超越技能分数：通过法国前一个月能源预测案例研究探索次季节预测价值
Beyond skill scores: exploring sub-seasonal forecast value through a case study of French month-ahead energy prediction
摘要：
我们量化了真实世界预测问题的亚季节预测的价值：法国未来一个月能源需求的预测。使用地面温度作为预测因子，我们构建了一个交易策略，并根据实际能源需求和价格数据评估了使用气象预测的财务价值。我们表明，预测的提前时间大于2周，可以有价值的应用程序，无论是在自己的短期预测，特别是在北方冬季。我们认为这个例子的基础上的成本/损失框架，并表明，虽然它捕捉的短期预测的性能，它错过了在长期预测的边际价值。我们还将我们对预测值的评估与传统技能评分进行了对比，我们发现如果单独使用，可能会产生误导。我们强调的重要性，基于最终用户实际使用的变量的预测技能的评估。
英文摘要：
We quantify the value of sub-seasonal forecasts for a real-world prediction problem: the forecasting of French month-ahead energy demand. Using surface temperature as a predictor, we construct a trading strategy and assess the financial value of using meteorological forecasts, based on actual energy demand and price data. We show that forecasts with lead times greater than 2 weeks can have value for this application, both on their own and in conjunction with shorter range forecasts, especially during boreal winter. We consider a cost/loss framework based on this example, and show that while it captures the performance of the short range forecasts well, it misses the marginal value present in the longer range forecasts. We also contrast our assessment of forecast value to that given by traditional skill scores, which we show could be misleading if used in isolation. We emphasise the importance of basing assessment of forecast skill on variables actually used by end-users.
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。
