A Comparative Study of Ensemble Support Vector Regression Methods for Short-term Load Forecasting

被引：0
|
作者：
Ye, Jianhua [1 ]
Yang, Li [2 ]
机构：
[1] Tianjin Univ Technol & Educ, Tianjin Key Lab Informat Sensing & Intelligent Co, Tianjin, Peoples R China
[2] State Grid Corp China, Chongqing Yongchuan Power Supply Co, Chongqing, Peoples R China
来源：
2018 5TH INTERNATIONAL CONFERENCE ON SYSTEMS AND INFORMATICS (ICSAI) | 2018年
关键词：
Short-term load forecasting (STLF); support vector regression (SVR); ensemble learning;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
The accuracy of short-term load forecasting has a vital function in the safety, stability and economic operation of the power grid. Support vector regression (SVR) have achieved good results in short-term load forecasting (STLF). In order to further enhance the performance of STLF, diverse ensemble SVR methods have been put forward in the literature. This paper is aim to compare the performance of several ensemble SVR methods with two data sets. It shows that these methods outperform SVR model. The simpler ensemble SVR methods performs better than sophisticated ones.
引用
收藏
页码：139 / 143
页数：5
相关论文
共 50 条
  • [1] A Short-term Load Forecasting Based on Support Vector Regression
    Yu, Lu
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON MECHATRONICS, ELECTRONIC, INDUSTRIAL AND CONTROL ENGINEERING, 2015, 8 : 1055 - 1059
  • [2] Application of support vector regression to temperature forecasting for short-term load forecasting
    Mori, Hiroyuki
    Kanaoka, Daisuke
    [J]. 2007 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-6, 2007, : 1085 - 1090
  • [3] Subsampled support vector regression ensemble for short term electric load forecasting
    Li, Yanying
    Che, Jinxing
    Yang, Youlong
    [J]. ENERGY, 2018, 164 : 160 - 170
  • [4] Short-term load forecasting based on support vector regression and load profiling
    Sousa, Joao C.
    Jorge, Humberto M.
    Neves, Luis P.
    [J]. INTERNATIONAL JOURNAL OF ENERGY RESEARCH, 2014, 38 (03) : 350 - 362
  • [5] Short-term load forecasting based on support vector machines regression
    Zhang, MG
    [J]. Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9, 2005, : 4310 - 4314
  • [6] A Strategy for Short-Term Load Forecasting by Support Vector Regression Machines
    Ceperic, Ervin
    Ceperic, Vladimir
    Baric, Adrijan
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2013, 28 (04) : 4356 - 4364
  • [7] Research on Short-Term Load Forecasting Based on Improved Support Vector Regression
    Wang, Baoyi
    Han, Tianyang
    Zhang, Shaomin
    [J]. PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2016), 2016, 50 : 794 - 799
  • [8] Short-term load forecasting based on support vector regression considering cooling load in summer
    Hu, Li
    Zhang, Lei
    Wang, Tao
    Li, Kai
    [J]. PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020), 2020, : 5495 - 5498
  • [9] Research on methods of short-term traffic forecasting based on support vector regression
    School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China
    [J]. Beijing Jiaotong Daxue Xuebao, 2006, 3 (19-22):
  • [10] Short-Term Load Forecasting Using Ensemble Empirical Mode Decomposition and Harmony Search Optimized Support Vector Regression
    Ye, Jianhua
    Yang, Li
    [J]. PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019), 2019, : 851 - 855
12345