A cooling load prediction method using improved CEEMDAN and Markov Chains correction. (October 2021)

• Record Type: Journal Article
• Authors: Gao, Yuefen; Hang, Yang; Yang, Mengliang
• Is Part Of: Journal of building engineering. Volume 42(2021)
• Publication Date: 2021-10
• Subjects: Cooling load prediction -- Neural network -- Markov chain -- Improved CEEMDAN; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2021.103041
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Due to having the characters of non-linear and non-stationary, it is difficult to accurately predict the air conditioning cooling load. In order to predict the load more accurately, this paper introduces the method of Improved CEEMDAN algorithm and Markov chain correction. The Improved CEEMDAN algorithm decomposes the affecting parameters of cooling load into different components. With Pearson correlation analysis, the components with high correlation to cooling load are retained. The component prediction model of the cooling load is established, through Markov chain correction to weaken the effect on the accuracy by the model's uncertainty. The above method alleviates the influence of data fluctuation on the prediction accuracy of cooling load. Parallel computing is used in the cooling load prediction model, which enhances the model operation speed. The results show that the prediction accuracy of the initial data of the model is improved and the peak error of the prediction is significantly improved, compared with the traditional GA-BP network and PSO-BP network. The improved model prediction results are more in line with the application of practical problems. Highlights: A novel cooling load prediction method is proposed. Model accuracy is improved by feature extraction and mode decomposition. Markov chain correction can reduce the effect of uncertainty. Parallel computing can reduce the running time.