Customers' load profiles are critical resources to support data analytics applications in modern power systems. However, there are usually insufficient historical load profiles for data analysis, due to the collection cost and data privacy issues. To address such data shortage problems, load profiles synthesis is an effective technique that provides synthetic training data for customers to build high-performance data-driven models. Nonetheless, it is still challenging to synthesize high-quality load profiles for each customer using generation models trained by the respective customer's data owing to the high heterogeneity of customer load. In this paper, we propose a novel customized load profiles synthesis method based on conditional diffusion models for heterogeneous customers. Specifically, we first convert the customized synthesis into a conditional data generation issue. We then extend traditional diffusion models to conditional diffusion models to realize conditional data generation, which can synthesize exclusive load profiles for each customer according to the customer's load characteristics and application demands. In addition, to implement conditional diffusion models, we design a noise estimation model with stacked residual layers, which improves the generation performance by using skip connections. The attention mechanism is also utilized to better extract the complex temporal dependency of load profiles. Finally, numerical case studies based on a public dataset are conducted to validate the effectiveness and superiority of the proposed method.

翻译：客户的负荷曲线是支撑现代电力系统数据分析应用的关键资源。然而，由于采集成本和数据隐私问题，历史负荷曲线数据通常不足以支撑分析。针对此类数据短缺问题，负荷曲线合成技术通过为各客户提供合成训练数据，能够有效构建高性能数据驱动模型。然而，由于客户负荷的高度异质性，利用各客户自身数据训练生成模型以合成高质量负荷曲线仍具挑战。本文提出一种基于条件扩散模型的异质性客户定制化负荷曲线合成方法。具体而言，我们首先将定制化合成转化为条件数据生成问题，继而将传统扩散模型扩展为条件扩散模型以实现条件数据生成，从而能够根据客户的负荷特性与应用需求，为每个客户合成专属负荷曲线。此外，为实现条件扩散模型，我们设计了基于堆叠残差层的噪声估计模型，通过跳跃连接提升生成性能，并利用注意力机制更有效地提取负荷曲线的复杂时间依赖性。最后，基于公开数据集的数值案例研究验证了所提方法的有效性与优越性。