The fuzzy regression analysis as a means of electric power losses evaluation in electrical networks

Manusov, V.Z.; Mogilenko, A.V.

doi:10.1049/cp:20020067

Cited by 2 publications

(1 citation statement)

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“…Nazarko and Zalewski (1999) employed the fuzzy regression approach to forecast the peak load in power distribution systems. Manusov and Mogilenko (2002) employed the fuzzy regression to determine electric power losses in electrical networks. Sedelnikov and Manusov (2004) attempted to forecast the loss of electric power based on uncertainty by the fuzzy regression analysis.…”

Section: Introductionmentioning

confidence: 99%

A robust fuzzy linear regression model for forecasting electricity consumption of residential sector in Iran

Omrani

Naini

Alinaghian

2012

IJAMS

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Many fuzzy linear regression (FLR) models are normally very sensitive to the changes on the input data so that the results could significantly be changed even with some small change on one or more input parameters. Therefore, many researchers have been interested in creating a so called robust model to increase the reliability of the estimated parameters. In this paper, a simple robust fuzzy linear regression (RFLR) model is suggested for forecasting the electricity consumption. Our RFLR model uses the robust optimisation (RO) technique introduced by Ben- Tal and Nemirovski (2000). The performance of the proposed model is illustrated through a numerical example and is applied for estimating electricity consumption of residential sector in Iran.

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Section: Introductionmentioning

confidence: 99%