U.S. patent application number 14/790056 was filed with the patent office on 2015-11-05 for conservation modeling engine framework.
The applicant listed for this patent is International Business Machines Corporation. Invention is credited to GREGORY J. BOSS, ALFRED JAMES BRIGNULL, MICHELE PALLADINO BRIGNULL, RICK A. HAMILTON, II, RUTHIE D. LYLE, ANNE R. SAND.
Application Number | 20150317584 14/790056 |
Document ID | / |
Family ID | 42631758 |
Filed Date | 2015-11-05 |
United States Patent
Application |
20150317584 |
Kind Code |
A1 |
BOSS; GREGORY J. ; et
al. |
November 5, 2015 |
CONSERVATION MODELING ENGINE FRAMEWORK
Abstract
Methods, including service methods, articles of manufacture,
systems, articles and programmable devices provide a conservation
modeling engine framework. Programmable conservation modeling
engines in communication with different customizable resource
conservation modules, each resource conservation module customized
to a distinct resource, select one of the modules customized to a
resource identified for conservation, and user-defined criteria as
a function of the identified resource and the selected module.
Input data is selected and collected as a function of the resource
identified and the selected module and used to weight the input
data. Different optimized conservation plans are created as a
function of the weighted input data and the selected module, each
of the optimized conservation plans displayed having a different
implementation cost, a different time for implementation and a
different total amount of the identified resource saved.
Inventors: |
BOSS; GREGORY J.; (SAGINAW,
MI) ; BRIGNULL; ALFRED JAMES; (ESSEX JUNCTION,
VT) ; BRIGNULL; MICHELE PALLADINO; (ESSEX JUNCTION,
VT) ; HAMILTON, II; RICK A.; (CHARLOTTESVILLE,
VA) ; LYLE; RUTHIE D.; (DURHAM, NC) ; SAND;
ANNE R.; (CANON CITY, CO) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
International Business Machines Corporation |
Armonk |
NY |
US |
|
|
Family ID: |
42631758 |
Appl. No.: |
14/790056 |
Filed: |
July 2, 2015 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
12390780 |
Feb 23, 2009 |
9098820 |
|
|
14790056 |
|
|
|
|
Current U.S.
Class: |
705/7.22 |
Current CPC
Class: |
G06Q 10/06 20130101;
G06Q 10/06375 20130101; G06Q 10/06313 20130101; G06Q 10/06312
20130101 |
International
Class: |
G06Q 10/06 20060101
G06Q010/06 |
Claims
1. A computer implemented method for conservation modeling, the
method comprising executing on a processor the steps of: creating a
plurality of different conservation plans for a region for a future
time period as a function of a determined rate of change of
availability of a resource identified for conservation, wherein the
plurality of conservation plans includes a first plan that has a
least implementation cost relative to implementation costs of a
second plan and a third plan of the plurality of conservation
plans, the second plan that has a fastest time for implementation
relative to times for implementation of the first plan and the
third plan, and the third plan that conserves a most amount of the
resource identified for conservation relative to amounts of the
resource identified for conservation that are conserved by the
first plan and the second plan; optimizing the first, second and
third plans via at least one of a greedy algorithm, a penalty
method algorithm and a cooperative optimization; predicting via a
Monte Carlo methodology future values of an input variable at an
execution time of the first, second and third plans; and modifying
the optimized first, second and third plans to meet a threshold as
a function of the predicted future value of the input variable.
2. The method of claim 1, further comprising selecting a
customizable resource conservation module from a provided plurality
of different customizable resource conservation modules as a
function of the selected module being customized to the resource
identified for conservation, wherein the selected module includes
requirements unique to the resource identified for conservation,
and wherein each of the different customizable resource
conservation modules are customized to different ones of a
plurality of distinct resources that includes the resource
identified for conservation; and using the selected customizable
resource conservation module to determine the rate of change of
availability of the resource identified for conservation.
3. The method of claim 2, further comprising: determining the rate
of change of availability of the resource identified for
conservation from: a real-time sensor input comprising a current
level of usage of the resource identified for conservation; a
dynamic data feed comprising at least one of weather conditions,
and demands for the resource identified for conservation that are
currently predicted to occur over a future time period; static data
comprising a number of facility items using the resource identified
for conservation; and historic data comprising at least one of an
average usage rate of the resource identified for conservation by
the facility items, and a historic weather pattern for a region
comprising the facility items; and creating the plurality of
different conservation plans for the region for the future time
period by applying the selected customizable resource conservation
module to the inputs of the determined rate of change of
availability of the resource, the real-time sensor input, the
dynamic data feed, the static data and the historic data.
4. The method of claim 1, further comprising: displaying the
optimized first, second and third plans in a single table diagram
that: distinguishes different values of implementation costs, times
for implementation and total amounts of the resource identified for
conservation that are each displayed for each of the displayed
plans; identifies the first plan as having the least implementation
cost, the second plan as having the fastest time for
implementation, and the third plan as conserving the most amount of
the resource identified for conservation; and displays sets of
ordered pluralities of different location-specific actions to be
taken to implement each of the optimized first, second and third
plans and in association with respective ones of the optimized
first, second and third plans, wherein each of the sets of ordered
pluralities are associated with different ones of the optimized
first, second and third plans and comprise different orders of the
location-specific actions.
5. The method of claim 4, further comprising: displaying each of
the sets of the ordered pluralities of different location-specific
actions in association with time periods for taking the ordered
actions, wherein the time periods are subsets of a total time of
the time for implementation of an associated plan of the optimized
first, second and third plans.
6. The method of claim 1, further comprising: predicting each of a
plurality of values of an input variable at a time of execution of
each of the optimized first, second and third plans; predicting a
future severity of a shortage of the resource identified for
conservation; and wherein the step of creating the optimized first,
second and third plans is further a function of the plurality of
predicted values of the input variable and the predicted future
shortage severity.
7. The method of claim 1, further comprising: calculating and
displaying an estimated return-on-investment time period for each
of the optimized first, second and third plans as a function of
their respective implementation costs; and weighting the first
plan, the second plan and the third plan to recommend a plan and a
time duration to achieve a return on investment of the recommended
plan.
