Building Energy Optimization Based on Biased ReLU Neural Network
作者单位：Harbin Institute of Technology Key Laboratory of System Control and Information ProcessingMinistry of Education
会议名称：《第40届中国控制会议》
会议日期：2021年
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 081304[工学-建筑技术科学] 0835[工学-软件工程] 0813[工学-建筑学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
关 键 词：Biased ReLU neural network MPC building energy consumption HVAC system
摘 要：This paper proposes a building energy optimization strategy based on artifical intelligence technology modeling ***,the data set generated by Energy Plus energy consumption simulation software is used as the training set and test set of the Biased ReLU neural network（BRNN）.Secondly,the building energy consumption prediction model and indoor temperature prediction model are built based on the Biased ReLU neural ***,model predictive control（MPC） is uesd to achieve energy saving by controlling the set temperature of the building’s Heating,Ventilation and Air Conditioning（HVAC） ***,the joint simulation of MATLAB and EnergyPlus is realized by introducing the building control virtual test bed（BCVTB）.The results show that our method can effectively reduce building energy consumption.