MLFGCN: short-term residential load forecasting via graph attention temporal convolution network
The MLFGCN algorithm proposed in this paper can significantly improve the accuracy of short-term residential load forecasting. This is achieved through high-quality feature reconstruction, comprehensive information graph construction and spatiotemporal features capture.
Authors
- Dengao Li (ORCID: https://orcid.org/0000-0002-8310-7684)
- Feng Ding (ORCID: https://orcid.org/0000-0001-7642-4182)
- Yu Zhou (ORCID: https://orcid.org/0000-0002-9810-5307)
- Wei Wang (ORCID: https://orcid.org/0000-0003-1568-222X)
Institutions
- Taiyuan Normal University (CN)
- Taiyuan University of Technology (CN)
Publication Details
- Journal
- Frontiers in Neurorobotics
- Published
- 2024-09-23
- DOI
- https://doi.org/10.3389/fnbot.2024.1461403
- Citations
- 6
- Primary Topic
- Energy Load and Power Forecasting
- Type
- article
- Field-Weighted Citation Impact
- 1.25