Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models

TL;DR

This paper introduces a novel conditional diffusion model approach to synthesize high-quality, customer-specific load profiles, addressing data scarcity and heterogeneity issues in power system data analytics.

Contribution

It extends traditional diffusion models to a conditional framework, incorporating residual layers and attention mechanisms for personalized load profile generation.

Findings

Effective synthesis of customer-specific load profiles demonstrated

Outperforms existing methods in quality and diversity of generated data

Validated on public dataset with superior results

Abstract

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.