Application of Improved PSO-BP Neural Network in Cold Load Forecasting of Mall Air-Conditioning. (22nd September 2019)

• Record Type: Journal Article
• Authors: Yu, JunQi; Jing, WenQiang; Zhao, AnJun; Ren, YanHuan; Zhou, Meng
• Other Names: Proto Daniela Academic Editor.
• Is Part Of: Journal of control science and engineering. Volume 2019(2019)
• Publication Date: 2019-09-22
• Subjects: Control theory -- Periodicals; 629.831205
• DOI: 10.1155/2019/2428176
• Languages: English
• ISSNs: 1687-5249

Abstract

Abstract : A combination of JMP, PSO-BP neural network, and Markov chain which aims at the low correlation between input and output data and the error of prediction model in the PSO-BP neural network prediction model is proposed. First, the JMP data processing software is used to process the input data and eliminate the samples with low coupling degree. Then, obtaining the cooling load prediction results relies on the training from the PSO-BP neural network. Finally, the final prediction results will be generated by eliminating the random errors using the Markov chain. The results show that the combination of the prediction methods has higher prediction accuracy and conforms to the change rule of the cooling load in shopping malls. Besides, the combination fits the actual application requirements as well.