Accuracy of ANN based methodology for load composition forecasting at bulk supply buses

Accurate prediction of load composition at bulk supply points can significantly improve power system planning, electricity market analysis and demand side management. This paper discusses an artificial neural network (ANN) based approach to forecasting load composition at the bulk supply bus based on RMS measurement of voltage, real and reactive power and local forecasted weather. Probabilistic distributions and confidence levels of the prediction under different prediction error intervals have been derived and analysed. It is demonstrated that the approach yields prediction of load composition with errors typically less than 10%.