiments, the weighted net project savings and the weighted
expected savings will be aggregated across all projects sharing a
variety of attributes to be analyzed for its impact on the
realization rate. For example, the weighted net project savings and
the weighted expected savings may be aggregated across all projects
that include a particular type of treatment or all projects that
were performed by a given partner in order to assess the impact of
that treatment or that partner on the realization rate of projects
with which they're associated. In at least one of the various
embodiments, the attributes included in a realization rate analysis
may include, but are not limited to, the party performing the
treatment, the party analyzing the facility for treatment, the
software used to analyze the facility for treatment, the treatment
performed on the facility, the location of the facility, the date
when the treatment was performed, physical aspects of the
facility's building(s), aspects of the facility's occupancy,
aspects of the facility's operation, the facility's type, or the
like.
[0203] At block 1010, in at least one of the various embodiments,
realization rate information for the program and for various
project attributes may be generated based on dividing the
aggregated project observable savings by the weighted expected
savings information.
[0204] Also, in at least one of the various embodiments, RPE
application 319 may be arranged to exclude project savings
information that appears to be statistical outliers. In at least
one of the various embodiments, the threshold for determining if a
project should be excluded as an outlier may be configured based on
project type. For example, if a project type, such as, window
replacement, is known to produce widely varying results, the
outlier threshold may be set accordingly. Also, in at least one of
the various embodiments, rather than totally excluding outliers
from the program savings information, their contribution may be
weighted such that they contribute less to the overall total
program savings.
[0205] In at least one of the various embodiments, RPE application
319 may be arranged to modify the savings confidence of each
project based on one or more confidence factors. In at least one of
the various embodiments, if curve fit of a facility's pre-treatment
performance profile to the historical information includes
significant outlier periods, the savings confidence for that
project may be reduced. In an embodiment where the confidence is
adjusted for factors beyond the portion of a typical year that have
currently been observed, note that the observable savings may also
be adjusted by the ratio of the savings confidence taking into
account all adjustments to the savings confidence based solely on
portion of the weather and operational load observed to the
savings. Failing to do so will leave the observable savings and
hence net project savings where they are while adjusting the
weighting of the expected savings associated with the projects with
outlier periods and skew the realization rate calculations. As
discussed above, reduced confidence in project savings information
(combined with the adjustment to the net project savings) may
result in reduced contribution of that project to the aggregated
net savings and the aggregated weighted expected savings for the
overall program or attribute. For example, assuming that a least
squares regression approach for determining pre-treatment
performance profiles was used, it may be likely that profiles known
to be accurate will have higher R-squared values of, such as, 0.97,
or above. Thus, if the R-squared value for a profile is
significantly less, such as, 0.67, the profile may be determined to
be inaccurate. Accordingly, in at least one of the various
embodiments, RPE application 319 may be configured to map
particular ranges R-squared values to particular confidence weight
values. For example, an RPE application may be configured to map
ranges R-squared values of 0.999-0.990 to confidence weight 1.0,
0.998-0.992 to confidence weight 0.9, 0.991-0.800 to confidence
weight 0.7, and so on. Thus, in at least one of the various
embodiments, project savings values may be reduced by multiplying
it by a determined confidence weight value.
[0206] For example, in at least one of the various embodiments,
assume project savings information for a treatment facility
includes an energy savings of 10 kWhrs. Low confidence in the
savings values may be reflected by multiplying the raw project
savings value by a confidence coefficient of 0.25, or the like, to
reflect the uncertainly in the project savings value. In at least
one of the various embodiments, reducing the impact of low
confidence facilities and/or project savings results may help avoid
overestimating the positive impact of the project when evaluating
the program as a whole.
[0207] Further, in at least one of the various embodiments, project
savings information may be weighted based on the proportion of
heating and cooling days seen by the treatment facility. For
example, if a typical weather year includes 100 CDDs (for a given
base temperature) but data was collected for only 80 CDDs the
project savings may be reduced by 20% (100* 80/100) to reflect that
only a portion of a typical weather year was seen by the facility.
Project savings may be modified similarly based on the number of
HDDs seen compared the HDD's in a typical year.
[0208] Also, in at least one of the various embodiments, the
collection of usable data for some projects may depend
significantly on the time of year. For example, projects to improve
air-conditioning efficiency (cooling energy efficiency) installed
in the winter months may not generate usable data until for the RPE
application until the end of summer. Thus, in at least one of the
various embodiments, such projects may be excluded from RPE
calculations and evaluation reports until sufficient data has been
collected. In at least one of the various embodiments, RPE
application 319 may be arranged to include table, or other data
structure of information for classifying a threshold number of
CDD/HDDs for treatments and/or projects that enables them to be
included in RPE reports.
