用混合机器学习模型提升智利风电预测精度，助力电网调度
Operational Wind Speed Forecasts for Chile's Electric Power Sector Using a Hybrid ML Model
- 结合MLP与图神经网络，分短中程预测风速
- 短程预报误差降4%-21%，中程降5%-23%
- 适合电力调度、新能源消纳场景
随着智利电力系统向可再生能源转型，准确预测可再生能源发电量对电网运行至关重要。由于风能和太阳能发电具有高度波动性，其并网带来调度挑战，延缓清洁能源利用。本文量化了风能与太阳能间歇性发电对热电厂的影响，并提出一种针对智利的混合机器学习风速预测方法。该方法融合基于MLP的TiDE模型（用于短期预测）与基于图神经网络的GraphCast模型（用于10天以内的中期预测）。实验表明，该混合方法在短期预测上优于现有最优确定性系统4%-21%，中期预测提升5%-23%。该模型可直接降低风电波动对热电厂调峰需求、弃风率及系统排放的影响。
原文摘要 · Abstract (English)
As Chile's electric power sector advances toward a future powered by renewable energy, accurate forecasting of renewable generation is essential for managing grid operations. The integration of renewable energy sources is particularly challenging due to the operational difficulties of managing their power generation, which is highly variable compared to fossil fuel sources, delaying the availability of clean energy. To mitigate this, we quantify the impact of increasing intermittent generation from wind and solar on thermal power plants in Chile and introduce a hybrid wind speed forecasting methodology which combines two custom ML models for Chile. The first model is based on TiDE, an MLP-based ML model for short-term forecasts, and the second is based on a graph neural network, GraphCast, for medium-term forecasts up to 10 days. Our hybrid approach outperforms the most accurate operational deterministic systems by 4-21% for short-term forecasts and 5-23% for medium-term forecasts and can directly lower the impact of wind generation on thermal ramping, curtailment, and system-level emissions in Chile.
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