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DOI	10.1016/j.jobe.2024.109247
	Optimizing building energy performance predictions: A comparative study of artificial intelligence models
	Alawi, Omer A.; Kamar, Haslinda Mohamed; Yaseen, Zaher Mundher
发表日期	2024
EISSN	2352-7102
起始页码	88
卷号	88
英文摘要	The December 2022 Commercial Buildings Energy Consumption Survey conducted by the Energy Information Administration (EIA) found that space heating constitutes 32% of total building enduse energy, with cooling accounting for 9%. It has a significant impact on climate change. In this research, intelligent models were developed to predict the annual heating and cooling loads (HL and CL) of residential buildings. Eight inputs, including relative compactness, roof area, overall height, surface area, glazing area, wall area, glazing area distribution, and orientation, were used for the modeling development. The artificial intelligence (AI) models were Support Vector Regression (SVR), K -Nearest Neighbors (KNN), Random Forest (RF), Multi -layer Perceptron (MLP), Gradient Boosting (GBoost), and Extreme Gradient Boosting (XGBoost). Three scenarios of input combinations were tested: Scenario -1 (S1) with eight inputs and Scenario -2 (S2) with five inputs. Similarly, Scenario -3 (S3) with five inputs. Results indicated that, the RF was the superior algorithm in HL for S1, achieving Kling -Gupta Efficiency (KGE = 0.998) and Root Mean Square Error (RMSE = 0.501 kW h/m 2 ). XGBoost performed outstandingly in CL with KGE = 0.994 and RMSE = 0.922 kW h/m 2 . In S2, KNN showed excellent HL with KGE = 0.945 and RMSE = 3.094 kW h/m 2 , and RF outperformed in CL with KGE = 0.941 and RMSE = 2.727 kW h/m 2 . In S3, XGBoost exhibited the highest efficiency for HL with KGE = 0.997 and RMSE = 0.492 kW h/m 2 , while RF performed best for CL with KGE = 0.976 and RMSE = 1.686 kW h/m 2 . In conclusion, S2 proved to be a logical choice, matching the efficiency of S1 with reduced error. Overall, HL predictions generally displayed superior performance compared to CL predictions.
英文关键词	Heating and cooling load; Energy -efficient building; Residential building design; Artificial intelligence; Climate change impact
语种	英语
WOS研究方向	Construction & Building Technology ; Engineering
WOS类目	Construction & Building Technology ; Engineering, Civil
WOS记录号	WOS:001225634900001
来源期刊	JOURNAL OF BUILDING ENGINEERING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290482
作者单位	Universiti Teknologi Malaysia; Al-Ayen University; King Fahd University of Petroleum & Minerals; Universiti Teknologi Malaysia
推荐引用方式 GB/T 7714	Alawi, Omer A.,Kamar, Haslinda Mohamed,Yaseen, Zaher Mundher. Optimizing building energy performance predictions: A comparative study of artificial intelligence models[J],2024,88.
APA	Alawi, Omer A.,Kamar, Haslinda Mohamed,&Yaseen, Zaher Mundher.(2024).Optimizing building energy performance predictions: A comparative study of artificial intelligence models.JOURNAL OF BUILDING ENGINEERING,88.
MLA	Alawi, Omer A.,et al."Optimizing building energy performance predictions: A comparative study of artificial intelligence models".JOURNAL OF BUILDING ENGINEERING 88(2024).