Short-Term Solar-Wind Power Forecasting via Physics-Informed MISO MFAC with Dual-Source Adaptive Forgetting

Short-Term Solar-Wind Power Forecasting via Physics-Informed MISO MFAC with Dual-Source Adaptive Forgetting | 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 Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed.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. 11 February 2026 V1 Latest version Share on Short-Term Solar-Wind Power Forecasting via Physics-Informed MISO MFAC with Dual-Source Adaptive Forgetting Author : Zhiyi Zhang 0009-0006-4797-5712 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.177077715.51265060/v1 100 views 71 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Accurate short-term forecasting of solar and wind power is crucial for reducing renewable energy uncertainty in smart grids. However, conventional model-free adaptive control (MFAC) methods lack physical consistency and suffer from a trade-off between transient tracking and steady-state smoothness. To address these limitations, this paper proposes a \textbf{Physics-Informed Multi-Input Single-Output MFAC (PI-MISO-MFAC)} framework with a dual-source adaptive mechanism. First, distinct from traditional heuristics, a \textbf{dual-source adaptive forgetting factor} is designed, which dynamically adjusts based on both \textit{input excitation intensity} and \textit{instantaneous prediction error}. This ensures fast tracking during sudden weather ramps and internal system drifts while maintaining robustness against noise. Second, a \textbf{physics-constrained projection algorithm} is introduced to the pseudo-partial derivative (PPD) estimation. By embedding prior knowledge of renewable generation characteristics (e.g., non-negativity of irradiance sensitivity), the interpretability and estimation accuracy are significantly enhanced. Experiments on the German 2016 dataset demonstrate that PI-MISO-MFAC outperforms Informer and optimized LSTM, achieving an MAE of 22.8 kW (18.5\% improvement), while strictly adhering to physical generation laws. Supplementary Material File (document.pdf) Download 506.15 KB Information & Authors Information Version history V1 Version 1 11 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords active disturbance rejection control adaptive control wind Authors Affiliations Zhiyi Zhang 0009-0006-4797-5712 [email protected] Liaoning Technical University - Huludao Campus View all articles by this author Metrics & Citations Metrics Article Usage 100 views 71 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Zhiyi Zhang. Short-Term Solar-Wind Power Forecasting via Physics-Informed MISO MFAC with Dual-Source Adaptive Forgetting. Authorea . 11 February 2026. DOI: https://doi.org/10.22541/au.177077715.51265060/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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