  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.
