Short Term Load Forecasting: A Dynamic Neural Network Based Genetic Algorithm Optimization

被引：0
|
作者：
Wang, Yan [1 ]
Ojleska, Vesna [2 ]
Jing, Yuanwei [3 ]
K.-Gugulovska, Tatjana [2 ]
Dimirovski, Georgi M. [2 ,4 ]
机构：
[1] Northeastern Univ, Shenyang, Peoples R China
[2] SS Cyril & Methodius Univ, Fac EEIT, Skopje, North Macedonia
[3] NE Univ, Coll Informat Sci Engn, Shenyang, Peoples R China
[4] Dogus Univ, Comp Eng & Control Eng, Istanbul, Turkey
来源：
PROCEEDINGS OF 14TH INTERNATIONAL POWER ELECTRONICS AND MOTION CONTROL CONFERENCE (EPE-PEMC 2010) | 2010年
关键词：
Distribution of electrical energy; emerging technology; energy system management; genetic algorithm; modeling; neural network;
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：
The short term load forecasting plays a significant role in the management of power system supply for countries and regions, in particular in cases of insufficient electric energy for increased needs. A back-propagation artificial neural-network (BP-ANN) genetic algorithm (GA) based optimizing technique for improved accuracy of predictions short term loads is proposed. With GA's optimizing and BP-ANN's dynamic capacity, the weighted GA optimization is realized by selection, crossing and mutation operations. The performance of the proposed technique has been tested using load time-series from a real-world electrical power system. Its prediction has been compared to those of obtained by only back-propagation based neural-network techniques. Simulation results demonstrated that the here proposed technique possesses superior performance thus guarantees better forecasting
引用
收藏
页数：5
相关论文
共 50 条
  • [31] Feature Selection and Optimization of Artificial Neural Network for Short Term Load Forecasting
    Eljazzar, Maged M.
    Hemayed, Elsayed E.
    PROCEEDINGS OF 2016 EIGHTEENTH INTERNATIONAL MIDDLE EAST POWER SYSTEMS CONFERENCE (MEPCON), 2016, : 827 - 831
  • [32] Short-Term Load Forecasting Based on Pelican Optimization Algorithm and Dropout Long Short-Term Memories-Fully Convolutional Neural Network Optimization
    Wang, Haonan
    Huang, Shan
    Yin, Yue
    Gu, Tingyun
    ENERGIES, 2024, 17 (23)
  • [33] Short-Term Load Forecasting Based on RBF Neural Network
    Zhao, Bing
    Liang, Yue
    Gao, Xin
    Liu, Xin
    3RD ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2018), 2018, 1069
  • [34] Neural Network Based Approach for Short-Term Load Forecasting
    Osman, Zainab H.
    Awad, Mohamed L.
    Mahmoud, Tawfik K.
    2009 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION, VOLS 1-3, 2009, : 1162 - +
  • [35] Artificial neural network based short-term load forecasting
    Munkhjargal, S
    Manusov, VZ
    KORUS 2004, VOL 1, PROCEEDINGS, 2004, : 262 - 264
  • [36] Short-term load forecasting based on fuzzy neural network
    Wang, Cuiru
    Cui, Zhikun
    Chen, Qi
    IITA 2007: WORKSHOP ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, PROCEEDINGS, 2007, : 335 - 338
  • [37] Short-term Load Forecasting Based on BP Neural Network
    Li Yan-bin
    Li Peng
    Li Guan-hong
    ICPOM2008: PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE OF PRODUCTION AND OPERATION MANAGEMENT, VOLUMES 1-3, 2008, : 1182 - 1186
  • [38] Study of Artificial Neural Network Based Short Term Load Forecasting
    Webberley, Ashton
    Gao, David Wenzhong
    2013 IEEE POWER AND ENERGY SOCIETY GENERAL MEETING (PES), 2013,
  • [39] Short-term load forecasting based on fuzzy neural network
    DONG Liang
    MU Zhichun (Information Engineering School
    Journal of University of Science and Technology Beijing(English Edition), 1997, (03) : 46 - 48
  • [40] Short Term Load Forecasting based on Improved RBF Neural Network
    Zhang, Hong
    Lei, Zhiguo
    Guo, Jian
    Pian, Zhaoyu
    ENERGY DEVELOPMENT, PTS 1-4, 2014, 860-863 : 2610 - +
12345