Title :
Application of Neural Networks on Next-Day Electricity Prices Forecasting
Author :
Catalao, J.P.S. ; Mariano, S.J.P.S. ; Mendes, V.M.F. ; Ferreira, L.A.F.M.
Author_Institution :
Univ. of Beira Interior, Covilha
Abstract :
This paper presents an application for next-day electricity prices forecasting based on neural networks. Good forecasting tools hedging against daily price volatility are becoming increasingly important in nowadays competitive electricity markets, avowing misjudgement of future price movements and preventing considerable losses for consumers and producers. Next-day electricity price forecast is essential to consumers and to producers in planning the operations of their electric energy resources and for developing negotiation skills in order to achieve better profits. We evaluate the accuracy of the proposed application of neural networks for next-day electricity prices forecasting based on case studies for a real world electricity market and report our experience with this application.
Keywords :
neural nets; power engineering computing; power markets; electric energy resources; forecasting tools; neural networks applications; next-day electricity prices forecasting; Contracts; Costs; Economic forecasting; Electricity supply industry; Energy consumption; Energy resources; Government; Load forecasting; Neural networks; Power generation; electricity markets; neural networks; prices forecasting;
Conference_Titel :
Universities Power Engineering Conference, 2006. UPEC '06. Proceedings of the 41st International
Conference_Location :
Newcastle-upon-Tyne
Print_ISBN :
978-186135-342-9
DOI :
10.1109/UPEC.2006.367642