Application of Fuzzy Support Vector Machine in Short-Term Power Load Forecasting

被引：3
|
作者：
Yang, Jie [1 ]
Tang, Yachun [1 ]
Duan, Huabin [1 ]
机构：
[1] Hunan Univ Sci & Engn, Coll Informat Engn, Yongzhou, Peoples R China
来源：
JOURNAL OF CASES ON INFORMATION TECHNOLOGY | 2022年 / 24卷 / 05期
关键词：
Fuzzy Support Vector Machine; Linear Extrapolatio; Load Forecasting; Power System; Short-Term Forecast of Power;
D O I：
10.4018/JCIT.295248
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The realization of short-term load forecasting is the basis of system planning and decision-making, and it is an important index to evaluate the safety and economy of power grid. In order to accurately predict the power load under the influence of many factors, a new short-term power load prediction method based on fuzzy support vector machine and similar daily linear extrapolation is proposed, which combines the method of fuzzy support vector machine and linear extrapolation of similar days. The method first selects similar days according to the effect of integrated weather and time on load. Then the fuzzy membership of the training sample is obtained by the normalization processing, and the daily maximum and minimum load is predicted by the fuzzy support vector machine. Finally, the load prediction value is obtained by combining the load trend curve obtained by the similar daily linear extrapolation method, and this method is feasible and effective for short-term forecasting of power load.
引用
收藏
页数：10
相关论文
共 50 条
  • [31] Support vector machine and wavelet transform for short term load forecasting
    Liu, Mengliang
    Liu, Xiaohua
    Ma, Jinjun
    General System and Control System, Vol I, 2007, : 60 - 63
  • [32] Application of nuSupport Vector Regression in Short-Term Load Forecasting
    Omidi, Adnan
    Barakati, S. Masoud
    Tavakoli, Saeed
    2015 20TH CONFERENCE ON ELECTRICAL POWER DISTRIBUTION NETWORKS CONFERENCE (EPDC), 2015, : 32 - 36
  • [33] Short-term load forecasting based on correlation coefficient and weighted support vector regression machine
    Liu, Limei
    PROCEEDINGS OF THE 4TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND MANAGEMENT INNOVATION, 2015, 28 : 1077 - 1081
  • [34] Short term load forecasting model based on support vector machine
    Niu, Dong-Xiao
    Wang, Qiang
    Li, Jin-Chao
    ADVANCES IN MACHINE LEARNING AND CYBERNETICS, 2006, 3930 : 880 - 888
  • [35] Short-term load forecasting using support vector machine with SCE-UA algorithm
    Li, Gang
    Cheng, Chun-tian
    Lin, Jian-yi
    Zeng, Yun
    ICNC 2007: THIRD INTERNATIONAL CONFERENCE ON NATURAL COMPUTATION, VOL 1, PROCEEDINGS, 2007, : 290 - +
  • [36] Short-Term Load Demand Forecasting Using Multinomial Naive Bayes with Support Vector Machine
    Dansalan, Jan Franchezka
    Diaz, Jedidia
    Ostia, Conrado, Jr.
    2024 16TH INTERNATIONAL CONFERENCE ON COMPUTER AND AUTOMATION ENGINEERING, ICCAE 2024, 2024, : 158 - 162
  • [37] Study on urban short-term gas load forecasting based on support vector machine model
    Zhang, Chao
    Liu, Yi
    Zhang, Hui
    Huang, Hong
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2014, 54 (03): : 320 - 325
  • [38] Short-Term Power Load Forecasting of Least Squares Support Vector Machine Based on Wavelet Transform and Drosophila Algorithm
    Zhao, Jian-Na
    He, Xiao-Bo
    PROCEEDINGS OF THE 2017 2ND INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT SCIENCE AND ECONOMICS (ICEMSE 2017), 2017, 49 : 322 - 325
  • [39] FUZZY EXPERT SYSTEMS - AN APPLICATION TO SHORT-TERM LOAD FORECASTING
    HSU, YY
    HO, KL
    IEE PROCEEDINGS-C GENERATION TRANSMISSION AND DISTRIBUTION, 1992, 139 (06) : 471 - 477
  • [40] Short-term Forecasting of PV Power Based on the Fuzzy Clustering Algorithm and Support Vector Machine in Smart Distribution Planning
    Li Shan
    Xin Peizhe
    Zou Guohui
    PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018), 2018, : 643 - 647
12345