Cold Load Prediction Model Based on Improved PSO-BP Algorithm

doi:10.16182/j.issn1004731x.joss.19-0223

Abstract

Abstract: Aiming at the low correlation between input and output data and the error of prediction model in PSO-BP neural network prediction model, a combined prediction method based on JMP, PSO-BP neural network and Markov chain is proposed. The method first uses JMP data processing software to process the input data and eliminating the low coupling degree samples, then conducts PSO-BP neural network training to obtain the cold load prediction results, and finally uses markov chain to eliminate the random errors generated by the system to obtain the final prediction results. The results show that the combined prediction method has higher prediction accuracy, and the prediction result conforms to the change rule of the shopping mall load, and meets the actual application requirements.

Key words: air conditioning cooling load, PSO-BP neural, prediction algorithms, markov chain

CLC Number:

TU831
TP391

Yu Junqi, Jing Wenqiang, Zhao Anjun, Ren Yanhuan, Zhou Meng, Huang Xinle, Yang Xue. Cold Load Prediction Model Based on Improved PSO-BP Algorithm[J]. Journal of System Simulation, 2021, 33(1): 54-61.

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