Title :
Classification of day-ahead prices in Asia´s first liberalized electricity market using PNN
Author :
Anbazhagan, S. ; Pravin, K. ; Kumarappan, N.
Author_Institution :
Electr. Eng., Annamalai Univ., Annamalai Nagar, India
Abstract :
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.
Keywords :
economic forecasting; feedforward neural nets; pattern classification; power engineering computing; power markets; pricing; Asia; PNN model; day-ahead price classification; error forecasting; expect electricity price classifications; liberalized electricity market; national electricity market; price thresholds; probabilistic neural networks mode; uniform Singapore energy price; Asia; Computational modeling; Electricity; Electricity supply industry; Forecasting; Principal component analysis; Probabilistic logic; classification of electricity prices; price forecasting; probabilistic neural network (PNN); uniform Singapore energy price (USEP);
Conference_Titel :
Computational Intelligence for Engineering Solutions (CIES), 2013 IEEE Symposium on
Conference_Location :
Singapore
DOI :
10.1109/CIES.2013.6611746
