Cooling load prediction model during the design of mosques in Madinah

The building sector accounts for almost 40% of the total global energy consumption. Saudi Arabia, along with other developed countries have expressed their concern on the increasing energy demand and established several related policies focusing on the building sector. Mosques are one category of bu...

Full description

Saved in:

Bibliographic Details
Main Author:	Alharbi, Emad Ameen
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/101472/1/EmadAmeenPSKA2022.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-utm-ep.101472
record_format	uketd_dc
spelling	my-utm-ep.1014722023-06-21T09:51:10Z Cooling load prediction model during the design of mosques in Madinah 2022 Alharbi, Emad Ameen TA Engineering (General). Civil engineering (General) The building sector accounts for almost 40% of the total global energy consumption. Saudi Arabia, along with other developed countries have expressed their concern on the increasing energy demand and established several related policies focusing on the building sector. Mosques are one category of buildings that consume huge amounts of energy above other public sector buildings such as hospitals. An extensive review of literature has revealed that there is an increased demand to build new mosques. The majority of previous research works focused on operational and maintenance stages. In term of energy reduction, not much can be done to existing mosques as the solutions are both costly and time consuming. The importance of making right design at the design stage has been stressed which may save up to 70% of total energy consumption. The literature also revealed that there is a gap and absence of design stage integration for mosque projects, due to the complexity of the stage, the lack of information, and limited support tools. This research aims to develop a prediction model known as the Mosque Cooling Load Prediction Model (MCLPM) to assist designers and local authorities in reducing the energy consumption of mosques during the design stage. The process began by identifying significant structural and architectural design parameters that influence the energy consumption of mosques, using a three rounds of Delphi approach with 33 local experts. Thirteen significant parameters were identified, of which mosque orientation was found to be the most significant. Integration between Rhinoceros/Grasshopper parametric model, EnergyPlus™ simulation of selected medium-sized mosques, and optimization through Genetic Algorithm (GA) and Galapagos were made to generate the dataset required for developing mosque cooling load prediction model based on the Artificial Neural Network (ANN) approach. Two thousand five hundred simulations were performed to achieve the optimum (approximately 58%) of total energy reduction, and 23 non-repetitive design alternatives with the least demand for cooling load were generated. The Mean Square Error (MSE) and correlation coefficient (R) were obtained for the developed ANN prediction model. Based on the findings, the least MSE and R values were at 6.27 * 1 0 _ 9 and 0.99888, respectively. Validation of the results revealed that the back-propagation strategy and Levenberg-Marquardt algorithm have the highest accuracies in predicting the exact total cooling load, in comparison to the actual values, and the absolute difference is less than 1%. The comparison with other methods and algorithms showed that the proposed prediction model has the highest accuracy, effectiveness, and least time required, to complete a given task. Hence, the developed prediction model act as a powerful tool to support the decision-making process that helps mosque designers provide a range of lowest cooling load design alternatives, thus facilitating the design process, and easy-quick estimation of total cooling load. 2022 Thesis http://eprints.utm.my/id/eprint/101472/ http://eprints.utm.my/id/eprint/101472/1/EmadAmeenPSKA2022.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:150564 phd doctoral Universiti Teknologi Malaysia Faculty of Engineering - School of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Alharbi, Emad Ameen Cooling load prediction model during the design of mosques in Madinah
description	The building sector accounts for almost 40% of the total global energy consumption. Saudi Arabia, along with other developed countries have expressed their concern on the increasing energy demand and established several related policies focusing on the building sector. Mosques are one category of buildings that consume huge amounts of energy above other public sector buildings such as hospitals. An extensive review of literature has revealed that there is an increased demand to build new mosques. The majority of previous research works focused on operational and maintenance stages. In term of energy reduction, not much can be done to existing mosques as the solutions are both costly and time consuming. The importance of making right design at the design stage has been stressed which may save up to 70% of total energy consumption. The literature also revealed that there is a gap and absence of design stage integration for mosque projects, due to the complexity of the stage, the lack of information, and limited support tools. This research aims to develop a prediction model known as the Mosque Cooling Load Prediction Model (MCLPM) to assist designers and local authorities in reducing the energy consumption of mosques during the design stage. The process began by identifying significant structural and architectural design parameters that influence the energy consumption of mosques, using a three rounds of Delphi approach with 33 local experts. Thirteen significant parameters were identified, of which mosque orientation was found to be the most significant. Integration between Rhinoceros/Grasshopper parametric model, EnergyPlus™ simulation of selected medium-sized mosques, and optimization through Genetic Algorithm (GA) and Galapagos were made to generate the dataset required for developing mosque cooling load prediction model based on the Artificial Neural Network (ANN) approach. Two thousand five hundred simulations were performed to achieve the optimum (approximately 58%) of total energy reduction, and 23 non-repetitive design alternatives with the least demand for cooling load were generated. The Mean Square Error (MSE) and correlation coefficient (R) were obtained for the developed ANN prediction model. Based on the findings, the least MSE and R values were at 6.27 * 1 0 _ 9 and 0.99888, respectively. Validation of the results revealed that the back-propagation strategy and Levenberg-Marquardt algorithm have the highest accuracies in predicting the exact total cooling load, in comparison to the actual values, and the absolute difference is less than 1%. The comparison with other methods and algorithms showed that the proposed prediction model has the highest accuracy, effectiveness, and least time required, to complete a given task. Hence, the developed prediction model act as a powerful tool to support the decision-making process that helps mosque designers provide a range of lowest cooling load design alternatives, thus facilitating the design process, and easy-quick estimation of total cooling load.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Alharbi, Emad Ameen
author_facet	Alharbi, Emad Ameen
author_sort	Alharbi, Emad Ameen
title	Cooling load prediction model during the design of mosques in Madinah
title_short	Cooling load prediction model during the design of mosques in Madinah
title_full	Cooling load prediction model during the design of mosques in Madinah
title_fullStr	Cooling load prediction model during the design of mosques in Madinah
title_full_unstemmed	Cooling load prediction model during the design of mosques in Madinah
title_sort	cooling load prediction model during the design of mosques in madinah
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Civil Engineering
publishDate	2022
url	http://eprints.utm.my/id/eprint/101472/1/EmadAmeenPSKA2022.pdf
_version_	1776100706276605952

Cooling load prediction model during the design of mosques in Madinah

Similar Items