8. The method of claim 1, further comprising: integrating
computer-readable program code into a computer infrastructure
comprising the processor, a computer readable memory and a computer
readable storage medium, wherein the computer readable program code
is embodied on the computer readable storage medium and comprises
instructions for execution by the processor via the computer
readable memory that cause the processor to perform the steps of
creating the plurality of different conservation plans for the
region for the future time period as the function of the determined
rate of change of availability of the resource identified for
conservation, optimizing the first, second and third plans via the
at least one greedy algorithm, penalty method algorithm and
cooperative optimization, predicting via the Monte Carlo
methodology the future values of the input variable at the
execution time of the first, second and third plans, and modifying
the optimized first, second and third plans to meet the threshold
as the function of the predicted future value of the input
variable.
9. A system, comprising: a hardware processor; a computer readable
memory in communication with the hardware processor; and a
computer-readable hardware storage device in communication with the
hardware processor; wherein the hardware processor executes program
instructions stored on the computer-readable hardware storage
device via the computer readable memory and thereby: creates a
plurality of different conservation plans for a region for a future
time period as a function of a determined rate of change of
availability of a resource identified for conservation, wherein the
plurality of conservation plans includes a first plan that has a
least implementation cost relative to implementation costs of a
second plan and a third plan of the plurality of conservation
plans, the second plan that has a fastest time for implementation
relative to times for implementation of the first plan and the
third plan, and the third plan that conserves a most amount of the
resource identified for conservation relative to amounts of the
resource identified for conservation that are conserved by the
first plan and the second plan; optimizes the first, second and
third plans via one of a greedy algorithm, a penalty method
algorithm and a cooperative optimization; predicts via a Monte
Carlo methodology future values of an input variable at an
execution time of the first, second and third plans; and modifies
the optimized first, second and third plans to meet a threshold as
a function of the predicted future value of the input variable.
10. The system of claim 9, wherein the hardware processor executes
the program instructions stored on the computer-readable hardware
storage device via the computer readable memory and thereby
further: selects a customizable resource conservation module from a
provided plurality of different customizable resource conservation
modules as a function of the selected module being customized to
the resource identified for conservation, wherein the selected
module includes requirements unique to the resource identified for
conservation, and wherein each of the different customizable
resource conservation modules are customized to different ones of a
plurality of distinct resources that includes the resource
identified for conservation; and uses the selected customizable
resource conservation module to determine the rate of change of
availability of the resource identified for conservation.
11. The system of claim 10, wherein the hardware processor executes
the program instructions stored on the computer-readable hardware
storage device via the computer readable memory and thereby
further: determines the rate of change of availability of the
resource identified for conservation from: a real-time sensor input
comprising a current level of usage of the resource identified for
conservation; a dynamic data feed comprising at least one of
weather conditions, and demands for the resource identified for
conservation that are currently predicted to occur over a future
time period; static data comprising a number of facility items
using the resource identified for conservation; and historic data
comprising at least one of an average usage rate of the resource
identified for conservation by the facility items, and a historic
weather pattern for a region comprising the facility items; and
creates the plurality of different conservation plans for the
region for the future time period by applying the selected
customizable resource conservation module to the inputs of the
determined rate of change of availability of the resource, the
real-time sensor input, the dynamic data feed, the static data and
the historic data.
12. The system of claim 9, wherein the hardware processor executes
the program instructions stored on the computer-readable hardware
storage device via the computer readable memory and thereby
further: displays the optimized first, second and third plans in a
single table diagram that: distinguishes different values of
implementation costs, times for implementation and total amounts of
the resource identified for conservation that are each displayed
for each of the displayed plans; identifies the first plan as
having the least implementation cost, the second plan as having the
fastest time for implementation, and the third plan as conserving
the most amount of the resource identified for conservation; and
displays sets of ordered pluralities of different location-specific
actions to be taken to implement each of the optimized first,
second and third plans and in association with respective ones of
the optimized first, second and third plans, wherein each of the
sets of ordered pluralities are associated with different ones of
the optimized first, second and third plans and comprise different
orders of the location-specific actions.
13. The system of claim 9, wherein the hardware processor executes
the program instructions stored on the computer-readable hardware
storage device via the computer readable memory and thereby
further: displays each of the sets of the ordered pluralities of
different location-specific actions in the single table in
association with time periods for taking the ordered actions,
wherein the time periods are subsets of a total time of the time
for implementation of an associated plan of the optimized first,
second and third plans.
14. The system of claim 9, wherein the hardware processor executes
the program instructions stored on the computer-readable hardware
storage device via the computer readable memory and thereby
further: predicts each of a plurality of values of an input
variable at a time of execution of each of the optimized first,
second and third plans; predicts a future severity of a shortage of
the resource identified for conservation; and creates the optimized
first, second and third plans as a function of the plurality of
predicted values of the input variable and the predicted future
shortage severity.
15. An article of manufacture, comprising: a computer readable
storage hardware device having computer readable program code
embodied therewith, the computer readable program code comprising
instructions for execution by a computer system processor that
cause the processor to: create a plurality of different
conservation plans for a region for a future time period as a
function of a determined rate of change of availability of a
resource identified for conservation, wherein the plurality of
conservation plans includes a first plan that has a least
implementation cost relative to implementation costs of a second
plan and a third plan of the plurality of conservation plans, the
second plan that has a fastest time for implementation relative to
times for implementation of the first plan and the third plan, and
the third plan that conserves a most amount of the resource
identified for conservation relative to amounts of the resource
identified for conservation that are conserved by the first plan
and the second plan; optimize the first, second and third plans via
one of a greedy algorithm, a penalty method algorithm and a
cooperative optimization; predict via a Monte Carlo methodology
future values of an input variable at an execution time of the
first, second and third plans; and modify the optimized first,
second and third plans to meet a threshold as a function of the
predicted future value of the input variable.
