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.

Description

CROSS-REFERENCE TO RELATED APPLICATION[0001]This application is based on and claims the benefit of priority to Korean Patent Application No. 10-2018-0109099, filed on Sep. 12, 2018, the disclosure of which is incorporated herein in its entirety by reference.BACKGROUND OF THE INVENTIONField of the Invention[0002]The present invention relates to implement a demand response for a heating, ventilation, and air-conditioning (HVAC) system in a building, and more particularly, to a method of utilizing a supervised learning for an improved demand response for the HVAC system in the building.Related Art[0003]Buildings have high thermal capacity, and thus heating, ventilation, and air-conditioning (HVAC) systems in the buildings can be used for a demand response (DR). However, it is particularly difficult to employ the demand response in the HVAC system in a multi-zone building, because using an HVAC system as a demand response resource requires different temperature models for each zone depe...

AI Technical Summary

Benefits of technology

[0004]An aspect of the present invention is to provide a method for creating a demand response determination model for an HVAC system, that can easily and quickly produce an optimal schedule of input power to the HVAC system by training an artificial neural network based on data built up through machine learning under normal building management conditions, emulating the trained artificial neural network into a mathematical formula using a piecewise linear equation, and applying this mathematical formula for a price-based demand response scheduling optimization problem.

[0005]Another aspect of the present invention is to provide a method for implementing demand response that can easily and quickly produce an optimal schedule of input power to the HVAC system by training an artificial neural network based on data built up through machine learning under normal building management conditions, emulating the trained artificial neural network into a mathematical formula using a piecewise linear equation, and applying this mathematical formula for a price-based demand response scheduling optimization problem.

[0019]In an aspect, 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.

[0020]In an aspect, 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.

Problems solved by technology

However, it is particularly difficult to employ the demand response in the HVAC system in a multi-zone building, because using an HVAC system as a demand response resource requires different temperature models for each zone dependent on the thermal conditions of the building and facility management in the building and these models should reflect the physical characteristics in detail.

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