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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14046 |
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| _version_ | 1866917990657687552 |
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| author | Nabil, Tahar Agoua, Ghislain Cauchois, Pierre De Moliner, Anne Grossin, Benoît |
| author_facet | Nabil, Tahar Agoua, Ghislain Cauchois, Pierre De Moliner, Anne Grossin, Benoît |
| contents | The undergoing energy transition is causing behavioral changes in electricity use, e.g. with self-consumption of local generation, or flexibility services for demand control. To better understand these changes and the challenges they induce, accessing individual smart meter data is crucial. Yet this is personal data under the European GDPR. A widespread use of such data requires thus to create synthetic realistic and privacy-preserving samples. This paper introduces a new synthetic load curve dataset generated by conditional latent diffusion. We also provide the contracted power, time-of-use plan and local temperature used for generation. Fidelity, utility and privacy of the dataset are thoroughly evaluated, demonstrating its good quality and thereby supporting its interest for energy modeling applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14046 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A synthetic dataset of French electric load curves with temperature conditioning Nabil, Tahar Agoua, Ghislain Cauchois, Pierre De Moliner, Anne Grossin, Benoît Machine Learning Artificial Intelligence The undergoing energy transition is causing behavioral changes in electricity use, e.g. with self-consumption of local generation, or flexibility services for demand control. To better understand these changes and the challenges they induce, accessing individual smart meter data is crucial. Yet this is personal data under the European GDPR. A widespread use of such data requires thus to create synthetic realistic and privacy-preserving samples. This paper introduces a new synthetic load curve dataset generated by conditional latent diffusion. We also provide the contracted power, time-of-use plan and local temperature used for generation. Fidelity, utility and privacy of the dataset are thoroughly evaluated, demonstrating its good quality and thereby supporting its interest for energy modeling applications. |
| title | A synthetic dataset of French electric load curves with temperature conditioning |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.14046 |