| 引用本文: | 黄冬梅,陈 欢,王 宁,等.基于自适应图注意力网络的短期用户负荷预测[J].电力系统保护与控制,2023,51(20):140-149. |
| HUANG Dongmei,CHEN Huan,WANG Ning,et al.Short-term user load prediction based on an adaptive graph attention network[J].Power System Protection and Control,2023,51(20):140-149 |
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| 摘要: |
| 为提高短期用户负荷预测精度，提出了一种基于自适应图注意力网络(adaptive graph attention network, AGAT)的短期用户负荷预测模型。首先，针对用户负荷存在规模小、波动性强的问题，通过门控机制结合多个大小不同的扩张卷积核来构造时序特征提取层，从多个尺度上提取用户负荷的高维时序特征。同时，考虑到不同用户负荷间潜在的动态相关性，使用马氏距离构造动态图学习层，生成动态图邻接矩阵。然后，采用图注意力网络根据动态图邻接矩阵将用户负荷的高维时序特征进行信息汇聚。最后，通过全连接层输出用户负荷预测值。为验证AGAT模型的有效性，采用UCI电力负荷数据集进行预测实验，分别与多种基线模型比较。实验结果表明，所提模型预测指标优于各基线模型，有助于提高短期用户负荷预测精度。 |
| 关键词: 短期用户负荷预测 自适应图注意力网络 时序特征提取 动态图学习 图神经网络 |
| DOI：10.19783/j.cnki.pspc.230392 |
| 分类号: |
| 基金项目:国家社会科学基金项目资助(19BGL003)；上海市科委地方院校能力建设项目资助(20020500700) |
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| Short-term user load prediction based on an adaptive graph attention network |
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HUANG Dongmei1, CHEN Huan2, WANG Ning3, WU Zhijian3, HU Wei4, SUN Yuan5
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1. College of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China;
2. College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China; 3. Suzhou Power
Supply Branch, State Grid Jiangsu Electric Power Co., Ltd, Suzhou 215004, China; 4. College of Economics and
Management, Shanghai University of Electric Power, Shanghai 200090, China; 5. College of Mathematics
and Physics, Shanghai University of Electric Power, Shanghai 201306, China
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| Abstract: |
| ph adjacency matrix. Then the graph attention network is used to gather the information of the high dimensional time sequence features of the user load according to the dynamic graph adjacency matrix. Finally, the predicted user load is output through the fully connected layer. To verify the validity of the AGAT model, the UCI power load dataset is used for prediction experiments. The experimental results show that the prediction indices of the proposed model are better than those of various baseline models. This is helpful for the improvement of the accuracy of short-term user load prediction. |
| Key words: short-term user load prediction adaptive graph attention network time sequence feature extraction dynamic graph learning graph neural network |
