Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2023
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2309.16238 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910185708060672 |
|---|---|
| author | Doumèche, Nathan Allioux, Yann Goude, Yannig Rubrichi, Stefania |
| author_facet | Doumèche, Nathan Allioux, Yann Goude, Yannig Rubrichi, Stefania |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_16238 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis Doumèche, Nathan Allioux, Yann Goude, Yannig Rubrichi, Stefania Applications 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. |
| title | Human spatial dynamics for electricity demand forecasting: the case of France during the 2022 energy crisis |
| topic | Applications |
| url | https://arxiv.org/abs/2309.16238 |