Research on time series based SVM model or electricity price forecasting

被引:0
|
作者
Sun Wei [1 ]
Lu Jian-chang [1 ]
Meng Ming [1 ]
机构
[1] N China Elect Power Univ, Sch Business Adm, Baoding 071003, Peoples R China
关键词
time series; support vector machine; electricity-price forecasting; electricity market;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Time series based support vector machine (SVM) model is provided for short term price forecasting. The Structure Risk Minimization (RSM) principle is embedded into the SVM, so on the basis of learning by fewer samples the presented model can conduct fast and accurate forecasting. It has better generalization. In this method, except considering main influential factors such as previous competitive load, system rotary reservation, competitive generating capacity etc, the past price data which are time series style or not hive been included as attributes in input parameters. A day ahead MCP forecasting model is established. The results show that the proposed model has better forecasting accuracy in practical application.
引用
收藏
页码:1021 / 1025
页数:5
相关论文
共 14 条
  • [1] [Anonymous], ADV KERNEL METHODS S
  • [2] Support vector machines experts for time series forecasting
    Cao, LJ
    [J]. NEUROCOMPUTING, 2003, 51 : 321 - 339
  • [3] CHEN BJ, 2001, LOAD FORECASTING USI
  • [4] HIPPER HS, 2001, IEEE T POWER SYSTEMS, V6, P44
  • [5] FUZZY EXPERT SYSTEMS - AN APPLICATION TO SHORT-TERM LOAD FORECASTING
    HSU, YY
    HO, KL
    [J]. IEE PROCEEDINGS-C GENERATION TRANSMISSION AND DISTRIBUTION, 1992, 139 (06) : 471 - 477
  • [6] Li Yuan-cheng, 2003, Proceedings of the CSEE, V23, P55
  • [7] MAO PF, 2004, EC TECHNICAL COOPERA, P59
  • [8] Forecasting next-day electricity prices by time series models
    Nogales, FJ
    Contreras, J
    Conejo, AJ
    Espínola, R
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2002, 17 (02) : 342 - 348
  • [9] Combining support vector machine learning with the discrete cosine transform in image compression
    Robinson, J
    Kecman, V
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2003, 14 (04): : 950 - 958
  • [10] Vapnik V, 1999, NATURE STAT LEARNING