TY - JOUR
TI - Hybrid artificial neural network system for short-term load forecasting
AU - Ilić Slobodan A
AU - Vukmirović Srđan
AU - Erdeljan Aleksandar M
AU - Kulić Filip J
JN - Thermal Science
PY - 2012
VL - 16
IS - 11
SP - 215
EP - 224
PT - Article
AB - 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).