用欧洲中期天气预报中心数据，实现法国风电1-46天的可靠概率预测。
Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
- 不依赖特定气象模型，通过后处理将天气预报转为风电预测。
- 16天内预测精度比气候基准提升5%-20%，且校准完美。
- 适合电力市场参与者做超前两周的可再生能源调度决策。
在可再生能源占比不断提升的电力系统中，精准可靠的风电预测对电网稳定、供需平衡和市场风险管理至关重要。尽管短期天气预报已用于3天内的发电预测，但超过一周的预测仍需深入研究。尽管近年来亚季节至季节（S2S）天气概率预报取得进展，其用于风电预测时通常需进行时间和空间聚合以获得合理性能。本研究提出一种与预报提前期和数值天气模型无关的预测流程，可将欧洲中期天气预报中心（ECMWF）的亚季节至季节天气预报转化为法国1至46天、日分辨率的风电预测。通过后处理生成的功率集合，我们发现该方法在16天内连续排名概率评分（CRPS）相比气候基准提升5%至15%，集合均方误差（MSE）提升5%至20%，之后趋于气候基准水平。同时，所有提前期的预测均保持近乎完美的校准性。结果表明，电力市场参与者可利用长达两周的预测范围优化可再生能源供应决策。
原文摘要 · Abstract (English)
In a growing renewable based energy system, accurate and reliable wind power forecasts are crucial for grid stability, balancing supply and demand and market risk management. Even though short-term weather forecasts have been thoroughly used to provide up to 3 days ahead renewable power predictions, forecasts involving prediction horizons longer than a week still need investigations. Despite the recent progress in subseasonal-to-seasonal weather probabilistic forecasting, their use for wind power prediction usually involves both temporal and spatial aggregation to achieve reasonable skill. In this study, we present a lead time and numerical weather model agnostic forecasting pipeline which enables to transform ECMWF subseasonal-to-seasonal weather forecasts into wind power forecasts for France for lead times ranging from 1 day to 46 days at daily resolution. By leveraging a post-processing step of the resulting power ensembles we show that these forecasts improve the climatological baseline by 15% to 5% for the Continuous Ranked Probability Score and 20% to 5% for ensemble Mean Squared Error up to 16 days in advance, before converging towards the climatological skill. This improvement in skill is jointly obtained with near perfect calibration of the forecasts for every lead time. The results suggest that electricity market players could benefit from the extended forecast range up to two weeks to improve their decision making on renewable supply
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