Classification of day-ahead prices in Asia´s first liberalized electricity market using PNN

A number of factors determined the outcome of electricity prices and exhibits a very complicated and irregular fluctuation. The accurate forecasting of various approaches is high in forecasting errors. In this work an application of probabilistic neural networks (PNN) mode is applied to national electricity market of Singapore (NEMS), i.e. Asia´s first liberalized electricity market. All market participants expect electricity price classifications than the forecasting prices for making decisions. Various price thresholds are used to classify the electricity prices. The proposed PNN model results show a better and efficient performance for classification of electricity market prices.