干旱区生态环境知识资源中心(Arid): The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

Arid

DOI	10.7717/peerj-cs.856
	The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings
	Ibrahim, Dina M.; Almhafdy, Abdulbasit; Al-Shargabi, Amal A.; Alghieth, Manal; Elragi, Ahmed; Chiclana, Francisco
通讯作者	Ibrahim, DM (corresponding author)，Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah, Qassim, Saudi Arabia. ; Ibrahim, DM (corresponding author)，Tanta Univ, Fac Engn, Dept Comp & Control Engn, Tanta, Egypt.
来源期刊	PEERJ COMPUTER SCIENCE
EISSN	2376-5992
出版年	2022
卷号	8
英文摘要	Prediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings' eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R-2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R-2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption.
英文关键词	Buildings characteristics Cooling load Heating load Energy consumption Statistical analysis
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E
WOS记录号	WOS:000749496100001
WOS关键词	OFFICE BUILDINGS ; CONSUMPTION ; PREDICTION ; DESIGN ; FORM
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems ; Computer Science, Theory & Methods
WOS研究方向	Computer Science
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376510
作者单位	[Ibrahim, Dina M.; Al-Shargabi, Amal A.; Alghieth, Manal] Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah, Qassim, Saudi Arabia; [Ibrahim, Dina M.] Tanta Univ, Fac Engn, Dept Comp & Control Engn, Tanta, Egypt; [Almhafdy, Abdulbasit] Qassim Univ, Coll Architecture & Planning, Dept Architecture, Buraydah, Qassim, Saudi Arabia; [Elragi, Ahmed] Qassim Univ, Coll Engn, Dept Civil Engn, Buraydah, Qassim, Saudi Arabia; [Chiclana, Francisco] De Montfort Univ Leicester, Fac Technol, Inst Artificial Intelligence IAI, Leicester, Leics, England
推荐引用方式 GB/T 7714	Ibrahim, Dina M.,Almhafdy, Abdulbasit,Al-Shargabi, Amal A.,et al. The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings[J],2022,8.
APA	Ibrahim, Dina M.,Almhafdy, Abdulbasit,Al-Shargabi, Amal A.,Alghieth, Manal,Elragi, Ahmed,&Chiclana, Francisco.(2022).The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings.PEERJ COMPUTER SCIENCE,8.
MLA	Ibrahim, Dina M.,et al."The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings".PEERJ COMPUTER SCIENCE 8(2022).