Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM

Jieyun Zheng, Linyao Zhang, Jinpeng Chen, Guilian Wu, Shiyuan Ni, Zhijian Hu, Changhong Weng and Zhi Chen
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Jieyun Zheng: Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China
Linyao Zhang: Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China
Jinpeng Chen: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China
Guilian Wu: Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China
Shiyuan Ni: Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China
Zhijian Hu: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China
Changhong Weng: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China
Zhi Chen: School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China

Energies, 2021, vol. 14, issue 8, 1-14

Abstract: With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation example analysis and comparison with other models, the forecasting accuracy of the method presented in this paper was improved to a certain extent.

Keywords: multiple-load forecasting; deep bidirectional long and short-term memory; Copula; correlation analysis; integrated energy system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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