Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, Manal Alghieth and Francisco Chiclana
Additional contact information
Amal A. Al-Shargabi: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Abdulbasit Almhafdy: Department of Architecture, College of Architecture and Planning, Qassim University, Qassim 52571, Saudi Arabia
Dina M. Ibrahim: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Manal Alghieth: Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
Francisco Chiclana: Faculty of Computing, Institute of Artificial Intelligence (IAI), Engineering and Media, De Montfort University, Leicester LE1 9BH, UK

Sustainability, 2021, vol. 13, issue 22, 1-20

Abstract: 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 (R 2 ). 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 R 2 both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R 2 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.

Keywords: building characteristics; deep neural networks; hyper-parameter tuning; prediction models; energy consumption; heating and cooling loads (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/22/12442/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/22/12442/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:22:p:12442-:d:676562

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().