[0209] In at least one of the various embodiments, project savings
may be further modified to account for service efficiency
improvements that may occur in the service population independent
from the program and/or projects. Otherwise, the utility may
incorrectly attribute savings to the program. For example, if the
price of heating oil fuel spikes coincidentally during a heating
efficiency program to convert residential oil furnaces to efficient
natural gas furnaces, there may be significant rush to covert to
natural gas furnaces independent of the program. Thus, RPE
application 319 may be arranged to discount project savings based
on a observed demand reduction and/or service savings in facilities
that were included in the program. The discount may be appropriate
because it can be inferred that a certain number of the treatment
facilities that completed the project under the program would have
made the efficient improvements absent the program. Accordingly,
RPE application 319 may be arranged to include a configurable
schedule of weighting factors to account for this "free-rider"
effect. For example, if 10% of non-participating facility exhibited
demand reduction during the program period, project savings may be
proportionally reduced. The schedule of weighting factors may be
adjusted based on the results of program evaluations. Also, the
schedule weighting factors may be set to different values for
different types of project and programs, as well as different type
of external events, such, fuel prices increases, natural disasters,
unseasonable weather, or the like.
[0210] In at least one of the various embodiments, RPE application
319 may be arranged to exclude project savings for facilities have
use that may be inconsistent with the purpose and/or goals of the
program. In at least one of the various embodiments, current and/or
historical usage information may be employed to determine if a
facility should be excluded from the program savings computations.
For example, a cooling efficiency program maybe targeted to
generate energy savings during the summer months. However, if RPE
application 319 determines that the facility is rarely occupied in
the summer months (e.g., Winter vacation homes in Florida), the
facility may be excluded from the program savings.
[0211] Accordingly, in at least one of the various embodiments, RPE
application 319 may determine one or more treatment facilities that
have non-conforming usage patterns and exclude them from the
program savings computation. One or more defined threshold values
may be employed to score if a usage pattern is non-conforming.
These threshold values may vary depending on the type of projects,
programs, or the like.
[0212] In at least one of the various embodiments, a program
realization rate may be generated based on a comparison of the
predicted program savings and the actual program savings value. In
at least one of the various embodiments, the project savings
information for each project in the program may be weighted and
aggregated to generate program savings information. Actual program
savings values may be compared with the predicted/expected program
savings values that were generated before the program went into
effect to produce a realization rate for the program. For example,
if a predicted program savings value is 5 MWh and the generated
program savings value is 4 MWh, the generated realization rate may
be 4/5=80%.
[0213] At block 1012, in at least one of the various embodiments, a
program evaluation report may be generated based on the program
realization information and displayed to a user and/or stored for
future use. In at least one of the various embodiments, the program
evaluation report may include program realization values, program
savings, project savings, realization rate analysis, current
expected savings, or the like.
[0214] In at least one of the various embodiments, RPE application
319 may be arranged to separate the low confidence results from the
high confidence results. Accordingly, RPE application 319 may be
arranged to use the high confidence results to generate a high
confidence program evaluation report and the low confidence results
may be used to generate a low confidence program evaluation
report.
[0215] In at least one of the various embodiments, the program
evaluation report may be automatically updated based on at least
new current usage information and new current weather information
that be periodically and/or continuously provided to RPE
application 319. Further, in at least one of the various
embodiments, the automatic updates to the program evaluation report
may include an accounting based on a quantity of current usage
information available for each treatment facility. Thus, the
project savings attributable to a treatment facility may be
adjusted based on the amount of information (e.g., usage
information, weather information, or the like) available at the
time the program evaluation report is regenerated and/or updated.
Next, control may be returned to a calling process.
[0216] However, the comparison savings values may indicate that one
or more factors separate from the project treatment may be
contributing to the treatment savings of its corresponding
treatment facility. For example, if the treatment savings for a
treatment facility is 8% and the comparison savings for its
associated comparison facilities is 5%, it may indicate that the
project treatment contributed 3% to the overall usage savings while
other factors contributed 5% to the overall usage savings.
[0217] It will be understood that each block of the flowchart
illustration, and combinations of blocks in the flowchart
illustration, can be implemented by computer program instructions.
These program instructions may be provided to a processor to
produce a machine, such that the instructions, which execute on the
processor, create means for implementing the actions specified in
the flowchart block or blocks. The computer program instructions
may be executed by a processor to cause a series of operational
steps to be performed by the processor to produce a
computer-implemented process such that the instructions, which
execute on the processor to provide steps for implementing the
actions specified in the flowchart block or blocks. The computer
program instructions may also cause at least some of the
operational steps shown in the blocks of the flowchart to be
performed in parallel. Moreover, some of the steps may also be
performed across more than one processor, such as might arise in a
multi-processor computer system. In addition, one or more blocks or
combinations of blocks in the flowchart illustration may also be
performed concurrently with other blocks or combinations of blocks,
or even in a different sequence than illustrated without departing
from the scope or spirit of the invention.
[0218] Accordingly, blocks of the flowchart illustration support
combinations of means for performing the specified actions,
combinations of steps for performing the specified actions and
program instruction means for performing the specified actions. It
will also be understood that each block of the flowchart
illustration, and combinations of blocks in the flowchart
illustration, can be implemented by special purpose hardware-based
systems, which perform the specified actions or steps, or
combinations of special purpose hardware and computer instructions.
The foregoing example should not be construed as limiting and/or
exhaustive, but rather, an illustrative use case to show an
implementation of at least one of the various embodiments of the
invention.
* * * * *