English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Accurate electricity demand forecasting is crucial to meet energy securityand efficiency, especially when relying on intermittent renewable energysources. Recently, massive savings have been observed in Europe, following anunprecedented global energy crisis. However, assessing the impact of suchcrisis and of government incentives on electricity consumption behaviour ischallenging. Moreover, standard statistical models based on meteorological andcalendar data have difficulty adapting to such brutal changes. Here, we showthat mobility indices based on mobile network data significantly improve theperformance of the state-of-the-art models in electricity demand forecastingduring the sobriety period. We start by documenting the drop in the Frenchelectricity consumption during the winter of 2022-2023. We then show how ourmobile network data captures work dynamics and how adding these mobilityindices outperforms the state-of-the-art during this atypical period. Ourresults characterise the effect of work behaviours on the electricity demand.

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