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

fYear

2008

Firstpage

1431

Lastpage

1435

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ISCCSP.2008.4537451

Filename

4537451