基于特征选择和组合模型的短期电力负荷预测

doi:10.11930/j.issn.1004-9649.202111045

中国电力 ›› 2022, Vol. 55 ›› Issue (7): 121-127.DOI: 10.11930/j.issn.1004-9649.202111045

基于特征选择和组合模型的短期电力负荷预测

徐宇颂^1,2, 邹山花³, 卢先领^1,2

1. 江南大学 “轻工过程先进控制”教育部重点实验室，江苏无锡 214122;
2. 江南大学物联网工程学院，江苏无锡 214122;
3. 江苏省物联网应用技术重点建设实验室，江苏无锡 214100

收稿日期:2021-11-10 修回日期:2022-05-16 出版日期:2022-07-28 发布日期:2022-07-20
作者简介:徐宇颂（1996—），男，硕士，从事短期电力负荷预测研究，E-mail：2459894852@qq.com;邹山花（1972—），女，副教授，从事物联网应用技术研究，E-mail：123066625@qq.com;卢先领（1972—），男，通信作者，教授，从事无线传感器网络、大数据和数字医疗研究，E-mail：jnluxl@jiangnan.edu.cn
基金资助:
国家自然科学基金资助项目（61573167）；教育部科技发展中心“云数融合科教创新”基金资助项目（2017A13055）。

Short-Term Load Forecasting Based on Feature Selection and Combination Model

XU Yusong^1,2, ZOU Shanhua³, LU Xianling^1,2

1. Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education, Jiangnan University, Wuxi 214122, China;
2. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China;
3. Jiangsu Key Construction Laboratory of IoT Application Technology, Wuxi 214100, China

Received:2021-11-10 Revised:2022-05-16 Online:2022-07-28 Published:2022-07-20
Supported by:
This work is supported by National Natural Science Foundation of China (No.61573167), Foundation for “Integration of Cloud Computing and Big Data” of Innovation of Science and Education (No.2017 A13055)

摘要/Abstract

摘要： 提出基于特征选择和组合模型的短期电力负荷预测方法。首先将特征向量按特点分为2类，分别使用斯皮尔曼相关系数、最大相关最小冗余算法进行选择，依据贝叶斯信息量准则确定最优特征向量维度。然后使用3个不同的核函数建立单核递归支持向量回归模型并完成预测。最后构建神经网络，进行实验分析。仿真结果表明所提方法具有较高的预测精度与鲁棒性。

关键词: 短期负荷预测, 支持向量回归, 浅层神经网络, 组合模型

Abstract: A short-term load forecasting method based on feature selection and combination model is proposed. At first, the method divides the feature vectors into two sets according to the individual characteristics. Spearman rank-order correlation coefficient and max-relevance & min-redundancy algorithm are individually employed for selection. Bayesian information criterion is used to get the dimension of the optimal feature vector. And then, three different simple-kernel based support vector regression models are built using three kernel functions respectively and complete prediction. Finally, a neural network is set up for experimental analysis. The simulation results show that the proposed combination model has a great high forecasting accuracy and robustness.

Key words: short-term load forecasting, support vector regression, shallow neural network, combination model

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.

