@misc{10481/71457,
year = {2021},
url = {http://hdl.handle.net/10481/71457},
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.},
organization = {Qassim University, represented by the Deanship of
Scientific Research, (coc-2019-2-2-I-5422)},
publisher = {MDPI},
keywords = {Building characteristics},
keywords = {Deep neural networks},
keywords = {Hyper-parameter tuning},
keywords = {Predictive models},
keywords = {Energy consumption},
keywords = {Heating and cooling loads},
title = {Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics},
doi = {10.3390/su132212442},
author = {Al-Shargabi, Amal A. and Almhafdy, Abdulbasit and Ibrahim, Dina M. and Alghieth, Manal and Chiclana Parrilla, Francisco},
}