AI-Enhanced Reliable Stochastic Modeling for Wind Power Forecasting: Addressing Practical Challenges in Hybrid Framework Deployment
Mohamed Yasser BOUNNITE, Marwane HOUNGNON
Abstract. The reliable integration of wind energy into power grids hinges on accurate forecasting, yet the inherent multi-scale variability and sudden shifts in atmospheric regimes present persistent challenges. We introduce a hybrid stochastic framework that couples Markov regime-switching models with Physics Informed Neural Networks (PINNs) and Variational AutoEncoders (VAEs). This architecture is designed to tackle specific deployment barriers: statistically grounded regime identification, computational tractability, and interpretable uncertainty quantification. Using NREL datasets spanning diverse geographical sites, our model yields a normalized mean absolute error of 1.2% and provides prediction intervals with 95.3% empirical coverage. In grid operations, these forecasts enable a 23% reduction in reserve requirements within a stochastic unit commitment model. We further identify current limitations and propose targeted improvements, offering both a rigorous methodological advance and practical insights for renewable energy integration.
Keywords
Wind Forecasting, Stochastic Modeling, Physics-Informed Neural Networks, Regime-Switching, Computational Complexity, Uncertainty Quantification
Published online 4/25/2026, 8 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Mohamed Yasser BOUNNITE, Marwane HOUNGNON, AI-Enhanced Reliable Stochastic Modeling for Wind Power Forecasting: Addressing Practical Challenges in Hybrid Framework Deployment, Materials Research Proceedings, Vol. 64, pp 36-43, 2026
DOI: https://doi.org/10.21741/9781644904091-5
The article was published as article 5 of the book Energy Futures
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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