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Conservation modeling engine framework

  • US 9,098,820 B2
  • Filed: 02/23/2009
  • Issued: 08/04/2015
  • Est. Priority Date: 02/23/2009
  • Status: Expired due to Fees
First Claim
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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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