16. The article of manufacture of claim 15, wherein the computer
readable program code instructions for execution by the processor
further cause the processor to: select a customizable resource
conservation module from a provided plurality of different
customizable resource conservation modules as a function of the
selected module being customized to the resource identified for
conservation, wherein the selected module includes requirements
unique to the resource identified for conservation, and wherein
each of the different customizable resource conservation modules
are customized to different ones of a plurality of distinct
resources that includes the resource identified for conservation;
and use the selected customizable resource conservation module to
determine the rate of change of availability of the resource
identified for conservation.
17. The article of manufacture of claim 16, wherein the computer
readable program code instructions for execution by the processor
further cause the processor to: determine the rate of change of
availability of the resource identified for conservation from: a
real-time sensor input comprising a current level of usage of the
resource identified for conservation; a dynamic data feed
comprising at least one of weather conditions, and demands for the
resource identified for conservation that are currently predicted
to occur over a future time period; static data comprising a number
of facility items using the resource identified for conservation;
and historic data comprising at least one of an average usage rate
of the resource identified for conservation by the facility items,
and a historic weather pattern for a region comprising the facility
items; and create the plurality of different conservation plans for
the region for the future time period by applying the selected
customizable resource conservation module to the inputs of the
determined rate of change of availability of the resource, the
real-time sensor input, the dynamic data feed, the static data and
the historic data.
18. The article of manufacture of claim 15, wherein the computer
readable program code instructions for execution by the processor
further cause the processor to display the optimized first, second
and third plans in a single table diagram that: distinguishes
different values of implementation costs, times for implementation
and total amounts of the resource identified for conservation that
are each displayed for each of the displayed plans; identifies the
first plan as having the least implementation cost, the second plan
as having the fastest time for implementation, and the third plan
as conserving the most amount of the resource identified for
conservation; and displays sets of ordered pluralities of different
location-specific actions to be taken to implement each of the
optimized first, second and third plans and in association with
respective ones of the optimized first, second and third plans,
wherein each of the sets of ordered pluralities are associated with
different ones of the optimized first, second and third plans and
comprise different orders of the location-specific actions.
19. The article of manufacture of claim 18, wherein the computer
readable program code instructions for execution by the processor
further cause the processor to display each of the sets of the
ordered pluralities of different location-specific actions in the
single table in association with time periods for taking the
ordered actions, wherein the time periods are subsets of a total
time of the time for implementation of an associated plan of the
optimized first, second and third plans.
20. The article of manufacture of claim 15, wherein the computer
readable program code instructions for execution by the processor
further cause the processor to: predict each of a plurality of
values of an input variable at a time of execution of each of the
optimized first, second and third plans; predict a future severity
of a shortage of the resource identified for conservation; and
create the optimized first, second and third plans as a function of
the plurality of predicted values of the input variable and the
predicted future shortage severity.
Description
FIELD OF THE INVENTION
[0001] The present invention generally describes conservation
modeling engine tools for planning enterprise-wide conservation
initiatives.
BACKGROUND OF THE INVENTION
[0002] Conservation plans are useful in the management of
enterprise resources. For example, with respect to an agricultural
business, a soil conservationist may evaluate a property's soil,
water, air, plant and animal resources and write a plan that
proposes actions addressing resource management and conditions.
Clients generally prefer options, and thus it is preferred that
alternative actions are offered within a given plan, which provides
more flexibility to clients in creating budgets and installation
schedules for deploying or upgrading systems in order to conserve
or to more efficiently utilize resources.
[0003] Creation, selection and deployment of conservation plans
generally involves system or resource specialists using tacit
information and manual calculations, and thus the plans written and
choices made are highly dependent upon skill sets and knowledge
specific and personal to the specialists involved. Moreover,
conservation efforts may require accommodating multiple goals,
objectives and preferences, some of which may conflict in creating
a given plan. Thus, the presence of multiple issues and
considerations may cause prior art conservation plans to fail to
meet the needs of a client.
SUMMARY OF THE INVENTION
[0004] Methods provide programmable conservation modeling engines
in communication with different customizable resource conservation
modules, each resource conservation module customized to a distinct
resource. The programmable conservation modeling engines are
configured by logic components to select one of the customizable
resource conservation modules customized to a resource identified
for conservation and to select user-defined criteria as a function
of the resource identified for conservation and the selected
conservation module. Input data is selected and collected as a
function of the resource identified for conservation and the
selected customizable resource conservation module and used by the
programmable conservation modeling engines to weight the selected
and collected input data as a function of the selected user-defined
criteria. Different optimized conservation plans are created as a
function of the weighted input data and the selected customizable
resource conservation module, each of the optimized conservation
plans having a different implementation cost, a different time for
implementation and a different total amount of the identified
resource saved, and optimized conservation plans are displayed by
distinguishing relative different implementation costs, different
times for implementation and different total amounts of the
identified resource saved.
[0005] Service methods are also provided comprising deploying
programmable conservation modeling engine frameworks or logic
components to configure programmable conservation modeling engines
according to the method steps described above, for example by a
service provider who offers to implement, deploy, and/or perform
functions for others. Still further, articles of manufacture
comprising a computer usable medium having a computer readable
program in said medium are provided. Such program code comprises
instructions which, when executed on a computer system, cause the
computer system to perform one or more method and/or process
elements described above for a programmable conservation modeling
engine framework. Moreover, systems, articles and programmable
devices are also provided, configured for performing one or more
method and/or process elements of the current invention for
providing a programmable conservation modeling engine framework,
for example as described above.
BRIEF DESCRIPTION OF THE DRAWINGS
[0006] These and other features of the methods, systems and devices
according to the present application will be more readily
understood from the following detailed description of the various
aspects of the embodiments taken in conjunction with the
accompanying drawings in which:
[0007] FIG. 1 is a diagrammatic illustration of a method, process
or system for providing a conservation modeling engine framework
according to the present invention.
[0008] FIG. 2 illustrates a conservation modeling engine framework
according to the present invention.
[0009] FIG. 3 is a block diagram of a Resource Conservation Module
according to the present invention.
