Research on hybrid ARIMA and support vector machine model in short term load forecasting

被引:0
作者
He, Yujun [1 ]
Zhu, Youchan [2 ]
Duan, Dongxing [1 ]
机构
[1] North China Elect Power Univ, Dept Elect & Commun Engn, Hebei 071003, Peoples R China
[2] North China Elect Power Univ, Ctr Informat & Network Management, Hebei 071003, Peoples R China
来源
ISDA 2006: SIXTH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS, VOL 1 | 2006年
关键词
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In power system, due to the complexity of the historical load data and the randomness of a lot of uncertain factors influence, the observed historical data showed linear and nonlinear characteristics. As we all known, the autoregressive integrated moving average (ARIMA) is one of the popular linear models in time series forecasting, and the SVM model is the recent research trend successfully used in solving nonlinear regression and time series. problem. So in this paper, a hybrid methodology that combines both ARIAM and SVM model is presented to take. advantage of the unique strength of ARIAM and SVM models in linear and nonlinear modeling. The linear pattern of the historical load data can be dealt with ARIMA, and the nonlinear part with SVM model. The effectiveness of the model has been tested using Hebei province daily load data with satisfactory results. The experimental results showed that the hybrid model can effectively improve the forecasting accuracy achieved by either of the models used separately.
引用
收藏
页码:804 / 808
页数:5
相关论文
共 50 条
  • [21] Support vector machine model in electricity load forecasting
    Guo, Ying-Chun
    Niu, Dong-Xiao
    Chen, Yan-Xu
    PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2006, : 2892 - +
  • [22] A hybrid ARIMA and support vector machines model in stock price forecasting
    Pai, PF
    Lin, CS
    OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE, 2005, 33 (06): : 497 - 505
  • [23] A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting
    Kavousi-Fard, Abdollah
    Samet, Haidar
    Marzbani, Fatemeh
    EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (13) : 6047 - 6056
  • [24] Research on Short-Term Load Forecasting Based on Improved Support Vector Regression
    Wang, Baoyi
    Han, Tianyang
    Zhang, Shaomin
    PROCEEDINGS OF THE 2016 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL & ELECTRONICS ENGINEERING AND COMPUTER SCIENCE (ICEEECS 2016), 2016, 50 : 794 - 799
  • [25] Hybrid model for microgrid short term load forecasting based on machine learning
    Khayat, Ahmed
    Kissaoui, Mohammed
    Bahatti, Lhoussaine
    Raihani, Abdelhadi
    Errakkas, Khalid
    Atifi, Youness
    IFAC PAPERSONLINE, 2024, 58 (13): : 527 - 532
  • [26] Hybrid GA based online support vector machine model for short-term traffic flow forecasting
    Su, Haowei
    Yu, Shu
    ADVANCED PARALLEL PROCESSING TECHNOLOGIES, PROCEEDINGS, 2007, 4847 : 743 - 752
  • [27] Short-Term Load Forecasting of Power System Based on Support Vector Machine Theory
    Zou, Chao
    PROCEEDINGS OF THE 3RD INTERNATIONAL SYMPOSIUM ON NEW ENERGY AND ELECTRICAL TECHNOLOGY, 2023, 1017 : 174 - 181
  • [28] Short-term Load Forecasting of Local Power Grid Based on Support Vector Machine
    Hua, Jing
    Xiong, Wei
    Zhou, Yanping
    PROCEEDINGS OF THE 2017 7TH INTERNATIONAL CONFERENCE ON EDUCATION, MANAGEMENT, COMPUTER AND SOCIETY (EMCS 2017), 2017, 61 : 1851 - 1856
  • [29] Combining KPCA with Support Vector Regression Machine for Short-term Electricity load Forecasting
    Zhang, Caiqing
    Lu, Pan
    Liu, Zejian
    2008 INTERNATIONAL CONFERENCE ON RISK MANAGEMENT AND ENGINEERING MANAGEMENT, ICRMEM 2008, PROCEEDINGS, 2008, : 305 - 310
  • [30] Short-term Load Forecasting Approach Based on RS and PSO Support Vector Machine
    Li Jin-ying
    Li Jin-chao
    2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 8286 - +