Zengyao Tian1,2, Li Lv1This email address is being protected from spambots. You need JavaScript enabled to view it., Wenchen Deng1, and Zhikui Chen3
1Shenyang Institute of Computing Technology Chinese Academy of Sciences, Shenyang, China
2University of Chinese Academy of Sciences, Beijing, China
3School of Software Technology, Dalian University of Technology, Dalian, China
Received: September 5, 2024 Accepted: September 29, 2024 Publication Date: October 26, 2024
Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.
Accurate signal pattern mining of renewable energy generation forecasting (REGF) is important to the daysahead power scheduling of renewable energy power systems. Despite achieving excellent performance with current methods, two issues still persist. (1) They solely utilize historical meteorological signal data to assist in power signal forecasting and neglect valuable information in future information of meteorological signals, consequently limiting their performance. (2) They pursue predictive performance by designing complex architectures and mechanisms, which may lead to insufficient model generalization. To this end, an effective and efficient MLP architecture is proposed to mine REGF signal patterns in renewable energy power systems (SPM-REPS), which contains power signal forecast architecture and meteorological signal forecast architecture. Two architectures seamlessly collaborate in forecasting power generation patterns, which achieves better performance. Meanwhile, time-correlation and feature-correlation strategies are devised within MLP networks to capture both intra-sequence and inter-sequence correlations of signal variables like transformer- and RNNbased methods. Furthermore, a theoretical analysis of linear architecture is given to prove the progressiveness of SPM-REPS. Finally, numerous experiments, conducted on common datasets (CSG-PV and CSG-wind) from Chinese State Grid, demonstrate SPM-REPS sets a new benchmark in mining REGF signal patterns of REPS.
Keywords: Power forecasting; meteorological assistance; time- and feature-correlations
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