Indoor Thermal Assessment of Medium-Cost House in Arid Climate

An environmental responsive design of building internal spaces is an important criterion and should be taken into account in the pre-design stage of the residential buildings. This study aims to analyze the existing spatial spaces of a residential building in terms of thermal performance in a hot arid climate area. Temperature data loggers were utilized in the duplex house. Results indicate that wall exposure, window size, floor level and orientation are the key to design a friendly environment of internal spaces. The neighbourhood fabric has an important role in terms of shadows and time of direct exposure to radiation. Keywords: Indoor house, Arid climate, Air temperature, spatial design eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i18.307

The Effect of Window-to-Wall Ratio (WWR) and Window Orientation (WO) on the Thermal Performance: A preliminary overview

Sustainable aspects of buildings became one of the most crucial aspects of the built environment. The thermal performance can be improved through sustainable design guidelines and, thus, reduce energy consumption. This review covered studies that addressed Window Wall Ratio (WWR) and Window Orientation (WO) and their effect on thermal performance. WWR as a design variable that deals with window design, while the WO as an environmental variable that deals with orientation. The results will help to highlight open issues and research directions in the context of WWR, WO and integrations with other factors in buildings. Keywords: WWR, window design factors, Energy, WO eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI

Optimization of courtyards design factors in the hot humid climate: energy and thermal assessment / Abdul Basit Ali Ahmed Almhafdy

Courtyards are regarded as a microclimate modifier, and their application has become popular in various forms of public buildings. This thesis reviews design factors of courtyards in hospitals in Malaysia, and assesses the resulting thermal performance of the courtyard space and energy performance of the attached built volume. The study took a sequential approach whereby knowledge gained from each phase of research,served to inform the direction for the next phase of the study. It began with the initialinquiry on what are the courtyard characteristics applied in hospitals in Malaysia. Data were gathered through field survey, followed by a typology analysis involving 34 courtyards in 19 government hospitals. The survey revealed extensive use of OEnclosure Courtyard (OEC) and U-Enclosure Courtyard (UEC), and that although the spaces inside the surveyed courtyards appeared as appealing, activities inside these courtyards were rather limited. This led to the next research inquiry on the thermal condition inside the courtyard and the adjacent rooms / built volumes. A field measurement was conducted on a case study hospital, where the thermal condition in the OEC and UEC were collected, analysed and compared

Impacts of Courtyard Geometrical Configurations on Energy Performance of Buildings

The courtyard is an architectural design element often regarded as microclimate modifiers.  It has the potential of improving comfort conditions within the outdoor courtyard space and the enclosing indoor spaces. Harnessing the optimum benefits of courtyards depends on several conditions namely the orientation and configurations of the courtyards, as well as the treatment of the external surfaces of the enclosing building envelopes. As three variables of orientation, number of floors and wall envelope have not been investigated in a single study, therefore, this parametric study was performed to investigate the microclimatic influence of varying courtyard geometric configurations and its enclosing facades in hot and humid climate using IES<VE> simulation tools.  The study observed the environmental impact regarding thermal performance and energy consumption of the enclosing indoor spaces.  The results suggest optimum conditions to harness the potential of courtyards to lower energy consumption of buildings in the tropics.Keywords: courtyard; thermal performance; energy consumption; simulationeISSN: 2398-4287 © 2019. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia.DOI: https://doi.org/10.21834/e-bpj.v4i10.1637

An overview of Wall Envelope Thermal Performance in Arid Climate Buildings

Energy efficiency is an important issue that has been considered by many construction sectors. Recently, the research attention is focused on the thermal performance of the wall envelope, in particular, for its high energy consumption. This paper conducted a literature review highlighting the recent studies and research approach and methodology on the building wall envelope. Results indicate that climate, insulation and orientation are the key factors that should be considered on wall envelope design and installation. Keywords: Wall Envelope, Orientation, Thermal Performance, Insulation Thickness eISSN: 2398-4287© 2020. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v5i15.250

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 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.Qassim University, represented by the Deanship of Scientific Research, (coc-2019-2-2-I-5422

The use of statistical and machine learning tools to accurately quantify the energy performance of residential buildings

open access articlePrediction of building energy consumption is key to achieving energy efficiency and sustainability. Nowadays, the analysis or prediction of building energy consumption using building energy simulation tools facilitates the design and operation of energy-efficient buildings. The collection and generation of building data are essential components of machine learning models; however, there is still a lack of such data covering certain weather conditions. Such as those related to arid climate areas. This paper fills this identified gap with the creation of a new dataset for energy consumption of 3,840 records of typical residential buildings of the Saudi Arabia region of Qassim, and investigates the impact of residential buildings’ eight input variables (Building Size, Floor Height, Glazing Area, Wall Area, window to wall ratio (WWR), Win Glazing U-value, Roof U-value, and External Wall U-value) on the heating load (HL) and cooling load (CL) output variables. A number of classical and non-parametric statistical tools are used to uncover the most strongly associated input variables with each one of the output variables. Then, the machine learning Multiple linear regression (MLR) and Multilayer perceptron (MLP) methods are used to estimate HL and CL, and their results compared using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and coefficient of determination (R2) performance measures. The use of the IES simulation software on the new dataset concludes that MLP accurately estimates both HL and CL with low MAE, RMSE, and R2, which evidences the feasibility and accuracy of applying machine learning methods to estimate building energy consumption

Numerical evaluation of thermal comfort in traditional courtyards to develop new microclimate design in a hot and dry climate

The growing interest in thermal comfort of outdoor environments yields in different analysis on courtyards as a common space between urban and architectural scales. However, there is a limited knowledge regarding the microclimatic behavior of such spaces. Using ENVI-met simulations, this paper aims to numerically discuss the thermal performance of different configurations of traditionally designed courtyards in Shiraz, Iran, which experiences hot summers and cold winters. The geometrical effects such as orientation and H/W (height to width ratio) of courtyards are considered as potential parameters to improve the microclimatic conditions. In this paper, PMV and UTCI are used as thermal comfort indices. The obtained results indicate mean radiant temperature and wind speed as the most effective parameters for thermal comfort of courtyards. In addition, the aforementioned geometrical parameters might not be able to solely create a desirable condition, but they could significantly improve the thermal comfort of courtyards during summer and winter. To achieve a desirable thermal comfort level, the results suggest using configurations of a high H/W rate and southward orientation in order to obtain better shading during summer as well as allowing the solar radiation in while regulating the wind speed in winter

Prediction models for building energy consumption based on buildings’ characteristics: research trends, taxonomy, and performance measures

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Building’s energy consumption prediction is essential to achieve energy efficiency and sustainability. Building’s energy consumption is highly dependent on buildings’ characteristics such as shape, orientation, roof type among others. This paper offers a systematic literature review of studies that proposed building’s characteristics based energy consumption prediction models. In particular, the paper reviews the types of buildings, their characteristics, the type of energy predicted, the dataset, the artificial intelligence (AI) methods used for energy consumption prediction, and the implemented research evaluation performance measures. The review findings show that a small number of studies consider buildings’ characteristics as predictors for energy consumption. Most of the studies use historical energy consumption data, i.e., time-series data, to predict future buildings’ energy consumption. The present study contributes a new taxonomy of the most common AI methods used for energy consumption predictions based on buildings’ characteristics. The study also provides a comparative analysis of the different AI methods in terms of their contributions regarding the prediction of energy consumption. The review identifies research gaps in the existing studies, which is used to highlight future research directions