基于VMD-CNN-BIGRU的电力系统短期负荷预测

doi:10.11930/j.issn.1004-9649.202206097

中国电力 ›› 2022, Vol. 55 ›› Issue (10): 71-76.DOI: 10.11930/j.issn.1004-9649.202206097

基于VMD-CNN-BIGRU的电力系统短期负荷预测

杨胡萍¹, 余阳², 汪超¹, 李向军³, 胡奕涛¹, 饶楚楚¹

1. 南昌大学信息工程学院，江西南昌　330031;
2. 广东电网有限责任公司肇庆供电局，广东肇庆　526040;
3. 南昌大学软件学院，江西南昌　330031

收稿日期:2022-06-22 修回日期:2022-09-02 发布日期:2022-10-20
作者简介:杨胡萍（1964—），女，教授，硕士生导师，从事电力系统继电保护与控制研究，E-mail：yhping123@163.com;余阳（1993—），男，硕士研究生，从事电力系统短期负荷预测研究，E-mail：18720920217@sina.cn;汪超（1997—），男，通信作者，硕士研究生，从事电力系统保护与控制研究，E-mail：1071327846@qq.com;李向军（1972—），男，教授，从事网络空间安全、人工智能和数据挖掘研究，E-mail：lixiangjun@ncu.edu.cn
基金资助:
国家自然科学基金资助项目（61862042）；国网江西省电力有限公司南昌市昌北供电分公司科技项目（CX202105280048）。

Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU

YANG Huping¹, YU Yang², WANG Chao¹, LI Xiangjun³, HU Yitao¹, RAO Chuchu¹

1. School of Information Engineering, Nanchang University, Nanchang 330031, China;
2. Zhaoqing Power Supply Bureau of Guangdong Power Grid Co., Ltd., Zhaoqing 526040 , China;
3. School of Software, Nanchang University, Nanchang 330031, China

Received:2022-06-22 Revised:2022-09-02 Published:2022-10-20
Supported by:
This work is supported by National Natural Science Foundation of China (No.61862042) and Science & Technology Project of State Grid Jiangxi Electric Power Co., Ltd. Nanchang Changbei Power Supply Branch (No.CX202105280048).

摘要/Abstract

摘要： 为提高负荷预测精度，考虑了历史负荷本身内在规律及外部影响因素，提出一种基于变分模态分解（variational modal decomposition，VMD） –卷积神经网络（convolutional neural networks，CNN） –双向门控循环单元（bi-directional gated recurrent unit，BIGRU）混合网络的短期负荷预测方法，改善了训练时长和预测效果。通过仿真分析验证了所提方法的有效性，且该方法与其他模型相比有更高的负荷预测精度和更强的鲁棒性，能够提高电力系统短期负荷预测的精确度。

关键词: 短期负荷预测, 变分模态分解, 卷积神经网络, 双向门控循环单元

Abstract: In order to improve the accuracy of load prediction, taking into account the internal laws and external influencing factors of historical load itself, a kind of variational modal decomposition (VMD) -convolutional neural networks (CNN) -bi-directional gated recurrent units are proposed. BIGRU) short-term load prediction method for hybrid networks, improving training duration and prediction results. The effectiveness of the proposed method is verified by simulation analysis, and the method has higher load prediction accuracy and stronger robustness than other models, which can improve the accuracy of short-term load prediction of power system.

Key words: short load forecasting, variational modal decomposition, convolutional neural network, bi-directional gated recurrent unit

杨胡萍, 余阳, 汪超, 李向军, 胡奕涛, 饶楚楚. 基于VMD-CNN-BIGRU的电力系统短期负荷预测[J]. 中国电力, 2022, 55(10): 71-76.

YANG Huping, YU Yang, WANG Chao, LI Xiangjun, HU Yitao, RAO Chuchu. Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU[J]. Electric Power, 2022, 55(10): 71-76.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202206097

