基于多分支门控残差卷积神经网络的短期电力负荷预测

doi:10.11930/j.issn.1004-9649.202112004

中国电力 ›› 2022, Vol. 55 ›› Issue (11): 155-162,174.DOI: 10.11930/j.issn.1004-9649.202112004

基于多分支门控残差卷积神经网络的短期电力负荷预测

樊江川¹, 于昊正¹, 刘慧婷², 杨丽君², 安佳坤³

1. 国网河南省电力公司经济技术研究院，河南郑州 450002;
2. 电力电子与电力传动河北省重点实验室（燕山大学），河北秦皇岛 066004;
3. 国网河北省电力有限公司经济技术研究院，河北石家庄 050011

收稿日期:2021-12-06 修回日期:2022-06-30 发布日期:2022-11-29
作者简介:樊江川（1986—），男，硕士，高级工程师，从事配电网规划研究与评审，E-mail：fjc1986@163.com;于昊正（1992—），男，硕士，工程师，从事配电网规划与新能源研究，E-mail：yhz29@qq.com;刘慧婷（1997—），女，通信作者，硕士，从事电力系统负荷预测研究，E-mail：liuhuiting0219@163.com
基金资助:
河北省自然科学基金资助项目（促进风电消纳的区域电-热综合能源系统协同优化调度研究，E2019203514）

Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network

FAN Jiangchuan¹, YU Haozheng¹, LIU Huiting², YANG Lijun², AN Jiakun³

1. Economic and Technological Research Institute of Henan Electric Power Company, Zhengzhou 450002, China;
2. Key Lab of Power Electronics for Energy Conservation and Motor Drive of Hebei Province, Yanshan University, Qinhuangdao 066004, China;
3. Economic and Technological Research Institute of Hebei Electric Power Company, Shijiazhuang 050011, China

Received:2021-12-06 Revised:2022-06-30 Published:2022-11-29
Supported by:
This work is supported by Natural Science Foundation of Hebei Province (Research on Collaborative Optimal Dispatching of Regional Power-Heat Integrated Energy System to Promote Wind Power Consumption, No.E2019203514).

摘要/Abstract

摘要： 短期电力负荷预测是电力部门进行电网规划和运行调度的重要工作之一，针对负荷数据的时序性特征，为提升电力负荷预测精度，建立了一种基于多分支门控残差卷积神经网络(residual gated convolutional neural network，RGCNN)的短期电力负荷预测模型。该模型首先采用多分支门控残差卷积神经网络对历史负荷的周周期特征、日周期特征、近邻特征进行深度特征提取；其次为增加模型的非线性拟合能力，采用注意力机制对权重进一步合理分配；最后通过归一化指数函数计算后输出负荷预测结果。使用2016年某电力竞赛数据进行实验，通过与4种常用模型对比，该模型预测结果的平均绝对百分误差（MAPE）评价指标下降了0.02%~0.70%，验证了该模型提高负荷预测精度的有效性。

关键词: 短期负荷预测, 多分支神经网络, 门控残差卷积神经网络, 注意力机制, 特征提取

Abstract: Short-term load forecasting is one of the important tasks for power utilities to formulate grid planning and scheduling plans. Considering the temporal characteristics of the load data, in order to improve the prediction accuracy of power load, a short-term load forecasting model is established based on multi-branch residual gated convolution neural network (RGCNN). Firstly, the multi-branch residual gated convolution neural network is used to extract the weekly cycle, daily cycle and the nearest neighbor cycle of the historical load data. Secondly, the attention mechanism is used to distribute the weight reasonably to increase the nonlinear fitting ability of the model. Finally, the load forecasting result is output after normalized exponential function calculation. Experiments are carried out with the data of a power competition in 2016. Compared with the four typical forecasting models, the proposed model provides the prediction result with the MAPE evaluation indicator decreased by 0.02%-0.70%, which verifies the effectiveness of the proposed model in improving the forecasting accuracy.

Key words: short-term load forecasting, multi-branch neural network, RGCNN, attention mechanism, feature extraction

樊江川, 于昊正, 刘慧婷, 杨丽君, 安佳坤. 基于多分支门控残差卷积神经网络的短期电力负荷预测[J]. 中国电力, 2022, 55(11): 155-162,174.

FAN Jiangchuan, YU Haozheng, LIU Huiting, YANG Lijun, AN Jiakun. Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network[J]. Electric Power, 2022, 55(11): 155-162,174.

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https://www.electricpower.com.cn/CN/Y2022/V55/I11/155

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基于多分支门控残差卷积神经网络的短期电力负荷预测

Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network

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基于多分支门控残差卷积神经网络的短期电力负荷预测

Short-Term Load Forecasting Based on Multi-branch Residual Gated Convolution Neural Network

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