[0010] FIG. 4 is a tabular diagram illustrating a display of
differentiated optimized plans by a conservation modeling engine
framework according to the present invention.
[0011] FIG. 5 illustrates a programmable device or module
configured to provide a conservation modeling engine framework
according to the present invention.
[0012] FIG. 6 illustrates an exemplary computerized implementation
of a conservation modeling engine framework according to the
present invention.
[0013] The drawings are not necessarily to scale. The drawings are
merely schematic representations, not intended to portray specific
parameters of the invention. The drawings are intended to depict
only typical embodiments of the invention, and therefore should not
be considered as limiting the scope of the invention. In the
drawings, like numbering represents like elements.
DETAILED DESCRIPTION OF THE INVENTION
[0014] For convenience, the Detailed Description of the Invention
has the following sections:
[0015] I. General Description; and
[0016] II. Computerized Implementation.
I. General Description
[0017] Conservation initiatives deployed to save resources (e.g.
water, electricity, etc.) in areas with shortages require the
consideration of a plurality of objectives, costs and party
interests. Decisions made on an enterprise level are complex, and
lack of adequate or sophisticated planning may result in deployment
plans inappropriate to some important objectives.
[0018] FIG. 1 illustrates a method, process or system for providing
a conservation modeling engine framework 10 according to the
present invention. At 12 a plurality of different customizable
resource conservation modules is provided, each of the different
customizable resource conservation modules customized to a distinct
resource (e.g. water, natural gas, electricity, carbon emissions,
etc.). At 14 a programmable conservation modeling engine is
provided in communication with the customizable resource
conservation modules, wherein the programmable conservation
modeling engine is configured by one or more logic components.
[0019] As configured, at 16 the conservation modeling engine
selects one of the customizable resource conservation modules
customized to a resource identified for conservation, at 18 selects
user-defined criteria as a function of the resource identified for
conservation and the selected customizable resource conservation
module, and at 20 selects and collects input data as a function of
the resource identified for conservation and the selected
customizable resource conservation module. At 22 the conservation
modeling engine weights the selected and collected input data as a
function of the selected user-defined criteria and at 24 creates
different optimized conservation plans as a function of the
weighted input data and the selected customizable resource
conservation module.
[0020] Each of the optimized conservation plans has different
attributes used to distinguish the plans and thereby select one or
more plans, including divergent implementation costs, times for
implementation and total amounts of the identified resource saved,
as well as other attributes.
[0021] At 26 the conservation modeling engine displays the best
optimized conservation plans with respect to different relative
attributes, such as implementation costs, different times for
implementation and different total amounts of the identified
resource saved, etc. In some embodiments the display at 26 may
comprise recommending each of a subset group of optimized plans as
a function of each having a most preferred (e.g. highest or lowest
or best) value of a total executing cost, a total amount of
resources saved, a period of time for executing the plan, best
return on investment, etc., relative to the other optimized
conservation plans. Thus, one embodiment may show and/or recommend
a lowest cost plan, another that saves the most resources, and
another that may be implemented the fastest.
[0022] FIG. 2 illustrates a conservation modeling engine framework
100 according to the present invention. Static input data 102
includes fixed or generally non-variable enterprise project
resources, for example the number of buildings under management or
use, numbers of types of water faucets in the buildings and
resource usage, maintenance and upgrade budgets (for example,
relative to faucets, energy costs for hot water faucets, water
provider bills for water usage, upgrade budget, etc.), and still
other static data 102 useful according to the present invention may
be apparent to one skilled in the art.
[0023] Dynamic input data 104 includes variable data inputs
dynamically derived from the latest data available etc.
Illustrative but not exhaustive examples include dynamic weather
service feeds (for example, drought or heighten-fire risk
conditions predicted to occur or continue over a given future time
period); commodities reports; population statistics; traffic
conditions (for example, local freeway construction impacting
traffic flows, resulting in predicted lower miles-per-gallon
performance for company and commuter vehicles and/or increased use
of mass transit options, etc.); and current regional or
extra-regional energy usage, demands availability or costs. Thus,
with respect to a water management plan, dynamic inputs 104 may
include weather, expected water shortage changes, expected water
price increase, and expected growth of an area covered by a water
source. Still other dynamic data 104 useful according to the
present invention may be apparent to one skilled in the art.
[0024] Real-time resource information may also be provided by
sensor outputs 106, for example water level observations provided
by reservoir and well detectors; metered real-time water flow from
each faucet; real-time traffic patterns; current electrical energy
usage on a given grid; current market pricing for natural gas;
current carbon gas emissions at given locations or regions; and
still other sensor outputs 106 useful according to the present
invention may be apparent to one skilled in the art.
[0025] As discussed above, Customizable Resource Conservation
Modules 110 are selected and incorporated into the Conservation
Modeling Engine 116 for use as a function of identified resource(s)
of concern, and static data 102, dynamic data 104 and sensor
outputs 106 are weighted through user-defined criteria 108 selected
and relevant to the resource(s) of concern and a selected Module
110. User-defined criteria 108 include weightings, priorities,
thresholds, including maximum budget available, amount of resource
that must be saved, and still other criteria useful according to
the present invention may be apparent to one skilled in the
art.
[0026] The present invention provides for the use of different
Resource Conservation Modules 110 for different resources as well
as for different conservation models, enabling a highly
customizable system that applies to different domain areas wherein
users do not have to modify application code to reapply the
framework 100 to different domains. Resource Conservation Modules
110 include user input data, dependencies and rules components
specific to given identified resource or resources and may be
represented as extensible mark-up language (XML) files, data
objects or as other data files.
