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

Abstract

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.

Linear fractional programming based on triangular neutrosophic numbers

“…Neutrosophic theory means neutrosophy applied in many fields of sciences, in order to solve problems related to indeterminacy. Although intuitionistic fuzzy sets can only handle incomplete information not indeterminate, the neutrosophic set can handle both incomplete and indeterminate information (Zimmermann, 2010;Shakib et al, 2017;Foroozesh et al, 2017;Hosseini and Keshavarz, 2017;Curry and Lazzari, 2009;Faisal et al, 2011;Kapoor and Tak, 2012;Öztayşi and Sari, 2012;Omrani et al, 2012;Behret and Gumussoy, 2012;Pal and Chakraborti, 2013;Smimou, 2013;Pourjavad and Shirouyehzad, 2014;Kurdhi et al, 2015;Jana et al, 2016). Neutrosophic sets characterised by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F), where T, I, F are standard or non-standard subsets of ] -0, 1 + [.…”

Linear fractional programming based on triangular neutrosophic numbers

“…Neutrosophic theory means neutrosophy applied in many fields of sciences, in order to solve problems related to indeterminacy. Although intuitionistic fuzzy sets can only handle incomplete information not indeterminate, the neutrosophic set can handle both incomplete and indeterminate information (Zimmermann, 2010;Shakib et al, 2017;Foroozesh et al, 2017;Hosseini and Keshavarz, 2017;Curry and Lazzari, 2009;Faisal et al, 2011;Kapoor and Tak, 2012;Öztayşi and Sari, 2012;Omrani et al, 2012;Behret and Gumussoy, 2012;Pal and Chakraborti, 2013;Smimou, 2013;Pourjavad and Shirouyehzad, 2014;Kurdhi et al, 2015;Jana et al, 2016). Neutrosophic sets characterised by three independent degrees namely truth-membership degree (T), indeterminacy-membership degree (I), and falsity-membership degree (F), where T, I, F are standard or non-standard subsets of ] -0, 1 + [.…”