Thermal Science 2012 Volume 16, Issue suppl. 1, Pages: 215-224
https://doi.org/10.2298/TSCI120130073I
Full text ( 635 KB)
Cited by
Hybrid artificial neural network system for short-term load forecasting
Ilić Slobodan A. (Department of Computing and Control, Faculty of Technical Sciences, Novi Sad)
Vukmirović Srđan M. (Department of Computing and Control, Faculty of Technical Sciences, Novi Sad)
Erdeljan Aleksandar M. (Department of Computing and Control, Faculty of Technical Sciences, Novi Sad)
Kulić Filip J. (Department of Computing and Control, Faculty of Technical Sciences, Novi Sad)
This paper presents a novel hybrid method for Short-Term Load Forecasting
(STLF). The system comprises of two Artificial Neural Networks (ANN),
assembled in a hierarchical order. The first ANN is a Multilayer Perceptron
(MLP) which functions as integrated load predictor (ILP) for the forecasting
day. The output of the ILP is then 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 load (ILP), additional
information is presented to the actual forecasting ANN (HLP), while keeping
its input space relatively small. This property enables online training and
adaptation, as new data become available, because of the short training time.
Different sizes of training sets have been tested, and the optimum of 30 day
sliding time-window has been determined. The system has been verified on
recorded data from Serbian electrical utility company. The results
demonstrate better efficiency of the proposed method in comparison to
non-hybrid methods because it produces better forecasts and yields smaller
mean average percentage error (MAPE).
Keywords: STLF, MLP, prediction model, Hybrid Neural Network Structure