A data‑driven method for state prediction of distributed low‑carbon energy stations

doi:10.19781/j.issn.1673-9140.2024.02.026

Home > Archive>Volume 39, Issue 2, 2024 >231-239. DOI:10.19781/j.issn.1673-9140.2024.02.026

A data‑driven method for state prediction of distributed low‑carbon energy stations
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                        10.19781/j.issn.1673-9140.2024.02.026
                    
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                        ZHANG Feifei1ZHANG Feifei
Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China
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ZHANG Jinrong1ZHANG Jinrong
Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China
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LU Tao1LU Tao
Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China
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ZHAO Ruizhi1ZHAO Ruizhi
Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China
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WANG Jiaxiang1WANG Jiaxiang
Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China
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LUO Yongheng2LUO Yongheng
School of Electrical ＆ Information Engineering, Changsha University of Science ＆ Technology, Changsha 410114, China
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JIANG Fei2JIANG Fei
School of Electrical ＆ Information Engineering, Changsha University of Science ＆ Technology, Changsha 410114, China
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Affiliation:(1. Changxing Power Supply Company,State Grid Shanghai Electric Power Company,Shanghai 201913, China; 2.School of Electrical ＆ Information Engineering, Changsha University of Science ＆ Technology, Changsha 410114, China)
Clc Number:TM863
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Abstract:

Distributed low-carbon energy stations (DLCES) can improve energy utilization efficiency and renewable energy consumption rates. Accurate prediction of the future operating status of DLCES can ensure its safe and reliable operation. Therefore, a data-driven prediction method for the status of DLCES is proposed. Firstly, the structure and operating status of DLCES are analyzed, and the operating status is divided into normal, recovery, critical, and emergency states using key state variables and deviations. Secondly, a deep long-short term memory (LSTM) model is constructed, and an improved particle swarm optimization algorithm is used for hyper-parameter optimization to improve the performance of the prediction model. Finally, the CMPSO-LSTM model is simulated using test sets data, and the results are compared with those of RNN, LSTM, and BP neural networks. The results show that the CMPSO-LSTM model can improve prediction results and has more practical significance.

Key words:state prediction;long-short term memory(LSTM);Cauchy mutation particle swarm optimization (CMPSO);time series prediction

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张菲菲,张金荣,鲁涛,赵睿智,王加祥,罗涌恒,姜飞.基于数据驱动的分布式低碳能源站状态预测方法[J].电力科学与技术学报英文版,2024,39(2):231-239. ZHANG Feifei, ZHANG Jinrong, LU Tao, ZHAO Ruizhi, WANG Jiaxiang, LUO Yongheng, JIANG Fei. A data‑driven method for state prediction of distributed low‑carbon energy stations[J]. Journal of Electric Power Science and Technology,2024,39(2):231-239.

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Online: May 29,2024
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