干旱区生态环境知识资源中心(Arid): Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Arid

DOI	10.3390/su132212442
	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
	Al-Shargabi, Amal A.; Almhafdy, Abdulbasit; Ibrahim, Dina M.; Alghieth, Manal; Chiclana, Francisco
通讯作者	Almhafdy, A (corresponding author), Qassim Univ, Coll Architecture & Planning, Dept Architecture, Qasim 52571, Saudi Arabia.
来源期刊	SUSTAINABILITY
EISSN	2071-1050
出版年	2021
卷号	13 期号:22
英文摘要	The dramatic growth in the number of buildings worldwide has led to an increase interest in predicting energy consumption, especially for the case of residential buildings. As the heating and cooling system highly affect the operation cost of buildings; it is worth investigating the development of models to predict the heating and cooling loads of buildings. In contrast to the majority of the existing related studies, which are based on historical energy consumption data, this study considers building characteristics, such as area and floor height, to develop prediction models of heating and cooling loads. In particular, this study proposes deep neural networks models based on several hyper-parameters: the number of hidden layers, the number of neurons in each layer, and the learning algorithm. The tuned models are constructed using a dataset generated with the Integrated Environmental Solutions Virtual Environment (IESVE) simulation software for the city of Buraydah city, the capital of the Qassim region in Saudi Arabia. The Qassim region was selected because of its harsh arid climate of extremely cold winters and hot summers, which means that lot of energy is used up for cooling and heating of residential buildings. Through model tuning, optimal parameters of deep learning models are determined using the following performance measures: Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, and coefficient of determination (R2). The results obtained with the five-layer deep neural network model, with 20 neurons in each layer and the Levenberg-Marquardt algorithm, outperformed the results of the other models with a lower number of layers. This model achieved MSE of 0.0075, RMSE 0.087, R and R2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R2 both as high as 0.99 in predicting the cooling load. As the developed prediction models were based on buildings characteristics, the outcomes of the research may be relevant to architects at the pre-design stage of heating and cooling energy-efficient buildings.
英文关键词	building characteristics deep neural networks hyper-parameter tuning prediction models energy consumption heating and cooling loads
类型	Article
语种	英语
开放获取类型	Green Published, gold
收录类别	SCI-E ; SSCI
WOS记录号	WOS:000725832200001
WOS关键词	OPTIMUM INSULATION THICKNESS ; TIME ; PERFORMANCE ; WALLS
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/373737
作者单位	[Al-Shargabi, Amal A.; Ibrahim, Dina M.; Alghieth, Manal] Qassim Univ, Coll Comp, Dept Informat Technol, Buraydah 51452, Saudi Arabia; [Almhafdy, Abdulbasit] Qassim Univ, Coll Architecture & Planning, Dept Architecture, Qasim 52571, Saudi Arabia; [Ibrahim, Dina M.] Tanta Univ, Fac Engn, Dept Comp & Control Engn, Tanta 31733, Egypt; [Chiclana, Francisco] Montfort Univ, Fac Comp, Inst Artificial Intelligence IAI Engn & Media, Leicester LE1 9BH, Leics, England; [Chiclana, Francisco] Univ Granada, Andalusian Res Inst Data Sci & Computat Intellige, Granada 18100, Spain
推荐引用方式 GB/T 7714	Al-Shargabi, Amal A.,Almhafdy, Abdulbasit,Ibrahim, Dina M.,et al. Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics[J],2021,13(22).
APA	Al-Shargabi, Amal A.,Almhafdy, Abdulbasit,Ibrahim, Dina M.,Alghieth, Manal,&Chiclana, Francisco.(2021).Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics.SUSTAINABILITY,13(22).
MLA	Al-Shargabi, Amal A.,et al."Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics".SUSTAINABILITY 13.22(2021).