Building Energy Optimization Based on Biased ReLU Neural Network

This paper proposes a building energy optimization strategy based on artifical intelligence technology modeling method. Firstly, the data set generated by EnergyPlus 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 network. Thirdly, 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) system. Finally, 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.