HYBRID ARTIFICIAL NEURAL NETWORK SYSTEM FOR SHORT-TERM LOAD FORECASTING

Abstract

This paper presents a novel hybrid method for Short-Term Load Forecasting (STLF). he system comprises of two Artificial Neural Networks (ANN), assembled in a ierarchical order. The first ANN is a Multilayer Perceptron (MLP) which functions s integrated load predictor (ILP) for the forecasting day. The output of the ILP is hen fed to another, more complex MLP, which acts as an hourly load predictor HLP) for a forecasting day. By using a separate ANN that predicts the integral of the oad (ILP), additional information is presented to the actual forecasting ANN (HLP), hile keeping its input space relatively small. This property enables online training nd adaptation, as new data become available, because of the short training time. ifferent sizes of training sets have been tested, and the optimum of 30 day sliding ime-window has been determined. The system has been verified on recorded data rom Serbian electrical utility company. The results demonstrate better efficiency of he proposed method in comparison to non-hybrid methods because it produces better orecasts and yields smaller mean average percentage error (MAPE).

Keywords

Dates

  • Submission Date2012-01-30
  • Revision Date2012-03-12
  • Acceptance Date2012-03-22

DOI Reference

10.2298/TSCI120130073I

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