Work

Short-Term Residential Load Forecasting Based on Generative Diffusion Models and Attention Mechanisms

Year: 2025

Type: article

Abstract: Accurate short-term prediction of residential power consumption is imperative for efficient energy system management. However, the complexity of high-resolution load data, nonlinear dynamics of load fluctuation, and external factor interactions pose challenges to traditional load forecasting methods. This work introduces a diffusion model-based and attention mechanism-enhanced temporal forecasting framework to address the volatility and uncertainty in load patterns. The proposed model enhances the noise robustness via diffusion processes, captures multi-scale temporal features through temporal convolutional networks, and adaptively focuses on critical time steps using attention mechanisms. Further, a dynamically weighted loss function is designed to improve both the prediction accuracy and latent representation quality. Experiments on multiple real-world residential load datasets show that the proposed model always outperforms benchmarks, reducing on average the mean absolute error (MAE) by 47.4%, symmetric mean absolute percentage error (SMAPE) by 39.7%, and mean absolute percentage error (MAPE) by 57.6%. It also achieves the superior root mean square error (RMSE) and Pearson correlation coefficient (PCC) performance, validating its effectiveness for high-resolution and multi-modal load forecasting.

Source: Energies

DOI: 10.3390/en18236208

Authors: Yitao Zhao, Jiahao Li, Chuanxu Chen, Quansheng Guan

Institutions: Yunnan Power Grid Co., Ltd. (China), South China University of Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,137