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

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 m...

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Autores principales:	Amal A. Al-Shargabi, Abdulbasit Almhafdy, Dina M. Ibrahim, Manal Alghieth, Francisco Chiclana
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	building characteristics deep neural networks hyper-parameter tuning prediction models energy consumption heating and cooling loads Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/586fd87ad23a4ca8987dd94f3f479249
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spelling	oai:doaj.org-article:586fd87ad23a4ca8987dd94f3f4792492021-11-25T19:01:05ZTuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics10.3390/su1322124422071-1050https://doaj.org/article/586fd87ad23a4ca8987dd94f3f4792492021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12442https://doaj.org/toc/2071-1050The 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>). 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> 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.Amal A. Al-ShargabiAbdulbasit AlmhafdyDina M. IbrahimManal AlghiethFrancisco ChiclanaMDPI AGarticlebuilding characteristicsdeep neural networkshyper-parameter tuningprediction modelsenergy consumptionheating and cooling loadsEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12442, p 12442 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	building characteristics deep neural networks hyper-parameter tuning prediction models energy consumption heating and cooling loads Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	building characteristics deep neural networks hyper-parameter tuning prediction models energy consumption heating and cooling loads Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Amal A. Al-Shargabi Abdulbasit Almhafdy Dina M. Ibrahim Manal Alghieth Francisco Chiclana Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
description	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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula>). 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<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> both as high as 0.99 in predicting the heating load and MSE of 0.245, RMSE of 0.495, R and R<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>2</mn></msup></semantics></math></inline-formula> 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.
format	article
author	Amal A. Al-Shargabi Abdulbasit Almhafdy Dina M. Ibrahim Manal Alghieth Francisco Chiclana
author_facet	Amal A. Al-Shargabi Abdulbasit Almhafdy Dina M. Ibrahim Manal Alghieth Francisco Chiclana
author_sort	Amal A. Al-Shargabi
title	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
title_short	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
title_full	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
title_fullStr	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
title_full_unstemmed	Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics
title_sort	tuning deep neural networks for predicting energy consumption in arid climate based on buildings characteristics
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/586fd87ad23a4ca8987dd94f3f479249
work_keys_str_mv	AT amalaalshargabi tuningdeepneuralnetworksforpredictingenergyconsumptioninaridclimatebasedonbuildingscharacteristics AT abdulbasitalmhafdy tuningdeepneuralnetworksforpredictingenergyconsumptioninaridclimatebasedonbuildingscharacteristics AT dinamibrahim tuningdeepneuralnetworksforpredictingenergyconsumptioninaridclimatebasedonbuildingscharacteristics AT manalalghieth tuningdeepneuralnetworksforpredictingenergyconsumptioninaridclimatebasedonbuildingscharacteristics AT franciscochiclana tuningdeepneuralnetworksforpredictingenergyconsumptioninaridclimatebasedonbuildingscharacteristics
_version_	1718410403085549568

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

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