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

被引:16
作者
Zheng, Jieyun [1 ]
Zhang, Linyao [1 ]
Chen, Jinpeng [2 ]
Wu, Guilian [1 ]
Ni, Shiyuan [1 ]
Hu, Zhijian [2 ]
Weng, Changhong [2 ]
Chen, Zhi [2 ]
机构
[1] State Grid Fujian Elect Power Co Ltd, Econ Technol Res Inst, Fuzhou 350000, Peoples R China
[2] Wuhan Univ, Sch Elect Engn & Automat, Wuhan 430000, Peoples R China
基金
中国国家自然科学基金;
关键词
multiple-load forecasting; deep bidirectional long and short-term memory; Copula; correlation analysis; integrated energy system;
D O I
10.3390/en14082188
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:14
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