[0027] User-defined thresholds and targets 112 are also provided to
the Conservation Modeling Engine 116, and in some cases
incorporated into the selected and incorporated Customizable
Resource Conservation Module 110 (as is more fully discussed
below). The user-defined thresholds and targets 112 may comprise
maximum time and materials costs, or maximum time for completion of
a conservation plan project, as specified by a budget or an
authorizing or managing entity, and still other thresholds and
targets 112 useful according to the present invention may be
apparent to one skilled in the art. Historic data 114 is also
provided to the Conservation Modeling Engine 116, and also in some
cases incorporated into a selected and incorporated Customizable
Resource Conservation Module 110: illustrative but not exhaustive
examples include historic weather patterns for a relevant region,
time of year (e.g. average rainfall of recent, current and future
time periods), average water usage per day for the present time
period (e.g. usual summer usage relevant to landscape maintenance),
and still other historic data 114 useful according to the present
invention may be apparent to one skilled in the art.
[0028] The Conservation Modeling Engine 116 is thus enabled to
create and output a plurality of optimized conservation plans 124.
The optimized conservation plans 124 may be presented for selection
and implementation by a user entity, or they may be automatically
selected and implemented by the Conservation Modeling Engine 116.
Attributes of the optimized conservation plans 124 are also used as
feedback 129 to the user-defined criteria 108 component, in one
aspect to change user-defined targets, thresholds, inputs and
weights used to weight the static data 102, dynamic data 104 and
sensor outputs 106 as discussed above.
[0029] FIG. 3 provides a block diagram illustration of a Resource
Conservation Module 110 according to the present invention
comprising a plurality of components 130-150. Some of the
components 130-150 are specific to a resource of concern, and thus
differ between different modules drawn to different resources of
concern. For purposes of illustration, each of the components
130-150 are described presently below for different module 110
embodiments drawn to one each of water, electricity, natural gas
and carbon emissions resources, though it will be appreciated that
other resources and models may be practiced (e.g. gasoline, diesel,
hybrid, hydrogen, biofuel use and distribution, vehicular selection
and deployment, etc.)
[0030] The Requirements & Regulations component 130
incorporates requirements from governmental or other regulating and
certifying bodies specific: examples include mandates for low-flow
devices and time-dependent bans on watering lawns (e.g. "Monday
through Friday") in a water module 110; peak hours consumption
limits (e.g. for heating and cooling) and building insulation
requirements in an electricity module 110; energy usage constraints
and loss restrictions in a natural gas module 110; and operation
time restrictions, requirements for carbon trading and/or exchange
participation, and pollution control requirements and restrictions,
in some embodiments industry or application-dependent, for a carbon
emissions module 110.
[0031] A target completion date component 132 may also be provided,
in some example specifying a plurality of phased completion dates
for implementation (e.g. "complete Phase 1 by end of second
quarter, and Phase 2 by end of fourth quarter"). A Budget
considerations component 134 incorporates resource or plan-specific
limits and parameters: in a water module 110 example a municipal
bond limit of forty million dollars (US$40M) and fiscal milestones,
some limited to non-crisis mode upgrades applications; in contrast,
a Power Grid upgrade project budget limit of one hundred million
dollars (US$100M) is provided by an electricity module 110 example,
a cost of unit conversions and costs and availability of
alternatives is provided by a natural gas module 110 and costs and
availability of unit conversions, alternatives, carbon emissions
equity trading and capability monitoring are provided by a carbon
emissions module 110.
[0032] An Optional Practices component 136 may incorporate rolling
shortages and permanent changes capabilities as well as peak hour
limitations, and construction limitations in the water module 110;
rationing, on/off peak penalties/rewards (e.g. energy credits), and
brownout or blackout capabilities may be considered in the
electricity module 110; facility rationing capabilities and
requirements for use and/or availability of alternatives may be
considered in the natural gas module 110; and shut down and/or line
down requirements, facility and/or community requirements, and use
of trading partners may be considered in the carbon emissions
module 110.
[0033] The Optional Equipment component 138 may consider and
compare the performance of different flow devices in meeting full,
medium and/or low-flow standards in the water module 110; lower
wattage fixtures or appliances and supplemental sources (e.g. wind,
solar energy sources) may be considered in the electricity module
110; and high-efficiency units and hybrid units may be identified
and considered in the natural gas module 110 and/or the carbon
emissions module 110, which may also consider emission control and
filtration devices. Dynamic regional Alerts 140 (e.g. from dynamic
data feeds 104 of FIG. 2) may be directly incorporated into the
Resource Conservation Module 110, for example drought, fire or
high-ultra violet (UV) index warnings may be considered in the
water module 110; brown out condition or peak usage alerts may be
considered in the electricity module 110; shortages and local price
spikes may be considered in the natural gas module 110; and
pollution indexes may be considered in the carbon emissions module
110.
[0034] The Usage Information component 142 may incorporate data
from the dynamic data 104, sensor outputs 106, historic data 114
and other sources. Thus, peak water usage times, members per
household, gallons used per member and average community usage may
be considered in the water module 110; peak electricity usage
times; members per household, kilowatt-hours usage per household
member, and high-use and critical use identification parameters may
be considered in the electricity module 110; cubic feet consumption
per household, members per household, high-use and critical use
identification and peak time parameters may be considered in the
natural gas module 110; and cubic feet generation or consumption
per household, members per household, high-use and critical use
identification parameters, and peak time parameters may be
considered in the carbon emissions module 110.
[0035] The Amounts of Resource Available component 144 may
incorporate data from the dynamic data 104, sensor outputs 106,
historic data 114 and other sources: thus reservoir levels (e.g. as
obtained from the sensor outputs 106), number of and capacity of
available water wells and average well depth may be considered in
the water module 110; grid capacity may be considered in the
electricity module 110; market availability and on-site resource
amounts or capacity may be considered in the natural gas module
110; and carbon-emitting practices, locations, equipment, and
monitoring capability may be considered in the carbon emissions
module 110.
[0036] The Unique Resource Requirements component 146 considers the
physical properties of the resources with respect to utilization,
storage, etc. Thus, in a water module 110 purity and contaminant
targets, limits or densities must be met, or some physical plants
and components may require environmental and operational
temperatures above freezing temperatures (e.g. sensor outputs 106
must indicate temperatures above 33 degrees Fahrenheit during
implementation of an upgrade, perhaps requiring delay of an upgrade
phase until after May 15th in some northern hemisphere locations);
battery storage, output, insulation and minimum separation distance
requirements may be considered in the electricity module 110; and
safety requirements, temperature requirements, purity and
contaminant requirements and storage practices may be considered in
the natural gas module 110 and the carbon emissions module 110,
which may also consider emission concentration, other pollutant
presence and contributors and storage and exchange practices.
