Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis

TL;DR

This paper demonstrates that incorporating mobile network-based mobility indices significantly enhances electricity demand forecasting accuracy during the 2022 French energy crisis, capturing behavioral changes due to government incentives.

Contribution

It introduces the use of mobility indices from mobile network data to improve demand forecasting models during atypical energy crisis periods.

Findings

Mobility indices improve forecasting performance during the 2022 energy crisis.

French electricity consumption dropped during winter 2022-2023.

Mobility data captures work behavior impacts on electricity demand.

Abstract

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.