Patent Document ID: 9817375
Application ID: 14632009
Patent Flag: 1

Claim One:
1. An energy consumption management system comprising: one or more local receivers disposed adjacent a respective one of one or more energy consuming units in a building, each local receiver comprising a processor configured for receiving usage instructions for the adjacent energy consuming unit and causing the usage instructions to be executed for the energy consuming unit; and a central computing system comprising: a memory configured for storing actual usage data associated with at least one energy consuming unit in the building for one or more time windows on each of days i through j, wherein day i is the first day for which data is stored and day j is the most recent day for which data is stored; and a processor configured for: receiving the actual usage data from the memory; executing at least one computer-based learning system to model energy consumption for day j+1 based on at least the actual energy usage data for the energy consumption unit; generating a demand response strategy for the energy consuming unit for day j+1 based on the modeled energy consumption and next-day energy pricing for each time window for day j+1; and communicating the demand response strategy of the energy consuming unit to the local receiver associated with the energy consuming unit, the demand response strategy comprising the usage instructions, wherein at least one of the one or more energy consuming units comprise an HVAC system and the computer-based learning system associated with the HVAC system comprises a regression based system, wherein the regression based system solves the following non-linear equation to learn energy consumption of the HVAC system: 
 Q k =q ( T k+1 I ,T k I ,T k O ,β), wherein Q k is the k th HVAC energy consumption observation stored in the memory, q(•) is a third order polynomial function of a predictor variable vector (T k+1 I ,T k I ,T k O ) which includes thermostat settings, indoor temperatures, and outdoor temperatures for observations k=1. .. n, respectively, n is the number of observations saved in the memory, and β is a parameter vector that includes β 0 , β 1 ,. .. , β p .