文献类型：期刊文献

英文题名：Analyzing the energy performance of buildings by neuro-fuzzy logic based on different factors

作者：Cao, Yan[1];Pourrostam, Towhid[2];Zandi, Yousef[3];Denic, Nebojsa[4];Cirkovic, Bogdan[5];Agdas, Alireza Sadighi[3];Selmi, Abdellatif[6,7];Vujovic, Vuk[8];Jermsittiparsert, Kittisak[9,10,11];Milic, Momir[8]

第一作者：Cao, Yan

通讯作者：Jermsittiparsert, K[1];Jermsittiparsert, K[2];Jermsittiparsert, K[3]

机构：[1]Xian Technol Univ, Sch Mechatron Engn, Xian 710021, Peoples R China;[2]Islamic Azad Univ, Dept Civil Engn, Cent Tehran Branch, Tehran, Iran;[3]Ghateh Gostar Novin Co, Tabriz, Iran;[4]Univ Prishtina, Fac Nat Sci, Kosovska Mitrovica, Serbia;[5]Univ Prishtina, Fac Tech Sci, Kosovska Mitrovica, Serbia;[6]Prince Sattam Bin Abdulaziz Univ, Coll Engn, Dept Civil Engn, Al Kharj 11942, Saudi Arabia;[7]Ecole Natl Ingenieurs Tunis ENIT, Civil Engn Lab, BP 37, Tunis 1002, Tunisia;[8]Alfa BK Univ, Fac Informat Technol, Belgrade, Serbia;[9]Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[10]Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[11]Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China

第一机构：Xian Technol Univ, Sch Mechatron Engn, Xian 710021, Peoples R China

通讯机构：[1]corresponding author), Duy Tan Univ, Inst Res & Dev, Da Nang 550000, Vietnam;[2]corresponding author), Duy Tan Univ, Fac Humanities & Social Sci, Da Nang 550000, Vietnam;[3]corresponding author), Henan Univ Econ & Law, MBA Sch, Zhengzhou 450046, Henan, Peoples R China.|[1048419]河南财经政法大学MBA学院;[10484]河南财经政法大学;

年份：2021

卷号：23

期号：12

起止页码：17349-17373

外文期刊名：ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY

收录：;Scopus(收录号：2-s2.0-85104564203);WOS:【SCI-EXPANDED(收录号:WOS:000639513200004)】；

基金：This paper is supported by Shaanxi Innovation Capability Support Plan (Grant: 2018TD-036), Shaanxi Natural Science Basic Research Project (Grant: S2019-JC-YB-2897), and Research Project of Graduate Education and Teaching Reform of Xi'an Technological University in 2017.

语种：英文

外文关键词：Building energy performance; Heating load; Cooling load; Neuro fuzzy logic

摘要：Energy performance of buildings is an important issue to estimate the energy waste of buildings and their impact on the environment, so designing energy-efficient buildings could improve their energy performance. In this case, the estimation of heating and cooling loads plays an important role in this regard. However, there are few factors with unpredictable influences on the heating and cooling loads. This study has attempted to analyze the eight parameters that can significantly affect the heating and cooling loads through the Neuro-fuzzy logic approach. Accordingly, the eight parameters of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area and glazing area distribution were considered as inputs and predicting the cooling and heating load changes was regarded as the output of this study. The model was developed and its results were measured in two regression indicators of r and RMSE. Based on the obtained results it was found that roof has the strongest impact on heating and cooling loads (RMSE: 4.3596), moreover, if two factors were concurrently changed, then the combination of relative compactness and wall area can significantly affect the heating and cooling loads (RMSE: 2.6312). The most influential combination of three factors is observed as well for the heating and cooling load and these factors are relative compactness, wall area and glazing area (0.5948). The most influential combination of three factors is observed as well for the hearing load and these factors are relative compactness, wall area and glazing area (RMSE: 0.5948 and RMSE: 1.5769 for heating and cooling load, respectively). However, Neuro-fuzzy logic showed overfitting for more than two inputs, therefore it is not recommended for more than two inputs.