在法国实现从亚季节到季节尺度的风力发电技能和可靠概率预报
Achieving Skilled and Reliable Daily Probabilistic Forecasts of Wind Power at Subseasonal-to-Seasonal Timescales over France
AI总结：
本文提出一种无需考虑提前时间的数值天气模型的预报流程，将ECMWF的亚季节到季节天气预报转化为法国每日分辨率的风力发电预报，提升预测精度并实现良好校准。
摘要：
在日益增长的基于可再生能源的能源系统中，准确且可靠的风力发电预测对于电网稳定性、供需平衡和市场风险管理至关重要。尽管短期天气预报已被广泛用于提供多达3天的可再生能源功率预测，但预测时间超过一周的预报仍需进一步研究。尽管亚季节到季节天气概率预报近期取得了进展，但其在风力发电预测中的应用通常需要时间和空间聚合以获得合理的技能。本文提出了一种无需考虑提前时间和数值天气模型的预报流程，能够将ECMWF的亚季节到季节天气预报转化为法国每日分辨率的风力发电预报，提前时间从1天到46天。通过利用所得功率集合的后处理步骤，我们证明这些预报在连续排名概率评分（CRPS）上比气候基线提高15%至5%，在集合均方误差（MSE）上提高20%至5%，在16天提前时间前达到最佳性能，之后趋于气候技能。这种技能的提升同时实现了所有提前时间的近乎完美校准。结果表明，电力市场参与者可以从延长的预测范围中获益，以改善可再生能源供应的决策。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
