Fuzzy Markov predictor with first and second-order dependences

Author

Teixeira, Marcelo Andrade ; Zaverucha, Gerson

Author_Institution

COPPE, Univ. Fed. do Rio de Janeiro, Brazil

fYear

2002

fDate

2002

Firstpage

80

Lastpage

85

Abstract

We present two new versions of the fuzzy Markov predictor (FMP) with different dependences among the inputs: first-order and second-order dependences. The FMP is a modification of the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor. These hybrid systems are applied to the task of monthly electric load forecasting and successfully compared with one fuzzy system, and two traditional forecasting methods: Box-Jenkins and Winters exponential smoothing.

Keywords

forecasting theory; fuzzy set theory; hidden Markov models; load forecasting; electric load forecasting; first-order dependences; fuzzy Bayes predictor; fuzzy Markov predictor; hidden Markov model; numerical value prediction; second-order dependences; Computer science; Fuzzy neural networks; Fuzzy systems; Hidden Markov models; Load forecasting; Niobium compounds; Power engineering and energy; Shape; Smoothing methods; Systems engineering and theory;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2002. SBRN 2002. Proceedings. VII Brazilian Symposium on

Print_ISBN

0-7695-1709-9

Type

conf

DOI

10.1109/SBRN.2002.1181439

Filename

1181439