Power load forecasting with least squares support vector machines and chaos theory

被引：0
|
作者：
Wu, Haishan [1 ]
Chang, Xiaoling [2 ]
机构：
[1] China Univ Min & Technol, Coll Informat & Elect Engn, Xuzhou 221008, Jiangsu, Peoples R China
[2] Huazhong Univ Sci & Technol, Dept Control Sci & Engn, Wuhan 430074, Peoples R China
来源：
WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS | 2006年
关键词：
least squares support vector machines; wavelet transform; chaos theory; load forecasting;
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：
In this paper, a novel approach to power load forecasting based on least squares support vector machines (LS-SVM) and chaos theory is presented. First, with the data from EUNITE network, we find the chaotic characteristics of the daily peak load series by analyzing the largest Lyapunov exponent and power spectrum. Average Mutual Information (AMI) method is used to find the optimal time lag. Then the time series is decomposed by wavelet transform. Cao's method is adopted to find the optimal embedding dimension of the decomposed series of each level. At last, with the optimal time lag and embedding dimension, LS-SVM is used to predict future load series of each level. The reconstruction of predicted time series is used as the final forecasting result. The mean absolute percentage error (MAPE) is 1.1013% and the maximum error is 25.1378 MW, which show that this approach is applicable for power load forecasting.
引用
收藏
页码：4369 / +
页数：2
相关论文
共 50 条
  • [1] Power load forecasting with least squares support vector machines and chaos theory
    Wu, HS
    Zhang, S
    [J]. PROCEEDINGS OF THE 2005 INTERNATIONAL CONFERENCE ON NEURAL NETWORKS AND BRAIN, VOLS 1-3, 2005, : 1020 - 1024
  • [2] Least Squares Support Vector Machines With Wavelet Transform for Power Load Forecasting
    Chen Qisong
    Wu Yun
    Chen Xiaowei
    Zhang Xin
    [J]. ICCSE 2008: PROCEEDINGS OF THE THIRD INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE & EDUCATION: ADVANCED COMPUTER TECHNOLOGY, NEW EDUCATION, 2008, : 1181 - 1186
  • [3] Short-term power load forecasting with least squares support vector machines and Wavelet Transform
    Chen, Qi-Song
    Zhang, Xin
    Xiong, Shi-Huan
    Chen, Xiao-Wei
    [J]. PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2008, : 1425 - +
  • [4] Load forecasting using fixed-size Least Squares Support Vector Machines
    Espinoza, M
    Suykens, JAK
    De Moor, B
    [J]. COMPUTATIONAL INTELLIGENCE AND BIOINSPIRED SYSTEMS, PROCEEDINGS, 2005, 3512 : 1018 - 1026
  • [5] Traffic forecasting using least squares support vector machines
    Zhang, Yang
    Liu, Yuncai
    [J]. TRANSPORTMETRICA, 2009, 5 (03): : 193 - 213
  • [6] Using Least Squares Support Vector Machines in Short-term Electrical Load Forecasting
    Li Jian
    Jiang Zhen-huan
    [J]. 2009 INTERNATIONAL CONFERENCE ON MANAGEMENT SCIENCE & ENGINEERING (16TH), VOLS I AND II, CONFERENCE PROCEEDINGS, 2009, : 1761 - 1767
  • [7] Short-term electrical load forecasting using least squares support vector machines
    Li, YC
    Fang, TJ
    Yu, EK
    [J]. POWERCON 2002: INTERNATIONAL CONFERENCE ON POWER SYSTEM TECHNOLOGY, VOLS 1-4, PROCEEDINGS, 2002, : 230 - 233
  • [8] Chaos control using least-squares support vector machines
    Suykens, J.A.K.
    Vandewalle, J.
    [J]. International Journal of Circuit Theory and Applications, 27 (06): : 605 - 615
  • [9] Chaos control using least-squares support vector machines
    Suykens, JAK
    Vandewalle, J
    [J]. INTERNATIONAL JOURNAL OF CIRCUIT THEORY AND APPLICATIONS, 1999, 27 (06) : 605 - 615
  • [10] Streamflow forecasting using least-squares support vector machines
    Shabri, Ani
    Suhartono
    [J]. HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES, 2012, 57 (07): : 1275 - 1293
12345