Open AccessPrediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network
Author(s) -
Qingwu Fan,
Shuo Li,
Xudong Li
Publication year - 2021
Publication title -
journal of autonomous intelligence
Language(s) - English
Resource type - Journals
ISSN - 2630-5046
DOI - 10.32629/jai.v3i2.285
Subject(s) - span (engineering) , artificial neural network , memory span , cooling load , mathematics , control theory (sociology) , computer science , structural engineering , artificial intelligence , engineering , working memory , biology , cognition , air conditioning , mechanical engineering , control (management) , neuroscience
Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearmans correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed topredictbuilding heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters.In this method,the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors.Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.