Diffusion-based inpainting approach for multifunctional short-term load forecasting

Short-Term Load Forecasting is of great significance for the economic and stable operation of the power system. Against the background of the breakthrough in generative artificial intelligence based on the Diffusion model, the research on relevant load forecasting methods of the latter is still relatively limited. Therefore, this paper refers to many related excellent works, analyzes the commonalities between image generation tasks and load forecasting tasks, and proposes the Diffusion-based Inpainting Forecasting Method (DIFM). DIFM supports multi-variable inputs and can achieve functions such as load sequence generation, quantile forecasting and missing data imputation, making it a flexible and multifunctional method. The feasibility and performance of this method are validated across multiple datasets, with experimental results revealing that DIFM reduces the mean absolute percentage error by 24.61 % and 17.91 % respectively in short-term load forecasting and load imputation tasks compared to the optimal benchmark models.