DocumentCode
2018388
Title
Bayesian neural networks for electric load forecasting
Author
Tito, Edison H. ; Zaverucha, Gerson ; Vellasco, Marley ; Pacheco, Marco
Author_Institution
COPPE, Univ. Fed. do Rio de Janeiro, Brazil
Volume
1
fYear
1999
fDate
1999
Firstpage
407
Abstract
The authors apply Bayesian neural networks to electric load forecasting with real data from some Brazilian power companies. The Bayesian methods used are the Gaussian approximation and the Markov chain Monte Carlo (MCMC) methods. The results obtained with these methods are favourably compared to backpropagation and some standard statistical techniques like Box & Jenkins and Holt-Winters
Keywords
Bayes methods; Gaussian distribution; Markov processes; Monte Carlo methods; load forecasting; neural nets; power engineering computing; Bayesian methods; Bayesian neural networks; Brazilian power companies; Gaussian approximation; MCMC; Markov chain Monte Carlo; backpropagation; electric load forecasting; real data; standard statistical techniques; Backpropagation; Bayesian methods; Gaussian approximation; Gaussian distribution; Load forecasting; Monte Carlo methods; Neural networks; Predictive models; Probability; Training data;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-5871-6
Type
conf
DOI
10.1109/ICONIP.1999.844023
Filename
844023
Link To Document