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International Journal for Uncertainty Quantification
IF: 0.967 5-Year IF: 1.301 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN Print: 2152-5080
ISSN Online: 2152-5099

Open Access

International Journal for Uncertainty Quantification

DOI: 10.1615/Int.J.UncertaintyQuantification.2018024815
pages 543-560


Sadegh Abbaszadeh
Department of Computer Sciences, Payame Noor University, Tehran, Iran
Alireza Tavakoli
CS Group of Mathematics Department, Shahid Beheshti University, Tehran, Iran
Marjan Movahedan
Department of Computer Science, University of Regina, Regina, Canada
Peide Liu
School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan Shandong 250014, China; School of Economics and Management, Civil Aviation University of China, Tianjin 300300, China


In this paper, new aggregation operators are introduced in order to develop scoring and classifying methods in decision sciences. The proposed operators are applied to evaluate and score the energy performance of residential buildings. At first, a classical linear regression approach and a random forests method are applied to calculate the effects of eight building factors on heating load (HL) and cooling load (CL) of residential buildings. Then, two novel definitions of discrete fuzzy integrals, i.e., the Frank and Weber integrals, are adopted to score each building according to its energy efficiency. To evaluate the proposed fuzzy operators, we apply them on a standard dataset of 768 diverse residential buildings, a so-called energy efficiency dataset. The results of the energy performance by using Frank and Weber integrals on the energy efficiency dataset are compared with the outcomes of two traditional methods, i.e., TOPSIS and the Choquet integral, two popular approaches of scoring, and it is shown that the proposed fuzzy operators outperform the traditional methods.