Energy Saving Control Method for Adaptive HVAC Systems Based on Convolutional Neural Networks
At present,less than half of the total energy consumption in society worldwide comes from the construction industry,and more than half of the energy consumption in this industry is caused by heating,ventilation and air conditioning(HVAC)systems.How to reduce the energy consumption of HVAC systems is an urgent problem to be solved.In response to this is-sue,this study proposes an energy saving control method for adaptive HVAC systems based on convolutional neural networks.This method analyzes the data received in buildings through convolutional neural networks to adjust the working status of vari-ous parts of the HVAC system and reduce the energy consumption of the entire system.The experimental results show that when the number of iterations reaches 90,the average absolute error values of the back propagation algorithm,random forest algorithm,and convolutional neural network algorithm models are 0.689,0.668,and 0.661 respectively,and the root mean square error values are 0.884,0.882,and 0.879.The research results indicate that the proposed algorithm model can effec-tively reduce the energy consumption of HVAC systems,and also ensures the comfort of personnel inside buildings,which pro-vides certain reference for guiding energy saving renovation of buildings.
HVAC systemconvolutional neural networkenergy saving controllarge building
万方数据
维普
