Conservation modeling engine framework
First Claim
1. A method for providing a conservation modeling engine framework, comprising:
- selecting, at a conservation modeling engine, 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 a 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;
determining, at the conservation modeling engine, a 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;
creating, at the conservation modeling engine, a plurality of different conservation plans for the region for the future time period by applying the selected customizable resource conservation module to 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, wherein the plurality of conservation plans includes a first plan that has a least implementation cost, a second plan that has a fastest time for implementation and a third plan that conserves a most amount of the resource identified for conservation;
optimizing, by a hardware processor, using one of a greedy algorithm, a penalty method algorithm and a cooperative optimization, the first, second and the third plans by predicting utilizing a Monte Carlo methodology, future values of input variables at an execution time of the first, second and third plans; and
based on said predicting, modifying the input variables and the optimized first, second and third plans to meet thresholds set at an end of a feedback workflow.
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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.
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Citations
22 Claims
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1. A method for providing a conservation modeling engine framework, comprising:
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selecting, at a conservation modeling engine, 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 a 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; determining, at the conservation modeling engine, a 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; creating, at the conservation modeling engine, a plurality of different conservation plans for the region for the future time period by applying the selected customizable resource conservation module to 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, wherein the plurality of conservation plans includes a first plan that has a least implementation cost, a second plan that has a fastest time for implementation and a third plan that conserves a most amount of the resource identified for conservation; optimizing, by a hardware processor, using one of a greedy algorithm, a penalty method algorithm and a cooperative optimization, the first, second and the third plans by predicting utilizing a Monte Carlo methodology, future values of input variables at an execution time of the first, second and third plans; and based on said predicting, modifying the input variables and the optimized first, second and third plans to meet thresholds set at an end of a feedback workflow. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system, comprising:
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a hardware processor, a computer readable memory and a computer-readable hardware storage device; wherein the hardware processor, when executing program instructions stored on the computer-readable hardware storage device via the computer readable memory; 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 a 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; determines a 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; creates a plurality of different conservation plans for the region for the future period time by applying the selected customizable resource conservation module to 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, wherein the plurality of conservation plans includes a first plan that has a least optimized implementation cost, a second plan that has a fastest time for implementation and a third plan that conserves a most amount of the resource identified for conservation; optimizes, via one of a greedy algorithm, a penalty method algorithm and a cooperative optimization, the first, second and the third plans by predicting utilizing a Monte Carlo methodology, future values of input variables at an execution time of the first, second and third plans; and based on said predicting, modifies the input variables and the optimized first, second and third plans to meet thresholds sets at an end of a feedback workflow. - View Dependent Claims (10, 11, 12, 13, 14, 15)
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16. An article of manufacture, comprising:
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a computer readable storage hardware device having computer readable program code embodied therewith, the computer readable program code comprising instructions that, when executed by a computer system processor, 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 a 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; determine a rate of change of availability of the resource identified for conservation from; a real-time sensor input comprising a current level of usage or reservoir amount 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; create a plurality of different conservation plans for the region for the future time period by applying the selected customizable resource conservation module to 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, wherein the plurality of conservation plans includes a first plan that has a least optimized implementation cost, a second plan that has a fastest time for implementation and a third plan that conserves a most amount of the resource identified for conservation; optimize, via one of a greedy algorithm, a penalty method algorithm and a cooperative optimization, the first, second and the third plans by predicting utilizing a Monte Carlo methodology, future values of input variables at an execution time of the first, second and third plans; and based on said predicting, modify the input variables and the optimized first, second and third plans to meet thresholds sets at an end of a feedback workflow. - View Dependent Claims (17, 18, 19, 20, 21, 22)
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Specification