Physical-Feature-Enhanced Hybrid Low-Rank Transformer for Wind Power Forecasting

Physical-Feature-Enhanced Hybrid Low-Rank Transformer for Wind Power Forecasting | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 13 December 2025 V1 Latest version Share on Physical-Feature-Enhanced Hybrid Low-Rank Transformer for Wind Power Forecasting Authors : Fusen Xiang 0009-0004-2053-1113 [email protected] , Long Luo , and Yong Li 0000-0002-1183-5359 Authors Info & Affiliations https://doi.org/10.22541/au.176558771.15692612/v1 185 views 213 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Abstract: Wind power forecasting is essential for secure, stable, and economic power system operation. Although deep learning models have achieved notable progress in learning spatiotemporal dependencies, they often incur high computational cost and exhibit limited capability in modeling complex multivariate interactions. This paper proposes a physical-feature-enhanced hybrid low-rank Transformer for wind power forecasting. Wind speed is explicitly modeled as the core endogenous variable to better characterize wind farm dynamics from a physically consistent perspective, improving forecasting accuracy and robustness. In addition, a hybrid low-rank attention mechanism is developed by combining a global low-rank approximation with local full-rank attention, thereby reducing computational complexity while preserving predictive performance. Experiments across multiple forecasting horizons show that the proposed method outperforms representative baselines in terms of MAE and RMSE, and yields more reliable prediction interval distributions and more stable error characteristics. Supplementary Material File (physical-feature-enhanced hybrid low-rank transformer for wind power forecasting.docx) Download 8.57 MB Information & Authors Information Version history V1 Version 1 13 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords forecasting theory wind power Authors Affiliations Fusen Xiang 0009-0004-2053-1113 [email protected] Hunan University View all articles by this author Long Luo Hunan University College of Electrical and Information Engineering View all articles by this author Yong Li 0000-0002-1183-5359 the College of Electrical and Information Engineering View all articles by this author Metrics & Citations Metrics Article Usage 185 views 213 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Fusen Xiang, Long Luo, Yong Li. Physical-Feature-Enhanced Hybrid Low-Rank Transformer for Wind Power Forecasting. Authorea . 13 December 2025. DOI: https://doi.org/10.22541/au.176558771.15692612/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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