利用条件扩散模型和建筑元数据创建合成能源仪表数据
Creating synthetic energy meter data using conditional diffusion and building metadata
AI总结：
针对建筑能源数据稀缺问题，本文提出利用建筑与气象等元数据的条件扩散模型，在1,828个电力仪表上生成年度用能曲线，质量显著优于CGAN和CVAE。
摘要：
机器学习的进步和计算能力的提升推动了能源相关研究的进展。然而，获取建筑私有能源数据的渠道有限，阻碍了依赖历史数据的传统回归模型。尽管生成模型提供了一种解决方案，但以往研究主要集中于短期生成周期（例如日用能曲线）和数量有限的仪表。因此，本研究提出一种条件扩散模型，利用相关元数据生成高质量合成能源数据。研究使用包含来自不同建筑和国家的1,828个电力仪表的数据集，将该模型与条件生成对抗网络（CGAN）和条件变分自编码器（CVAE）等传统方法进行比较。该模型明确处理长期年度用能曲线，利用位置、天气、建筑和仪表类型等元数据，生成与真实世界能源消耗模式高度相似且连贯一致的合成数据。结果表明，所提出的扩散模型具有优越性能，与次优方法相比，Fréchet Inception Distance（FID）分数降低36%，Kullback-Leibler散度（KL散度）降低13%。所提方法通过元数据成功生成高质量能源数据，其代码将开源，为未来更广泛的能源数据生成模型奠定基础。
英文摘要：
Advances in machine learning and increased computational power have driven progress in energy-related research. However, limited access to private energy data from buildings hinders traditional regression models relying on historical data. While generative models offer a solution, previous studies have primarily focused on short-term generation periods (e.g., daily profiles) and a limited number of meters. Thus, the study proposes a conditional diffusion model for generating high-quality synthetic energy data using relevant metadata. Using a dataset comprising 1,828 power meters from various buildings and countries, this model is compared with traditional methods like Conditional Generative Adversarial Networks (CGAN) and Conditional Variational Auto-Encoders (CVAE). It explicitly handles long-term annual consumption profiles, harnessing metadata such as location, weather, building, and meter type to produce coherent synthetic data that closely resembles real-world energy consumption patterns. The results demonstrate the proposed diffusion model's superior performance, with a 36% reduction in Frechet Inception Distance (FID) score and a 13% decrease in Kullback-Leibler divergence (KL divergence) compared to the following best method. The proposed method successfully generates high-quality energy data through metadata, and its code will be open-sourced, establishing a foundation for a broader array of energy data generation models in the future.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