https://www.electricpower.com.cn/CN/Y2022/V55/I10/71

参考文献

[1] 李正浩, 李孟凡. 基于深度学习的智能型负荷预测方法的研究[J]. 智慧电力, 2020, 48(10): 78–85,112
LI Zhenghao, LI Mengfan. Smart load forecasting method based on deep learning[J]. Smart Power, 2020, 48(10): 78–85,112
[2] 管霖, 卓映君, 周保荣, 等. 复杂波动时间序列的多尺度分解算法及其在可再生能源发电建模应用中的性能评估[J]. 南方电网技术, 2020, 14(6): 11–16, 32
GUAN Lin, ZHUO Yingjun, ZHOU Baorong, et al. Multi-scale decomposition algorithms for complicated fluctuant time series and their performance evaluation in renewable energy generation modeling[J]. Southern Power System Technology, 2020, 14(6): 11–16, 32
[3] 邓带雨, 李坚, 张真源, 等. 基于EEMD-GRU-MLR的短期电力负荷预测[J]. 电网技术, 2020, 44(2): 593–602
DENG Daiyu, LI Jian, ZHANG Zhenyuan, et al. Short-term electric load forecasting based on EEMD-GRU-MLR[J]. Power System Technology, 2020, 44(2): 593–602
[4] 王帅, 王文爽, 孙伟, 等. 基于粗糙集和BP网络的微网短期负荷预测[J]. 控制工程, 2018, 25(8): 1528–1533
WANG Shuai, WANG Wenshuang, SUN Wei, et al. Short-term load forecasting of micro grid based on rough sets and BP neural network[J]. Control Engineering of China, 2018, 25(8): 1528–1533
[5] 苏尤丽. 基于人工神经网络的微电网短期负荷预测[J]. 内蒙古师范大学学报(自然科学汉文版), 2016, 45(1): 43–45
SU Youli. A short term load forecasting of micro-grid based on artificial neural network[J]. Journal of Inner Mongolia Normal University (Natural Science Edition), 2016, 45(1): 43–45
[6] 闫重熙, 陈皓. 基于改进天牛须搜索算法优化LSSVM短期电力负荷预测方法研究[J]. 电测与仪表, 2020, 57(6): 6–11,18
YAN Chongxi, CHEN Hao. Research of LSSVM short-term load forecasting method based on the improved beetle antennae search algorithm[J]. Electrical Measurement & Instrumentation, 2020, 57(6): 6–11,18
[7] 李霄, 王昕, 郑益慧, 等. 基于改进最小二乘支持向量机和预测误差校正的短期风电负荷预测[J]. 电力系统保护与控制, 2015, 43(11): 63–69
LI Xiao, WANG Xin, ZHENG Yihui, et al. Short-term wind load forecasting based on improved LSSVM and error forecasting correction[J]. Power System Protection and Control, 2015, 43(11): 63–69
[8] 王小君, 毕圣, 徐云鹍, 等. 基于数据挖掘技术和支持向量机的短期负荷预测[J]. 电测与仪表, 2016, 53(10): 62–67
WANG Xiaojun, BI Sheng, XU Yunkun, et al. Short-term load forecasting based on support vector machines and data mining technology[J]. Electrical Measurement & Instrumentation, 2016, 53(10): 62–67
[9] 于昕妍, 沈艳霞, 陈杰, 等. 考虑概率区间的微电网短期负荷多目标预测方法[J]. 电子学报, 2017, 45(4): 930–936
YU Xinyan, SHEN Yanxia, CHEN Jie, et al. A multi-objective prediction method for short-term microgrid load considering interval probability[J]. Acta Electronica Sinica, 2017, 45(4): 930–936
[10] 李鹏, 何帅, 韩鹏飞, 等. 基于长短期记忆的实时电价条件下智能电网短期负荷预测[J]. 电网技术, 2018, 42(12): 4045–4052
LI Peng, HE Shuai, HAN Pengfei, et al. Short-term load forecasting of smart grid based on long-short-term memory recurrent neural networks in condition of real-time electricity price[J]. Power System Technology, 2018, 42(12): 4045–4052
[11] 王增平, 赵兵, 纪维佳, 等. 基于GRU-NN模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(5): 53–58
WANG Zengping, ZHAO Bing, JI Weijia, et al. Short-term load forecasting method based on GRU-NN model[J]. Automation of Electric Power Systems, 2019, 43(5): 53–58
[12] 杨海柱, 江昭阳, 李梦龙, 等. 基于CS-GRU模型的短期负荷预测方法研究[J]. 传感器与微系统, 2022, 41(9): 54–57
YANG Haizhu, JIANG Zhaoyang, LI Menglong, et al. Study on short-term load forecasting method based on CS-GRU model[J]. Transducer and Microsystem Technologies, 2022, 41(9): 54–57
[13] DRAGOMIRETSKIY K, ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544.
[14] 田波, 朴在林, 郭丹, 等. 基于改进EEMD-SE-ARMA的超短期风功率组合预测模型[J]. 电力系统保护与控制, 2017, 45(4): 72–79
TIAN Bo, PIAO Zailin, GUO Dan, et al. Wind power ultra short-term model based on improved EEMD-SE-ARMA[J]. Power System Protection and Control, 2017, 45(4): 72–79
