Tuning deep neural networks for predicting energy consumption in the arid climate based on buildings characteristics

Date

2021-11-11

Advisors

Journal Title

Journal ISSN

ISSN

2071-1050

Volume Title

Publisher

MDPI

Type

Article

Peer reviewed

Yes

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 (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.

Description

The Publisher's final version can be found by following the DOI link.. Open access article

Keywords

building characteristics, deep neural networks, hyper-parameter tuning, prediction models, energy consumption, heating and cooling loads

Citation

Amal A. Al-Shargabi, Abdulbasit A. Almhafdy, Dina M. Ibrahim, Manal Alghieth, Francisco Chiclana (2021) Tuning deep neural networks for predicting energy consumption in the arid climate based on buildings characteristics. Sustainabilty, 13, 12442.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)