Adaptive Spatiotemporal Graph Neural Network-based Net Load Forecasting for Residential Areas

Authors

• Yongqi Yang, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Cong Wang, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Hongli Zhang, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Ping Ma, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Yue Meng, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China
• Tianhao Zhou, School of Intelligence Science and Technology, Xinjiang University, Urumqi 830017, China

Abstract

Abstract: To address the issue that traditional residential overall power load forecasting methods fail to fully consider the differences in users' electricity consumption habits, making it difficult to improve prediction accuracy, a residential net load forecasting method combining representation learning clustering and an AGCN-Transformer was proposed. The representation learning method was utilized to fully extract the latent features of users' net load data, and users were divided into different groups based on the similarity of electricity consumption behaviors; the net load data of the same group were aggregated, and a graph structure suitable for this task was constructed by comprehensively considering the number of clusters, users' historical net load sequences, and the correlations among different groups; the AGCN-Transformer model was adopted to predict the future loads for users of different categories. Experimental results indicate that the proposed method outperforms multiple mainstream forecasting models in both prediction accuracy and generalization ability, showing a good application prospect.

Recommended Citation

Yang, Yongqi; Wang, Cong; Zhang, Hongli; Ma, Ping; Meng, Yue; and Zhou, Tianhao (2026) "Adaptive Spatiotemporal Graph Neural Network-based Net Load Forecasting for Residential Areas," Journal of System Simulation: Vol. 38: Iss. 7, Article 15.
DOI: 10.16182/j.issn1004731x.joss.25-0774
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss7/15

First Page

2007

Last Page

2019

CLC

TP391.9

Recommended Citation

Yang Yongqi, Wang Cong, Zhang Hongli, et al. Adaptive Spatiotemporal Graph Neural Network-based Net Load Forecasting for Residential Areas[J]. Journal of System Simulation, 2026, 38(7): 2007-2019.

Corresponding Author

Wang Cong

DOI

10.16182/j.issn1004731x.joss.25-0774