[15] 李青, 李军, 马昊. 基于互补型集成经验模态分解-模糊熵和回声状态网络的短期电力负荷预测[J]. 计算机应用, 2014, 34(12): 3651–3655,3659
LI Qing, LI Jun, MA Hao. Short-term electricity load forecasting based on complementary ensemble empirical mode decomposition-fuzzy permutation and echo state network[J]. Journal of Computer Applications, 2014, 34(12): 3651–3655,3659
[16] 李香龙, 张宝群, 张宇, 等. 基于EEMD-BP神经网络的含电采暖的配电变压器短期负荷预测[J]. 电测与仪表, 2018, 55(10): 101–107
LI Xianglong, ZHANG Baoqun, ZHANG Yu, et al. Short-term load forecasting of distribution transformer with electric heating based on EEMD-BP neutral network[J]. Electrical Measurement & Instrumentation, 2018, 55(10): 101–107
[17] LAWRENCE S, GILES C L, TSOI A C, et al. Face recognition: a convolutional neural-network approach[J]. IEEE Transactions on Neural Networks, 1997, 8(1): 98–113.
[18] 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12): 4370–4376
ZHAO Bing, WANG Zengping, JI Weijia, et al. A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power System Technology, 2019, 43(12): 4370–4376
[19] 何成兵, 王润泽, 张霄翔. 基于改进一维卷积神经网络的汽轮发电机组轴系扭振模态参数辨识[J]. 中国电机工程学报, 2020, 40(增刊1): 195–203
HE Chengbing, WANG Runze, ZHANG Xiaoxiang. Modal parameters identification of torsional vibration of turbogenerator shafting based on improved one-dimensional convolution neural network[J]. Proceedings of the CSEE, 2020, 40(S1): 195–203
[20] 胡威, 张新燕, 李振恩, 等. 基于优化的VMD-mRMR-LSTM模型的短期负荷预测[J]. 电力系统保护与控制, 2022, 50(1): 88–97
HU Wei, ZHANG Xinyan, LI Zhenen, et al. Short-term load forecasting based on an optimized VMD-m RMR-LSTM model[J]. Power System Protection and Control, 2022, 50(1): 88–97
[21] 魏震波, 余雷. 基于FFT, DC-HC及LSTM的短期负荷预测方法[J]. 智慧电力, 2022, 50(3): 37–43
WEI Zhenbo, YU Lei. Short-term load forecasting method based on FFT, DC-HC and LSTM[J]. Smart Power, 2022, 50(3): 37–43
[22] 曾囿钧, 肖先勇, 徐方维, 等. 基于CNN-BiGRU-NN模型的短期负荷预测方法[J]. 中国电力, 2021, 54(9): 17–23
ZENG Youjun, XIAO Xianyong, XU Fangwei, et al. A short-term load forecasting method based on CNN-BiGRU-NN model[J]. Electric Power, 2021, 54(9): 17–23
[23] 张震, 李孟洲, 李浩方, 等. 基于VMD-LSTM-MLR的短期电力负荷预测[J]. 水电能源科学, 2021, 39(10): 208–212
ZHANG Zhen, LI Mengzhou, LI Haofang, et al. Short-term power load forecasting based on VMD-LSTM-MLR[J]. Water Resources and Power, 2021, 39(10): 208–212
[24] 刘辉, 李侯君, 刘雨薇, 等. 基于VMD和GWO-SVR的电力负荷预测方法[J]. 现代电子技术, 2020, 43(23): 167–172
LIU Hui, LI Houjun, LIU Yuwei, et al. Power load forecasting method based on VMD and GWO-SVR[J]. Modern Electronics Technique, 2020, 43(23): 167–172
[25] 杨海柱, 田馥铭, 张鹏, 等. 基于CEEMD-FE和AOA-LSSVM的短期电力负荷预测[J]. 电力系统保护与控制, 2022, 50(13): 126–133
YANG Haizhu, TIAN Fuming, ZHANG Peng, et al. Short-term load forecasting based on CEEMD-FE-AOA-LSSVM[J]. Power System Protection and Control, 2022, 50(13): 126–133
[26] 徐宇颂, 邹山花, 卢先领. 基于特征选择和组合模型的短期电力负荷预测[J]. 中国电力, 2022, 55(7): 121–127
XU Yusong, ZOU Shanhua, LU Xianling. Short-term load forecasting based on feature selection and combination model[J]. Electric Power, 2022, 55(7): 121–127
[27] 周雪, 鲍刚, 龚顺琦. 基于参数优化的KELM和GRU的短期电力负荷预测[J]. 电子器件, 2022, 45(4): 931–938
ZHOU Xue, BAO Gang, GONG Shunqi. Short-term power load forecasting based on GRU and parameter optimized KELM[J]. Chinese Journal of Electron Devices, 2022, 45(4): 931–938

基于VMD-CNN-BIGRU的电力系统短期负荷预测

Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU

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基于VMD-CNN-BIGRU的电力系统短期负荷预测

Short-Term Load Forecasting of Power System Based on VMD-CNN-BIGRU

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