Accurate electricity demand forecasting is crucial to meet energy security and efficiency, especially when relying on intermittent renewable energy sources. Recently, massive savings have been observed in Europe, following an unprecedented global energy crisis. However, assessing the impact of such crisis and of government incentives on electricity consumption behaviour is challenging. Moreover, standard statistical models based on meteorological and calendar data have difficulty adapting to such brutal changes. Here, we show that mobility indices based on mobile network data significantly improve the performance of the state-of-the-art models in electricity demand forecasting during the sobriety period. We start by documenting the drop in the French electricity consumption during the winter of 2022-2023. We then show how our mobile network data captures work dynamics and how adding these mobility indices outperforms the state-of-the-art during this atypical period. Our results characterise the effect of work behaviours on the electricity demand.

翻译：精确的电力需求预测对于保障能源安全与提升效率至关重要，尤其是在依赖间歇性可再生能源的背景下。近期，在史无前例的全球能源危机之后，欧洲出现了大规模节能现象。然而，评估此类危机及政府激励措施对电力消费行为的影响具有挑战性。此外，基于气象和日历数据的标准统计模型难以适应此类剧烈变化。本文研究表明，基于移动网络数据的流动性指数在节制用能期间显著提升了最先进的电力需求预测模型的性能。我们首先记录了2022-2023年冬季法国电力消费的下降情况，随后展示了移动网络数据如何捕捉工作动态，并证明在此非常时期加入这些流动性指数后，模型性能超越了现有最优方法。我们的结果刻画了工作行为对电力需求的影响特征。