An application of artificial neural networks (ANNs) to short-term load forecasting is presented in this paper. An algorithm using cascaded learning together with historical load and weather data is proposed to forecast half-hourly power system load for the next 24 hours. This cascaded neural network algorithm (CANNs) includes peak, minimum and daily energy prediction as additional input data for the final forecast stage. These additional input data are predicted using the first (ANNs) model. The networks are trained and tested on the electric power system of Kuwait. The absolute average forecasting error is reduced from 3.367% to 2.707% by applying CANNs as compared to the conventional ANNs. Simulation results indicate that the developed forecasting approach is effective and point to the potential of the methodology for economic applications.
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