Prediction of Building Heating and Cooling Load Based on IPSO-LSTM Neural Network

Abstract

Accurately prediction of building load is essential for energy saving and environmental protection. Exploring the influence of building charac teristics on heating and cooling load can improve energy performance from the design phase of the construction. In this paper, a prediction model of construction heating and cooling loads is proposed, which based on improved Particle Swarm Optimization (IPSO) algorithm and Long Short-Term Memory (LSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman's correlation coefficient method; Then the prediction model based on the LSTM neural network is constructed to forecast building heating and cooling load. The IPSO algorithm is adopted to deal with the trouble that manual work cannot precisely adjust parameters. In this method, the optimization capability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the LSTM neural network are taken as IPSO optimization object to realize the combination of the two algorithms and improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-LSTM can get the best results in the prediction of heating and cooling load.