用条件扩散模型生成法国电力负荷合成数据，兼顾隐私与真实度。
A synthetic dataset of French electric load curves with temperature conditioning
- 基于条件扩散模型生成带温控信息的电力负荷曲线
- 验证了数据在保真度、可用性和隐私性上的良好表现
- 适合能源建模与需求侧管理研究者使用
能源转型正在引发用电行为变化，如本地发电自用或需求响应灵活性服务。为深入理解这些变化及其带来的挑战，获取个体智能电表数据至关重要，但此类数据受欧盟GDPR保护。因此，广泛使用需依赖合成且隐私安全的数据。本文提出一种基于条件潜空间扩散模型生成的法国电力负荷合成数据集，并包含合同容量、分时电价计划及本地温度信息。通过全面评估数据的保真度、实用性和隐私性，证明其质量优良，具备支持能源建模应用的潜力。
原文摘要 · Abstract (English)
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.
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