ANN based Electric Load Forecasting Applied to Real Time Data

被引:0
作者
Khatoon, Shahida [1 ]
Ibraheem [1 ]
Singh, Arunesh Kr [1 ]
Priti [1 ]
机构
[1] Jamia Miilia Islamia, Dept Elect Engn, New Delhi, India
来源
2015 ANNUAL IEEE INDIA CONFERENCE (INDICON) | 2015年
关键词
Load; Time series; MA; Forecasting; Time series methods; ARTIFICIAL NEURAL-NETWORKS; SYSTEM; MODEL;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The accuracy of electric load forecasts significantly affects the overall performance of power system. Some time due to complicated load pattern, forecasting becomes difficult. The object of this study is to develop more effective forecasting models, among others. This paper compares the electric load forecasting accuracy of ANN based techniques. This study investigates the time series techniques used to forecasts electric load, e.g. MA, linear trend, the exponential and parabolic trend. In the present study ANN based time series forecasting model has been developed, on hourly electric load consumption data. Data used to forecast is acquired from a distribution company located in Noida, Uttar Pradesh. These ANN based techniques are evaluated for the forecasting errors.
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页数:5
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共 15 条
  • [1] Electric load forecasting: literature survey and classification of methods
    Alfares, HK
    Nazeeruddin, M
    [J]. INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE, 2002, 33 (01) : 23 - 34
  • [2] A methodology for Electric Power Load Forecasting
    Almeshaiei, Eisa
    Soltan, Hassan
    [J]. ALEXANDRIA ENGINEERING JOURNAL, 2011, 50 (02) : 137 - 144
  • [3] A neural network short term load forecasting model for the Greek power system
    Bakirtzis, AG
    Petridis, V
    Klartzis, SJ
    Alexiadis, MC
    Maissis, AH
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1996, 11 (02) : 858 - 863
  • [4] Dash P. K., 1994, ENG INTELL SYST ELEC, V2, P185
  • [5] Short-Term Load Forecasting Based on a Semi-Parametric Additive Model
    Fan, Shu
    Hyndman, Rob J.
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2012, 27 (01) : 134 - 141
  • [6] NARMAX time series model prediction: feedforward and recurrent fuzzy neural network approaches
    Gao, Y
    Er, MJ
    [J]. FUZZY SETS AND SYSTEMS, 2005, 150 (02) : 331 - 350
  • [7] Artificial neural networks for short-term load forecasting in microgrids environment
    Hernandez, Luis
    Baladron, Carlos
    Aguiar, Javier M.
    Carro, Belen
    Sanchez-Esguevillas, Antonio
    Lloret, Jaime
    [J]. ENERGY, 2014, 75 : 252 - 264
  • [8] HOOSHMAND RA, 2013, ELECT POWER ENERGY S, V45, P313, DOI DOI 10.1016/J.IJEPES.2012.09.002
  • [9] IMPLEMENTATION OF HYBRID SHORT-TERM LOAD FORECASTING SYSTEM USING ARTIFICIAL NEURAL NETWORKS AND FUZZY EXPERT-SYSTEMS
    KIM, KH
    PARK, JK
    HWANG, KJ
    KIM, SH
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 1995, 10 (03) : 1534 - 1539
  • [10] A fuzzy inference model for short-term load forecasting
    Mamlook, Rustum
    Badran, Omar
    Abdulhadi, Emad
    [J]. ENERGY POLICY, 2009, 37 (04) : 1239 - 1248