Jul 2026· International Conference on Signal Processing and Communications· pp. 1-5· 0 citations· 19 references
Abstract
Wind energy forecasting has increasingly shifted from point to probabilistic approaches to support risk-aware decision-making. However, most existing methods evaluate models using global calibration metrics, which fail to capture reliability in real-time operations. In this paper, we emphasize the importance of local calibration for trustworthy decision support in wind power generation. We propose a set of local calibration metrics to assess probabilistic forecasts at a finer temporal scale. Furthermore, we incorporate the Adaptive Conformal Inference (ACI) framework as a model-agnostic, post-hoc approach to improve calibration. Extensive experiments on real-world wind power datasets using deep probabilistic forecasting models show that ACI consistently enhances local calibration performance, with improvements of up to 30% in the proposed metrics. These results highlight the significance of local calibration and demonstrate the effectiveness of ACI in improving the reliability of probabilistic forecasts for real-time, risk-aware decision-making.
A practical contribution is provided in the form of a forecasting method that is not only accurate but also statistically reliable in estimating operational risk, thereby bridging the gap between industry demands for robust systems and the constraints imposed by real-world data quality.
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