Ultra-short-term power load forecasting method based on stochastic configuration networks and empirical mode decomposition

Frontiers in Energy Research

Energy Research

Yihua Ma¹  Xiangbin Meng¹  Wei Liu¹  Haibo Li¹  Wei Sun²  Xinfu Pang³ 

[1] Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang, China;Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang, China;Fuxin Power Supply Company, State Grid Liaoning Electric Power Co., Ltd., Fuxin, China;Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang, China;School of Computer Science and Informatics, De Montfort University, Leicester, United Kingdom;

关键词: ultra-short-term power load forecasting;    feature extraction;    stochastic configuration networks;    empirical mode decomposition;    K;   

DOI  :  10.3389/fenrg.2023.1182287

 received in 2023-03-08, accepted in 2023-07-11,  发布年份 2023

来源: Frontiers

【 摘 要 】

Ultra-short-term power load forecasting (USTPLF) can provide strong support and guarantee the decisions on unit start-up, shutdown, and power adjustment. The ultra-short-term power load (USTPL) has strong non-smoothness and nonlinearity, and the time-series characteristics of the load data themselves are difficult to explore. Therefore, to fully exploit the intrinsic features of the USTPL, a stochastic configuration networks (SCNs) USTPLF method based on K-means clustering (K-means) and empirical mode decomposition (EMD) is proposed. First, the load data are decomposed into several intrinsic mode functions (i.e., IMFs) and residuals (i.e., Res) by EMD. Second, the IMFs are classified by K-means, and the IMF components of the same class are summed. Third, the SCNs is used to forecast the electric load on the basis of the classified data. Lastly, on the basis of the real load of Shenzhen City, the proposed method is applied for emulation authentication. The result verifies the efficiency of the proposed measure.

【 授权许可】

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Copyright © 2023 Pang, Sun, Li, Ma, Meng and Liu.