Regional Load Clustering Integration Forecasting Based on Convolutional Neural Network Support Vector Regression Machine

被引：0
|
作者：
Shen Z. [1 ]
Yuan S. [1 ]
机构：
[1] School of Electronics and Information Engineering, Shanghai University of Electric Power, Yangpu District, Shanghai
来源：
Dianwang Jishu/Power System Technology | 2020年 / 44卷 / 06期
关键词：
Clustering; Convolutional neural network; Load forecasting; Support vector regression machine;
D O I：
10.13335/j.1000-3673.pst.2019.0759
中图分类号：
学科分类号：
摘要：
In order to improve computation efficiency and prediction accuracy of regional load forecasting, this paper proposes a regional load clustering integrated forecasting method based on convolutional neural network support vector regression machine (CNN-SVR). Firstly, clustering model is applied to group the real load data of the users within the region and analyze the effects of different clustering models. Secondly, the cluster classification labels are obtained, and the user data groups are integrated to construct training data. Then, a convolutional neural network support vector regression model is constructed based on improved convolutional neural network. Finally, the prediction results are saved and summed to obtain the final predicted load of the region. Packet load forecasting is used and the prediction results are summed to obtain the final monthly load of the region, and compared with the convolutional neural network model, long short-term memory (LSTM) model, decision tree model and support vector regression model. Simulation using the data of Yangzhong High-tech Zone confirms that this proposed method has higher efficiency and accuracy of load prediction than currently used algorithms. © 2020, Power System Technology Press. All right reserved.
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页码：2237 / 2244
页数：7
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