WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting

• URL: http://arxiv.org/abs/2509.06311v1
• Date: Mon, 08 Sep 2025 03:26:18 GMT
• Title: WindFM: An Open-Source Foundation Model for Zero-Shot Wind Power Forecasting
• Abstract summary: This paper introduces WindFM, a lightweight and generative Foundation Model for wind power forecasting.<n>WindFM learns a universal representation of wind generation dynamics by autoregressively pre-training on these token sequences.<n>Our experiments demonstrate that WindFM achieves state-of-the-art zero-shot performance on both deterministic and probabilistic tasks.
• Score: 18.100611997642257
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality wind power forecasting is crucial for the operation of modern power grids. However, prevailing data-driven paradigms either train a site-specific model which cannot generalize to other locations or rely on fine-tuning of general-purpose time series foundation models which are difficult to incorporate domain-specific data in the energy sector. This paper introduces WindFM, a lightweight and generative Foundation Model designed specifically for probabilistic wind power forecasting. WindFM employs a discretize-and-generate framework. A specialized time-series tokenizer first converts continuous multivariate observations into discrete, hierarchical tokens. Subsequently, a decoder-only Transformer learns a universal representation of wind generation dynamics by autoregressively pre-training on these token sequences. Using the comprehensive WIND Toolkit dataset comprising approximately 150 billion time steps from more than 126,000 sites, WindFM develops a foundational understanding of the complex interplay between atmospheric conditions and power output. Extensive experiments demonstrate that our compact 8.1M parameter model achieves state-of-the-art zero-shot performance on both deterministic and probabilistic tasks, outperforming specialized models and larger foundation models without any fine-tuning. In particular, WindFM exhibits strong adaptiveness under out-of-distribution data from a different continent, demonstrating the robustness and transferability of its learned representations. Our pre-trained model is publicly available at https://github.com/shiyu-coder/WindFM.

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