[0037] User defined rules 148, including priorities, allowable
combinations, and thresholds and required degrees of adherence to
regulations and rules and defined weightings, may be incorporated
into the module 110 from the user-defined criteria 108, the
user-defined thresholds and targets 112 and from other sources, in
some embodiments as provided or further refined and revised through
feedback 129 to the user-defined criteria 108 from the optimized
conservation plans 124 produced by the Conservation Modeling Engine
116. A Rate of Change of a Resource 150 may also be observed and
considered, in some examples as provided by the sensor outputs 106
and dynamic data feeds 104 in view of static data 102 and historic
data 14 inputs. Thus, a rate-of-change in resource availability or
use may be derived from average household usage, current supply
availability and current/predicted/historic weather conditions in
the water module 110, the electricity module 110 and the natural
gas module 110; and emissions by location (e.g. "hot spots") may be
considered in the carbon emissions module 110.
[0038] Referring again to FIG. 2, the Conservation Modeling Engine
116 is configured to select an appropriate module 110 in response
to an identified resource and utilize Predictive Modeling Agent
118, Scenario Simulator 120 and Project Plan Optimizer 122
components to perform modeling, create scenarios of possible
implementation plans and display a plurality of resultant optimized
plans each differentiated by specific result parameters. The
Predictive Modeling Agent 118 and Scenario Simulator 120 use
predictive methods to predict what value a certain input variable
may have at a time of plan execution; for example they may predict
a future severity of a resource shortage. Some embodiments may
utilize Monte Carlo methodology, which solves a problem by
generating suitable random numbers and observing that fraction of
the numbers obeying some property or properties; this type of
method is useful for obtaining numerical solutions to problems
considered too complicated to solve analytically.
[0039] Prior art decision systems often operate in two basic
methods, wherein inputs are set and may not be modified, and the
decision system determines resulting outputs; and wherein inputs
are set but may be modified to achieve desired output. In contrast,
the Predictive Modeling Agent 118 of the present invention provides
a hybrid decision entity, wherein the Conservation Modeling Engine
116 may modify both inputs 102, 104, 106, 108, 112 and 114 and
optimized conservation plan outputs 124 to meet thresholds set at
each end and thereby determine the best optimized plan at 124. More
particularly, feedback 129 to the user-defined criteria input 108
enables calculation of plans and values by modifying both inputs
and outputs.
[0040] The Project Plan Optimizer 122 uses one or more optimization
algorithms to address optimization problems, providing
computational solutions to a problem in which the object is to find
the best of all possible solutions. More formally, it finds a
solution in a feasible region which meets a minimum or maximum
value of an objective function, and may recommend plans or
solutions at 124 based on a most efficient use of combined
available resources and weighted inputs, priorities and/or
parameters (e.g., response to time, energy, money, space, etc.),
which may also be presented, represented or analyzed on a
return-on-investment (ROI) basis.
[0041] Illustrative but not exhaustive examples of optimization
algorithms that may be used by the Project Plan Optimizer 122
include: a "Greedy Algorithm," one that that follows a
problem-solving metaheuristic of making a locally optimum choice at
each stage, with the objective of finding a global optimum result;
a "Penalty Method," which replaces an original constraint
optimization problem with a series of unconstrained problems,
wherein the individual problem solutions must converge to a
solution of the original constrained problem; and a "Cooperative
Optimization," which is a general optimization method incorporating
a cooperation principle in attacking difficult optimization
problems. Embodiments of the Project Plan Optimizer 122 utilizing
Cooperative Optimization algorithmic processes may solve real-world
optimization problems encompassing millions of variables while
meeting high performance and speed standards.
[0042] In one example implementation, a facility manager desires to
reduce water usage, for example in response to a government or
enterprise entity mandate resulting from a current or projected
water supply shortage. In order to generate an efficient facilities
upgrade plan in compliance with the mandate, the facility manager
identifies the resource of concern as "water" to the Conservation
Modeling Engine 116, which responsively selects a Water Resource
Conservation Module 110 from a plurality of available resource
modules 110. The facility manager further provides specific inputs,
dependencies and rules for possible water conservation scenarios as
user-defined criteria 108 or thresholds/targets 112 to the
Conservation Modeling Engine 116, for example including minimum
requirements to meet availability expectations (e.g., pipes cannot
run dry, temperatures must remain above freezing, etc.).
[0043] Water resource-related static inputs 102 are provided to or
retrieved by the Conservation Modeling Engine 116, including number
of buildings and locations, proximity to each other, number of
facility items at each location (e.g., faucets, washing machines,
toilets, etc.). Water resource-related Dynamic inputs 104 are also
provided to or retrieved by the Conservation Modeling Engine 116,
including traffic patterns for each facility and facility item,
number of people available to perform upgrades, estimated time of
an item upgrade, costs to upgrade per facility item, time and costs
to move upgrade crews between facilities, etc.
[0044] The Conservation Modeling Engine 116 utilizes predictive
methods through the Predictive Modeling Agent 118, Scenario
Simulator 120 and Project Plan Optimizer 122 components as
discussed above, which incorporate and consider expected weather
conditions, increased or reduced demands and availability due to
holidays and/or special events in the community, predicted future
drought severity, governmental energy and/or water conservation
incentives, risks, possible political actions, etc., from the data
inputs 102, 104 and 106 and past experience 114 to produce three
different optimized plans 174, 176 and 178 illustrated in FIG. 4
which created and displayed differentiated with respect to
differentiated relative main objectives 162 as well as other
attributes 164-172.
