Adaptive spatiotemporal graph learning for multi-horizon probabilistic wind power forecasting

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Chinese explanation / 中文解读

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Original abstract

Accurate and reliable forecasting of wind power generation is a cornerstone for ensuring the operational flexibility, economic efficiency, and grid stability of modern power systems with high renewable penetration. While recent advances in deep learning have improved point and probabilistic forecasting, most existing methods still emphasize error minimization at isolated horizons or locations, neglecting the spatiotemporal dependencies, uncertainty calibration, and temporal coherence that are critical in real-world decision-making contexts. In this study, we propose a novel adaptive spatiotemporal graph neural network (ST-GNN) framework for multi-horizon probabilistic wind power forecasting, designed to jointly optimize deterministic accuracy, distributional sharpness, and temporal stability. The framework integrates site-specific meteorological inputs and SCADA data into a dynamically evolving graph structure, where edges are adaptively reweighted to capture changing inter-site correlations driven by wind climatology. A unified multi-objective loss function is formulated, combining mean error minimization with probabilistic metrics such as the Continuous Ranked Probability Score (CRPS) and a temporal smoothness regularizer that mitigates spurious fluctuations across consecutive horizons. Comprehensive case studies are conducted using two years of high-resolution data from a wind farm cluster consisting of twelve geographically diverse sites. Results demonstrate that the proposed model achieves up to 13% reduction in RMSE at 1-4 hour horizons and maintains improvements above 5% at 24-hour horizons when compared with state-of-the-art baselines. Probabilistic evaluations indicate a reduction in calibration error, with empirical coverage deviations generally within 3% across central quantiles and uncertainty estimates that adapt to different meteorological regimes. Moreover, the temporal rank-order consistency of predictions remains significantly higher than existing models, reducing dispatch-related instabilities and improving multi-site coordination. These findings suggest that the integration of spatiotemporal graph structures with probabilistic training objectives can provide a flexible and potentially generalizable paradigm.

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