基于多负荷特征和TCN-GRU神经网络的负荷预测

doi:10.11930/j.issn.1004-9649.202012107

中国电力 ›› 2022, Vol. 55 ›› Issue (11): 142-148.DOI: 10.11930/j.issn.1004-9649.202012107

基于多负荷特征和TCN-GRU神经网络的负荷预测

郑豪丰¹, 杨国华¹, 康文军², 刘志远², 刘世涛², 伍弘², 张鸿皓¹

1. 宁夏大学物理与电子电气工程学院，宁夏银川 750021;
2. 国网宁夏电力公司电力科学研究院，宁夏银川 750001

收稿日期:2021-01-08 修回日期:2022-10-09 发布日期:2022-11-29
作者简介:郑豪丰（1996—），男，硕士研究生，从事电力系统负荷预测研究，E-mail：zhf026@outlook.com;杨国华（1972—），男，通信作者，教授，硕士生导师，从事电力系统负荷预测研究，E-mail：ygh@nxu.edu.cn
基金资助:
国家自然科学基金资助项目（71263043）；国网宁夏电力公司科技项目（5229DK20004M）。

Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network

ZHENG Haofeng¹, YANG Guohua¹, KANG Wenjun², LIU Zhiyuan², LIU Shitao², WU Hong², ZHANG Honghao¹

1. School of Physics and Electronic-Electrical Engineering, Ningxia University, Yinchuan 750021, China;
2. Electric Power Research Institute of State Grid Ningxia Electric Power Company, Yinchuan 750001, China

Received:2021-01-08 Revised:2022-10-09 Published:2022-11-29
Supported by:
This work is supported by National Natural Science Foundation of China (No.71263043)，Science and Technology Project of State Grid Ningxia Electric Power Company (No.5229DK20004M)

摘要/Abstract

摘要： 传统负荷预测未深入考虑负荷序列对模型预测精度的影响。为提高预测精度，提出了多负荷特征组合(multi-load feature combination, MLFC)，并结合时间卷积网络(temporal convolution network，TCN)和门控循环单元(gated recurrent unit，GRU)构建了负荷预测框架。首先，引入负荷变化率特征和基于集合经验模态分解的负荷分量特征，并与负荷、日期特征构成MLFC；其次，选取TCN和GRU进行特征提取和预测，基于MLFC搭建MLFC-TCN-GRU预测框架；最后，采用不同模型验证所提方法。结果表明：MLFC有助于预测精度提升，且适用于不同模型。同时，MLFC-TCN-GRU相较于其他模型有着较高预测精度。

关键词: 负荷预测, 集合经验模态分解, 时间卷积网络, 门控循环单元

Abstract: Traditional load forecasting does not deeply consider the impact of load sequence on model forecasting accuracy. To improve the prediction accuracy, a multi-load feature combination (MLFC) is proposed, and a load prediction framework is constructed by combining Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU). Firstly, the load change rate feature and the load component feature based on the ensemble empirical mode decomposition are introduced, and the feature combination MLFC is formed with the load and date features; Second, select TCN and GRU for feature extraction and prediction, and build an MLFC-TCN-GRU prediction framework based on MLFC; Finally, using different models to verify the proposed method, the results show that MLFC helps to improve the prediction accuracy and is suitable for different models; at the same time, MLFC-TCN-GRU has the highest prediction accuracy compared with other models.

Key words: load forecasting, ensemble empirical mode decomposition, temporal convolutional network, gated recurrent unit

郑豪丰, 杨国华, 康文军, 刘志远, 刘世涛, 伍弘, 张鸿皓. 基于多负荷特征和TCN-GRU神经网络的负荷预测[J]. 中国电力, 2022, 55(11): 142-148.

ZHENG Haofeng, YANG Guohua, KANG Wenjun, LIU Zhiyuan, LIU Shitao, WU Hong, ZHANG Honghao. Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network[J]. Electric Power, 2022, 55(11): 142-148.

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https://www.electricpower.com.cn/CN/Y2022/V55/I11/142

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基于多负荷特征和TCN-GRU神经网络的负荷预测

Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network

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基于多负荷特征和TCN-GRU神经网络的负荷预测

Load Forecasting Based on Multiple Load Features and TCN-GRU Neural Network

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