用弱监督GAN生成百万条真实住宅用电模式，提升电网规划数据质量
Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection
- 基于过完备自编码器与弱监督训练，学习家庭用电分布规律
- 通过权重选择缓解模式崩溃，生成上百万条高多样性用电模式
- 适用于电力系统仿真、碳中和研究，可公开获取合成数据集
住宅用电数据稀缺制约了居民领域脱碳及电网规划与运行。现有合成数据方法在可扩展性、多样性与真实性方面存在局限。本文提出一种基于生成对抗网络的住宅负荷模式生成模型（RLP-GAN），采用弱监督框架，利用过完备自编码器捕捉复杂多样的负荷模式依赖关系，并在大规模下学习家庭级数据分布。通过引入模型权重选择机制缓解模式崩溃问题，提升生成多样性。基于417户真实数据构建综合评估体系验证效果，结果表明RLP-GAN在捕捉时序依赖性和生成与真实数据更相似的负荷模式方面优于当前最优模型。此外，本文已公开发布包含一百万条合成住宅负荷模式的数据库。
原文摘要 · Abstract (English)
The scarcity of high-quality residential load data can pose obstacles for decarbonizing the residential sector as well as effective grid planning and operation. The above challenges have motivated research into generating synthetic load data, but existing methods faced limitations in terms of scalability, diversity, and similarity. This paper proposes a Generative Adversarial Network-based Synthetic Residential Load Pattern (RLP-GAN) generation model, a novel weakly-supervised GAN framework, leveraging an over-complete autoencoder to capture dependencies within complex and diverse load patterns and learn household-level data distribution at scale. We incorporate a model weight selection method to address the mode collapse problem and generate load patterns with high diversity. We develop a holistic evaluation method to validate the effectiveness of RLP-GAN using real-world data of 417 households. The results demonstrate that RLP-GAN outperforms state-of-the-art models in capturing temporal dependencies and generating load patterns with higher similarity to real data. Furthermore, we have publicly released the RLP-GAN generated synthetic dataset, which comprises one million synthetic residential load pattern profiles.
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