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

翻译：在可再生能源占比日益增长的能源系统中，准确可靠的风电功率预测对于电网稳定性、供需平衡及市场风险管理至关重要。尽管短期天气预报已广泛应用于未来3天的可再生能源功率预测，但超过一周的预测时长仍需深入研究。近年来次季节到季节概率天气预报虽取得进展，但其在风电预测中的应用通常需通过时间和空间聚合手段才能达到合理技能水平。本研究提出了一种与预测步长和数值天气预报模型无关的通用预测流程，可将ECMWF次季节到季节天气预报转换为法国地区未来1至46天逐日分辨率的风电功率预测。通过引入结果功率集合的后处理步骤，我们证明：对于连续等级概率评分（CRPS），这些预测较气候学基准改进达15%至5%；对于集合均方误差（MSE），改进达20%至5%，且该改进可持续至提前16天，随后趋于气候学技能水平。在提升预测技能的同时，所有预测步长的预测均实现了近乎完美的校准。结果表明，电力市场参与者可受益于长达两周的扩展预测范围，从而优化可再生能源供应的决策。