[0045] More particularly, the table 160 of FIG. 4 presents the
optimized plans 174, 176 and 178 by indicating their differentiated
relative main objectives 162: the first plan 174 achieves the
highest relative amount of water conserved; the second plan 176
offers the fastest time of implementation; and the third plan 178
offers the lowest implementation cost. Thus, in one aspect, a user
may choose (or the Conservation Modeling Engine 116 automatically
chooses) one of the plans 174, 176 and 178 in response to a
preference or priority; if maximum water conservation is desired or
required independent of cost, then the first plan 174 is selected,
but if time of implementation is more important than cost, then the
second plan 176 should be selected, and if lowest cost is more
important than either time or resource savings, the third plan 178
is selected.
[0046] The main relative characteristics 162 may also be weighted
or prioritized relevant to each other through user-defined criteria
108 or threshold/targets 112 to provide recommendations or
automatic selections. In one example, from a total of 100% the
priority of relative total cost of a resource conservation action
plan is weighted at 60%, the priority of a relative amount of
conservation achieved by a plan is weighted at 30%, and the
priority of a relative time period to effect a conservation action
plan is weighted at 10%: thus since relative total cost has the
highest priority, the least costly plan 178 of FIG. 4 would be
recommended or automatically selected by the Conservation Modeling
Engine 116 over the other plans 174 and 176.
[0047] According to the present invention, the attributes 164-172
of each plan 174, 176 and 178 are also displayed in the table 160.
Thus, actions to be taken are organized with respect to periods of
time, with first "week 1" row 164 and second "week 2" row 166
displaying deployment order in routes "A1, C3 and B2," etc., that
may identify buildings, floors, addresses or other location indicia
associated with certain faucets for replacement. Costs 168 of
executing each plan 174, 176 and 178 in terms of money and
resources (e.g. materials costs for new faucets, man-hours, etc.)
are provided. Resources saved 170 by executing each plan (e.g.
quantified through comparison to the cost of a baseline such as the
costs of doing nothing or in executing a current or alternative
baseline plan) are provided in gallons-per-day (g.p.d.). Lastly,
the duration of time required to achieve a return-on-investment
(ROI) 172 equivalent to the costs of implementation is also
provided. Thus, in one aspect, any or each of the attributes 164,
166, 168, 170 and 172 may also be used and considered in selecting
one of the three plans 174, 176 and 178.
[0048] FIG. 5 illustrates a programmable device or module 200
configured to provide a conservation modeling engine framework
according to the present invention, for example as illustrated
and/or configured to provide the processes and results illustrated
in FIGS. 1-4 and described above. The device 200 may be
incorporated into a larger system (such as one provided by a
service provider) wherein other applications and components of the
larger system accomplish systems and methods according to the
present invention, or it may be a stand-alone device or module 200
configured to perform each of the systems and methods described
above. The present embodiment thus comprises a central processing
unit (CPU) or other processing means 201 in communication with a
memory 203 comprising logic components that enable the CPU 201 to
perform processes and methods according to the present application.
Thus, the memory 203 comprises Customizable Resource Conservation
Modules 110 and a Predictive Modeling Agent 118 logic component, a
Scenario Simulator 120 logic component and a Project Plan Optimizer
122 logic component, each having functions and attributes
understood through reference to FIGS. 1-4 the associated
specification materials above.
[0049] A power source 205 is configured to provide operative power
to the device 200; examples include battery units 205 and power
inputs configured to receive alternating or direct current
electrical power, and other appropriate power units 205 will be
apparent to one skilled in the art. A communication port or network
link/node means ("com port") 207 is also provided and configured to
enable data and other communications as may be appropriate, for
example as discussed above.
II. Computerized Implementation
[0050] Referring now to FIG. 6, an exemplary computerized
implementation of a conservation modeling engine framework
according to the present invention includes a computer system 304
deployed within a computer infrastructure 308 such as a computer or
a programmable device such as a personal digital assistant (PDA) or
cellular phone. This is intended to demonstrate, among other
things, that the present invention could be implemented within a
network environment 340 (e.g., the Internet, a wide area network
(WAN), a local area network (LAN), a virtual private network (VPN),
etc.) in communication with one or more additional computers 336,
or on a stand-alone computer infrastructure 308. In the case of the
former, communication throughout the network 340 can occur via any
combination of various types of communication links. For example,
the communication links can comprise addressable connections that
may utilize any combination of wired and/or wireless transmission
methods. Where communications occur via the Internet, connectivity
could be provided by conventional TCP/IP sockets-based protocol,
and an Internet service provider could be used to establish
connectivity to the Internet.
[0051] As shown, the computer system 304 includes a central
processing unit (CPU) 312, a memory 316, a bus 320, and
input/output (I/O) interfaces 324. Further, the computer system 304
is shown in communication with external I/O devices/resources 328
and storage systems 332. In general, the processing unit 312
executes computer program code, such as the code to implement
various components of the process and systems, and devices as
illustrated in FIGS. 1-5 and described above, including the
Customizable Resource Conservation Modules 110, the Predictive
Modeling Agent 118 logic component, the Scenario Simulator 120
logic component and the Project Plan Optimizer 122 logic component,
and which are stored in memory 316 and/or storage system 332. It is
to be appreciated that two or more, including all, of these
components may be implemented as a single component.
[0052] While executing computer program code, the processing unit
312 can read and/or write data to/from the memory 316, the storage
system 332 (e.g. the, and/or the I/O interfaces 324. The bus 320
provides a communication link between each of the components in
computer system 304. The external devices 328 can comprise any
devices (e.g., keyboards, pointing devices, displays, etc.) that
enable a user to interact with computer system 304 and/or any
devices (e.g., network card, modem, etc.) that enable computer
system 304 to communicate with one or more other computing
devices.
[0053] The computer infrastructure 308 is only illustrative of
various types of computer infrastructures for implementing the
invention. For example, in one embodiment, computer infrastructure
308 comprises two or more computing devices (e.g., a server
cluster) that communicate over a network to perform the various
process steps of the invention. Moreover, computer system 304 is
only representative of various possible computer systems that can
include numerous combinations of hardware.
