Medium- and Long-term Industry Load Forecasting Method Considering Multi-dimensional Temporal Features

被引：0
|
作者：
Zhang K. [1 ]
Cai S. [2 ]
Zhang T. [1 ]
Pan Y. [3 ]
Wang S. [3 ]
Lin Z. [1 ]
机构：
[1] College of Electric Engineering, Zhejiang University, Hangzhou
[2] Hangzhou Yuhang Power Supply Co., Ltd. of State Grid Zhejiang Electric Power Co., Ltd., Hangzhou
[3] Zhejiang Huayun Information Science and Technology Co., Ltd., Hangzhou
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2023年 / 47卷 / 20期
基金：
中国国家自然科学基金;
关键词：
convolutional neural network; gate recurrent unit; kernel density estimation; load forecasting; power system;
D O I：
10.7500/AEPS20230115004
中图分类号：
学科分类号：
摘要：
Medium- and long-term load forecasting is an important basis for power system planning and design, and precise medium- and long-term industry load forecasting can provide the decision support for power system planning and maintenance programming. On this background, a medium- and long-term industry load forecasting method based on the idea of decomposition and forecasting is proposed to account for the multi-dimensional temporal features hidden in the medium- and long-term industry load. Firstly, the feature decomposition model of medium- and long-term industry load based on the seasonal trend decomposition algorithm is constructed to obtain the trend, periodic, and residual components, which represent the trend, periodical and stochastic features of the industry load, respectively. Secondly, according to each dimensional decomposed component obtained by decomposition, a global trend feature extraction and forecasting model based on gate recurrent unit, a local load feature extraction model based on convolutional neural network, and a residual load probability density estimation model based on the improved adaptive Gaussian kernel density estimation are constructed, respectively. Therefore, a medium- and long-term industry load forecasting method considering multi-dimensional temporal features is formed. Finally, the load data of the chemical industry in a city of China is employed to verify the effectiveness of the proposed forecasting method. © 2023 Automation of Electric Power Systems Press. All rights reserved.
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页码：104 / 114
页数：10
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