Method for creating demand response determination model for hvac system and method for implementing demand response

Abstract

A method for implementing a demand response (DR) for a HVAC system in a building is provided. The method comprises: creating a zone temperature determination model that outputs temperatures of the building by considering an input power provided to the HVAC system and a thermal state of the building; generating objective functions for a power supply schedule in which optimal solutions vary with electricity prices and the thermal state, wherein the power supply schedule includes linear equations for emulating the zone temperature determination model; determining the optimal solutions to the objective functions based on a plurality of electricity price profiles and thermal state profiles; and creating a demand response determination model for taking the electricity price profiles and the thermal state profiles as input and producing a power supply schedule for the HVAC system as output.

What is claimed is: 
     
         1 . A method for implementing a demand response (DR) for a heating, ventilation, and air-conditioning (HVAC) system in a building, the method comprising:
 creating a zone temperature determination model that outputs temperatures of the building by considering an input power provided to the HVAC system and a thermal state of the building, wherein the zone temperature determination model is created by training a first artificial neural network based on a plurality of first training data sets;   generating objective functions for a power supply schedule in which optimal solutions vary with electricity prices and the thermal state, wherein the power supply schedule includes linear equations for emulating the zone temperature determination model;   determining the optimal solutions to the objective functions based on a plurality of electricity price profiles and thermal state profiles; and   creating a demand response determination model that outputs the power supply schedule for the HVAC system by considering the electricity price profiles and the thermal state profiles, wherein the demand response determination model is created by training a second artificial neural network based on a plurality of second training data sets each including the electricity price profiles, thermal state profiles, and determined optimal solutions.   
     
     
         2 . The method of  claim 1 , wherein the building comprises multiple zones, and the zone temperature determination model outputs temperatures for each of the multiple zones. 
     
     
         3 . The method of  claim 2 , wherein each of the plurality of first training data sets includes information related to an existing input power provided to the HVAC system and the thermal state of the building and information related to temperatures for the multiple zones dependent on the existing input power provided to the HVAC system and the thermal state of the building. 
     
     
         4 . The method of  claim 1 , wherein the thermal state of the building comprises at least one of an atmospheric temperature, daylight hours, a wind force, a humidity, a thermal load on the building, and/or building usage schedules. 
     
     
         5 . The method of  claim 3 , wherein the information related to the existing input power provided to the HVAC system and the thermal state of the building is obtained from a building energy management system (BEMS). 
     
     
         6 . The method of  claim 2 , wherein an input layer of the first artificial neural network comprises the input power provided to the HVAC system from a predetermined first time to the present time, the thermal state from a predetermined second time to the present time, and the temperatures for the multiple zones from a third predetermined time to the present time. 
     
     
         7 . The method of  claim 6 , wherein the first artificial neural network model is implemented as a deep nonlinear auto-regressive network (D-NARX). 
     
     
         8 . The method of  claim 1 , wherein the first artificial neural network comprises a plurality of hidden layers,
 wherein one or more of the hidden layers uses a sigmoid function or rectified linear unit (ReLU) function as an activation function.   
     
     
         9 . The method of  claim 1 , wherein the first artificial neural network comprises a pre-processor for normalizing input data and a post-processor for de-normalizing output data. 
     
     
         10 . The method of  claim 2 , wherein the first artificial neural network comprises one or more weight coefficients and one or more bias values,
 wherein the weight coefficients and the bias values are determined based on normalized mean squared errors (NMSE).   
     
     
         11 . The method of  claim 10 , wherein the weight coefficients and bias values are determined for each zone. 
     
     
         12 . The method of  claim 1 , wherein the linear equations for emulating the zone temperature determination model comprise piecewise linear equations which are generated by locally linearizing the activation functions respectively corresponding to the hidden layers included in the first artificial neural network. 
     
     
         13 . The method of  claim 2 , wherein the power supply schedule is determined in such a way as to maintain the temperatures of the multiple zones within a predetermined range and minimize the total electricity cost according to time-varying electricity prices. 
     
     
         14 . The method of  claim 13 , wherein the objective functions are used to determine the power supply schedule for the HVAC system and the temperatures for the multiple zones as the optimal solutions, in order to minimize the total electricity cost and the sum of surpluses from a predetermined boundary temperature. 
     
     
         15 . The method of  claim 2 , wherein the temperature boundary condition for each of the multiple zones allows a first offset for the lower limit of the boundary temperature and a second offset for the upper limit of the boundary temperature, and the first and second offsets are set differently for each of the multiple zones. 
     
     
         16 . The method of  claim 15 , wherein the first offset does not exceed a first reinforced offset, and the second offset does not exceed a second reinforced offset. 
     
     
         17 . The method of  claim 1 , wherein, in the objective function, the HVAC system input power is set to zero for predetermined hours that there are no people in the building. 
     
     
         18 . The method of  claim 1 , wherein the electricity price profiles include information on time-varying electricity prices, and the thermal state profiles include information on the time-varying thermal state of the building. 
     
     
         19 . The method of  claim 1 , wherein, in the determining of optimal solutions to objective functions for a power supply schedule, optimal solutions to objective functions for a power supply schedule are determined by using mixed-integer linear programing (MILP). 
     
     
         20 . A method for implementing a demand response (DR) for a heating, ventilation, and air-conditioning (HVAC) system in a building, the method comprising:
 obtaining an electricity price prediction profile including information related to time-varying electricity prices and a thermal state prediction profile including information related to an time-varying thermal state of the building; and   determining a power supply schedule for the HVAC system in the building based on the information related to the time-varying electricity prices and the information related to the time-varying thermal state of the building.