  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.
