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Orientador(es)
Resumo(s)
Wind power curtailment, known in Brazil as constrained-off, has intensified with the rapid expansion of renew able generation, driven by transmission operational constraints. Accurate forecasting of curtailment is essential for grid planning, compensation mechanisms, and infrastructure optimization. However, constrained-off time series exhibit strong non-stationarity, multi-scale behavior, and long-range dependencies that challenge conventional forecasting models. This paper proposes a decomposition-driven Mamba state space framework with Mixture-of-Experts (MoE) routing for wind curtailment forecasting. The approach integrates Entropy-Guided Variational Mode Decomposition (EG-VMD) for adaptive trend and noise separation, Reversible Instance Normalization to mitigate distribution shift, multi-scale seasonal-trend modeling, and a sparse expert-based Mamba backbone with linear-time complexity. In benchmark comparisons against state-of-the-art forecasting models, the proposed MoE-Mamba reduces Mean Squared Error (MSE) by up to 27%, Root Mean Squared Error by 14.6%, and Mean Absolute Error by 16.1% relative to the strongest baselines, while improving upon a single-expert Mamba by 15.6% in MSE. The results highlight the effectiveness of decomposition-enhanced State Space Models with expert routing for scalable, long-horizon forecasting in renewable power systems.
Descrição
Palavras-chave
Mamba architecture Mixture-of-experts Reversible instance normalization Variational mode decomposition Wind curtailment
Contexto Educativo
Citação
Seman, L. O., Stefenon, S. F., & Matos-Carvalho, J. P. (2026). Decomposition-driven Mamba state space models with expert routing for wind curtailment forecasting. Electric Power Systems Research, 260, Article 113259. https://doi.org/10.1016/j.epsr.2026.113259
Editora
Elsevier