Title :
Electrical energy demand prediction using Artificial Neural Networks and Support Vector Regression
Author :
Ruas, Gabriel I S ; Bragatto, Ticiano A C ; Lamar, Marcus V. ; Aoki, Alexandre R. ; De Rocco, Sílvio M.
Author_Institution :
Dept. of Electr. Eng., UnB, Brasilia
Abstract :
This paper describes a short time electrical energy demand forecast system using two different techniques of artificial intelligence: recurrent artificial neural networks and support vector regression. A brief analysis of the demand over the electrical energy network connection points is also done.
Keywords :
artificial intelligence; demand forecasting; load forecasting; neural nets; power engineering computing; power grids; power markets; regression analysis; support vector machines; artificial intelligence; artificial neural networks; electrical energy demand prediction; electrical energy network connection points; electrical grid connection points; electricity market; load forecast; short time electrical energy demand forecast system; support vector regression; Artificial neural networks; Autocorrelation; Biological neural networks; Computer networks; Data analysis; Delay effects; Economic forecasting; Load forecasting; Neurons; Time series analysis; Load forecast; Neural Networks; Support Vector Regression; Time Series Prediction;
Conference_Titel :
Communications, Control and Signal Processing, 2008. ISCCSP 2008. 3rd International Symposium on
Conference_Location :
St Julians
Print_ISBN :
978-1-4244-1687-5
Electronic_ISBN :
978-1-4244-1688-2
DOI :
10.1109/ISCCSP.2008.4537451
