DocumentCode :
3642294
Title :
Forecasting prices of electricity on HUPX
Author :
Dino Mileta;Zdenko Šimić;Minea Skok
Author_Institution :
Energy Institute Hrvoje Pozar Savska cesta 163, 10 001, Zagreb, Croatia
fYear :
2011
fDate :
5/1/2011 12:00:00 AM
Firstpage :
204
Lastpage :
208
Abstract :
The development of new simulation techniques by using Artificial Intelligence (AI) has become an improved tool to better forecast energy prices. This paper demonstrates building and validating a short term model for Hungarian day ahead power market - HUPX electricity price forecasting. This models takes into account multiple sources of information; the data set used is a table of historical hourly loads, electricity prices and other regional information´s. Paper is discussing the application of intelligent systems to short term electricity prices forecasting and outlining proposed research direction.
Keywords :
"Artificial neural networks","Electricity","Forecasting","Data models","Load modeling","Predictive models","Biological neural networks"
Publisher :
ieee
Conference_Titel :
Energy Market (EEM), 2011 8th International Conference on the European
Print_ISBN :
978-1-61284-285-1
Type :
conf
DOI :
10.1109/EEM.2011.5953009
Filename :
5953009
Link To Document :
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