Analysis and Modeling of Modern Forecasting Methods for Power Generation in Hybrid Wind–Solar Systems

2026

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DOI 10.52209/3136-4861_2026_1_5
Data confidence 70 medium
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Abstract

This paper provides a theoretical analysis of modern forecasting methodologies for power generation in hybrid wind–solar systems. It discusses statistical, physical, and machine learning approaches, with emphasis on hybrid architectures integrating numerical weather prediction (NWP) data with deep learning models. Evaluation metrics and methodological frameworks are analyzed, highlighting their applicability to hybrid energy forecasting.