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参考文献

[1] BARMAN M, DEV CHOUDHURY N B, SUTRADHAR S. A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India[J]. Energy, 2018, 145: 710–720.
[2] 苏振宇, 龙勇, 赵丽艳. 基于regARIMA模型的月度负荷预测效果研究[J]. 中国电力, 2018, 51(5): 166–171
SU Zhenyu, LONG Yong, ZHAO Liyan. Study on the monthly power load forecasting performance based on regARIMA model[J]. Electric Power, 2018, 51(5): 166–171
[3] 张林, 罗晓初, 徐瑞林, 等. 基于时间序列的电力负荷预测新算法研究[J]. 电网技术, 2006, 30(增刊2): 595–599
ZHANG Lin, LUO Xiaochu, XU Ruilin, et al. Study on novel algorithm based on time series in power system load forecasting[J]. Power System Technology, 2006, 30(S2): 595–599
[4] 方仍存, 周建中, 彭兵, 等. 电力负荷混沌动力特性及其短期预测[J]. 电网技术, 2008, 32(4): 61–66
FANG Rengcun, ZHOU Jianzhong, PENG Bing, et al. Chaotic dynamics of power load and its short-term forecasting[J]. Power System Technology, 2008, 32(4): 61–66
[5] 于军琪, 聂己开, 赵安军, 等. 基于特征挖掘的ARIMA-GRU短期电力负荷预测[J]. 电力系统及其自动化学报, 2022, 34(3): 91–99
YU Junqi, NIE Jikai, ZHAO Anjun, et al. ARIMA-GRU short-term power load forecasting based on feature mining[J]. Proceedings of the CSU-EPSA, 2022, 34(3): 91–99
[6] AHMAD A, JAVAID N, MATEEN A, et al. Short-term load forecasting in smart grids: an intelligent modular approach[J]. Energies, 2019, 12(1): 164.
[7] 祝学昌. 基于IFOA-GRNN的短期电力负荷预测方法研究[J]. 电力系统保护与控制, 2020, 48(9): 121–127
ZHU Xuechang. Research on short-term power load forecasting method based on IFOA-GRNN[J]. Power System Protection and Control, 2020, 48(9): 121–127
[8] 张淑清, 段晓宁, 张立国, 等. Tsne降维可视化分析及飞蛾火焰优化ELM算法在电力负荷预测中应用[J]. 中国电机工程学报, 2021, 41(9): 3120–3130
ZHANG Shuqing, DUAN Xiaoning, ZHANG Liguo, et al. Tsne dimension reduction visualization analysis and moth flame optimized ELM algorithm applied in power load forecasting[J]. Proceedings of the CSEE, 2021, 41(9): 3120–3130
[9] 吕海灿, 王伟峰, 赵兵, 等. 基于Wide&Deep-LSTM模型的短期台区负荷预测[J]. 电网技术, 2020, 44(2): 428–436
Lü Haican, WANG Weifeng, ZHAO Bing, et al. Short-term substation load forecast based on Wide & Deep-LSTM model[J]. Power System Technology, 2020, 44(2): 428–436
[10] 朱凌建, 荀子涵, 王裕鑫, 等. 基于CNN-Bi LSTM的短期电力负荷预测[J]. 电网技术, 2021, 45(11): 4532–4539
ZHU Lingjian, XUN Zihan, WANG Yuxin, et al. Short-term power load forecasting based on CNN-BiLSTM[J]. Power System Technology, 2021, 45(11): 4532–4539
[11] 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
[12] 王新刚, 朱彬若, 顾臻. 基于综合能源计量大数据的中长期用电量预测[J]. 中国电力, 2021, 54(10): 211–216
WANG Xingang, ZHU Binruo, GU Zhen. Mid-and-long term load forecasting based on integrated power consumption data[J]. Electric Power, 2021, 54(10): 211–216
[13] 侯慧, 王晴, 赵波, 等. 关键信息缺失下基于相空间重构及机器学习的电力负荷预测[J]. 电力系统保护与控制, 2022, 50(4): 75–82
HOU Hui, WANG Qing, ZHAO Bo, et al. Power load forecasting without key information based on phase space reconstruction and machine learning[J]. Power System Protection and Control, 2022, 50(4): 75–82
[14] 李伟, 闫宁, 张振刚. 基于粗糙集的混合支持向量机长期电力负荷预测研究[J]. 电力系统保护与控制, 2010, 38(13): 31–34
LI Wei, YAN Ning, ZHANG Zhengang. Study on long-term load forecasting of MIX-SVM based on rough set theory[J]. Power System Protection and Control, 2010, 38(13): 31–34
[15] 欧阳俊, 陆锋, 刘兴权, 等. 基于多核混合支持向量机的城市短时交通预测[J]. 中国图象图形学报, 2010, 15(11): 1688–1695
OUYANG Jun, LU Feng, LIU Xingquan, et al. Short-term urban traffic forecasting based on multi-kernel SVM model[J]. Journal of Image and Graphics, 2010, 15(11): 1688–1695
[16] CHE J X, WANG J Z. Short-term load forecasting using a kernel-based support vector regression combination model[J]. Applied Energy, 2014, 132: 602–609.
[17] ZARE-NOGHABI A, SHABANZADEH M, SANGRODY H. Medium-term load forecasting using support vector regression, feature selection, and symbiotic organism search optimization[C]//2019 IEEE Power & Energy Society General Meeting. Atlanta, GA, USA. IEEE, 2019: 1-5.
[18] 宋国锋. 一种基于AICc的新信息准则-bAICc[D]. 长春: 吉林大学, 2017.
SONG Guofeng. A new AICc-based information criterion-bAICc[D]. Changchun: Jilin University, 2017.
[19] WOLPERT D H, MACREADY W G. No free lunch theorems for optimization[J]. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67–82.
[20] CORTES C, VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273–297.
[21] 金樑. SVM与神经网络的组合模型在短期电力负荷预测中的应用研究[D]. 长春: 吉林大学, 2018.
JIN Liang. Research on short-term power load forecasting based on combination model of SVM and neural network[D]. Changchun: Jilin University, 2018.
[22] MARQUARDT D W. An algorithm for least-squares estimation of nonlinear parameters[J]. Journal of the Society for Industrial and Applied Mathematics, 1963, 11(2): 431–441.
[23] 孔祥玉, 郑锋, 鄂志君, 等. 基于深度信念网络的短期负荷预测方法[J]. 电力系统自动化, 2018, 42(5): 133–139
KONG Xiangyu, ZHENG Feng, E Zhijun, et al. Short-term load forecasting based on deep belief network[J]. Automation of Electric Power Systems, 2018, 42(5): 133–139
[24] 陈如意, 江军, 陈珉, 等. 基于最大信息系数的变压器过热故障特征选择[J]. 电力工程技术, 2020, 39(2): 140–145
CHEN Ruyi, JIANG Jun, CHEN Min, et al. Feature selection of dissolved gases in power transformer based on maximal information coefficient[J]. Electric Power Engineering Technology, 2020, 39(2): 140–145
[25] 谢家安. 基于体感温度的电力系统负荷分类及负荷预测[J]. 电网与清洁能源, 2012, 28(8): 24–28
XIE Jiaan. Load classification and load forecasting of power system based on apparent temperature[J]. Power System and Clean Energy, 2012, 28(8): 24–28

基于特征选择和组合模型的短期电力负荷预测

Short-Term Load Forecasting Based on Feature Selection and Combination Model

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基于特征选择和组合模型的短期电力负荷预测

Short-Term Load Forecasting Based on Feature Selection and Combination Model

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