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ORIGINAL RESEARCH article
Front. Neurorobot.
Volume 18 - 2024 |
doi: 10.3389/fnbot.2024.1461403
MLFGCN: Short-Term Residential Load Forecasting via Graph Attention Temporal Convolution Network
Provisionally accepted- 1 College of Computer Science and Technology (College of Data Science), Taiyuan University of Technology, Taiyuan, China
- 2 Taiyuan University of Technology, Taiyuan, China
Residential load forecasting is a challenging task due to the random fluctuations caused by complex correlations and individual differences. The existing short-term load forecasting models usually introduce external influencing factors such as climate and date. However, these additional information not only bring computational burden to the model, but also have uncertainty. To address these issues, we propose a novel multi-level feature fusion model based on graph attention temporal convolutional network (MLFGCN) for short-term residential load forecasting. The proposed MLFGCN model fully consider the potential long-term dependencies in a single load series and the correlations between multiple load series, and does not require any additional information to be added. Temporal convolutional network (TCN) with gating mechanism is introduced to learn potential long-term dependencies in the original load series. In addition, we design two graph attentive convolutional modules to capture potential multi-level dependencies in load data. Finally, the outputs of each module are fused through an information fusion layer to obtain the highly accurate forecasting results. We conduct validation experiments on two real-world datasets, which demonstrate that our proposed model is always better than the baselines.
Keywords: load forecasting, Multi-level feature fusion, Neural Network, Time-series forecasting, Graph neural networks
Received: 08 Jul 2024; Accepted: 09 Sep 2024.
Copyright: © 2024 Feng, Li, Zhou and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Dengao Li, Taiyuan University of Technology, Taiyuan, China
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