[0054] To this extent, in other embodiments, the computer system
304 can comprise any specific purpose-computing article of
manufacture comprising hardware and/or computer program code for
performing specific functions, any computing article of manufacture
that comprises a combination of specific purpose and
general-purpose hardware/software, or the like. In each case, the
program code and hardware can be created using standard programming
and engineering techniques, respectively. Moreover, the processing
unit 312 may comprise a single processing unit, or be distributed
across one or more processing units in one or more locations, e.g.,
on a client and server. Similarly, the memory 316 and/or the
storage system 332 can comprise any combination of various types of
data storage and/or transmission media that reside at one or more
physical locations.
[0055] Further, I/O interfaces 324 can comprise any system for
exchanging information with one or more of the external device 328.
Still further, it is understood that one or more additional
components (e.g., system software, math co-processing unit, etc.)
not shown in FIG. 4 can be included in computer system 304.
However, if computer system 304 comprises a handheld device or the
like, it is understood that one or more of the external devices 328
(e.g., a display) and/or the storage system 332 could be contained
within computer system 304, not externally as shown.
[0056] The storage system 332 can be any type of system (e.g., a
database) capable of providing storage for information under the
present invention. To this extent, the storage system 332 could
include one or more storage devices, such as a magnetic disk drive
or an optical disk drive. In another embodiment, the storage system
332 includes data distributed across, for example, a local area
network (LAN), wide area network (WAN) or a storage area network
(SAN) (not shown). In addition, although not shown, additional
components, such as cache memory, communication systems, system
software, etc., may be incorporated into computer system 304.
[0057] While shown and described herein as a method and a system,
it is understood that the invention further provides various
alternative embodiments. For example, in one embodiment, the
invention provides a computer-readable/useable medium that includes
computer program code to enable a computer infrastructure to
implement methods, systems and devices according to the present
application, for example as illustrated in FIGS. 1 through 4
described above and otherwise herein. To this extent, the
computer-readable/useable medium includes program code that
implements each of the various process steps of the present
application.
[0058] It is understood that the terms "computer-readable medium"
or "computer useable medium" comprise one or more of any type of
physical embodiment of the program code. In particular, the
computer-readable/useable medium can comprise program code embodied
on one or more portable storage articles of manufacture (e.g., a
compact disc, a magnetic disk, a tape, etc.), on one or more data
storage portions of a computing device, such as the memory 316
and/or the storage system 332 (e.g., a fixed disk, a read-only
memory, a random access memory, a cache memory, etc.).
[0059] Still yet, computer infrastructure 308 is intended to
demonstrate that some or all of the components of implementation
according to the present application could be deployed, managed,
serviced, etc. by a service provider who offers to implement,
deploy, and/or perform the functions of the present invention for
others, for example by licensing methods and browser or application
server technology to an internet service provider (ISP) or a
cellular telephone provider. In one embodiment, the invention may
comprise a business method that performs the process steps of the
invention on a subscription, advertising, and/or fee basis. Thus, a
service provider can create, maintain, support, etc., a computer
infrastructure, such as the computer infrastructure 308 that
performs the process steps of the present application for one or
more customers, and in return the service provider can receive
payment from the customer(s) under a subscription and/or fee
agreement and/or the service provider can receive payment from the
sale of advertising content to one or more third parties.
[0060] In still another embodiment, the invention provides a
computer-implemented method for enabling the processes, methods and
devices according to the present application. In this case, a
computer infrastructure, such as computer infrastructure 308, can
be provided and one or more systems for performing the process
steps of the invention can be obtained (e.g., created, purchased,
used, modified, etc.) and deployed to the computer infrastructure.
To this extent, the deployment of a system can comprise one or more
of: (1) installing program code on a computing device, such as
computer system 304, from a computer-readable medium; (2) adding
one or more computing devices to the computer infrastructure; and
(3) incorporating and/or modifying one or more existing systems of
the computer infrastructure to enable the computer infrastructure
to perform the process steps of the invention.
[0061] As used herein, it is understood that the terms "program
code" and "computer program code" are synonymous and mean any
expression, in any language, code or notation, of a set of
instructions intended to cause a computing device having an
information processing capability to perform a particular function
either directly or after either or both of the following: (a)
conversion to another language, code or notation; and/or (b)
reproduction in a different material form. To this extent, program
code can be embodied as one or more of: an application/software
program, component software/a library of functions, an operating
system, a basic I/O system/driver for a particular computing and/or
I/O device, and the like. Computer readable media can be any
available media that can be accessed by a computer. By way of
example, and not limitation, computer readable media may comprise
"computer storage media" and "communications media."
[0062] "Computer storage media" include volatile and non-volatile,
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.
Computer storage media includes, but is not limited to, 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 medium which can be used to store the desired
information and which can be accessed by a computer.
[0063] "Communication media" typically embodies computer readable
instructions, data structures, program modules, or other data in a
modulated data signal, such as carrier wave or other transport
mechanism. Communication media also includes any information
delivery media.
[0064] The term "modulated data signal" means a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in the signal. By way of example, and not
limitation, communication media includes wired media such as a
wired network or direct-wired connection, and wireless media such
as acoustic, RF, infrared, and other wireless media. Combinations
of any of the above are also included within the scope of computer
readable media.
[0065] Certain examples and elements described in the present
specification, including in the claims and as illustrated in the
Figures, may be distinguished or otherwise identified from others
by unique adjectives (e.g. a "first" element distinguished from
another "second" or "third" of a plurality of elements, a "primary"
distinguished from a "secondary," one or "another" item, etc.) Such
identifying adjectives are generally used to reduce confusion or
uncertainty, and are not to be construed to limit the claims to any
specific illustrated element or embodiment, or to imply any
precedence, ordering or ranking of any claim elements, limitations
or process steps.
[0066] The foregoing description of various aspects of the
invention has been presented for purposes of illustration and
description. It is not intended to be exhaustive or to limit the
invention to the precise form disclosed, and obviously, many
modifications and variations are possible. Such modifications and
variations that may be apparent to a person skilled in the art are
intended to be included within the scope of the invention as
defined by the accompanying claims.
* * * * *