Selecting a Suitable Flat in a High-Rise Apartment by Evaluation of Heat, Light, and Ventilation †
Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India
*
Author to whom correspondence should be addressed.
†
Presented at the International Conference on Recent Advances in Science and Engineering, Dubai, United Arab Emirates, 4–5 October 2023.
Eng. Proc. 2023, 59(1), 40; https://doi.org/10.3390/engproc2023059040
Published: 13 December 2023
(This article belongs to the Proceedings of Eng. Proc., 2023, RAiSE-2023)
Abstract
:In scientific literature, the impacts of heat, light, and ventilation on indoor settings have been extensively studied. It shows how important it is to consider a building’s HLV characteristics in the context of its surroundings. These elements have a direct impact on a building’s comfort level, energy effectiveness, and general sustainability. Many studies have investigated the effects of heat, light, and ventilation individually, rather than in combination with each other. This is because these factors have complex and dynamic interactions with each other, making it challenging to study them comprehensively. However, not many studies in this area have been made considering Indian geographical conditions. It can be challenging for a customer to find an apartment in a high rise building that meets their needs. Thus, using DesignBuilder tools at four different locations in India, a simulation was made and an analysis on the effects of HLV was performed for a symmetrical 10-storey building with adjacent buildings. An in-depth discussion of the air change rate of the building, daylighting performance in relation to different floors, and the difference between the indoor and outdoor temperatures of the building has been performed in this study. The criteria for choosing an apartment in a high rise building in accordance with the client’s requirements have also been derived from these results. Analysis on the effect of heat shows that the higher-density and taller surrounding buildings have a more pronounced effect on reducing the temperature difference. In the analysis of light, the height and distance of the surrounding buildings play a significant role in casting shadows on the main building. Ventilation analysis showed that higher floors have better ventilation compared to the lower floors and an increase in distance of the surrounding building increases the air change rate. The energy consumption analysis highlights that when the main building is surrounded by multiple buildings, energy consumption tends to decrease. The results indicate that as the building distance increases, energy consumption increases. Similar patterns are shown in all of the locations which were simulated, but the energy consumption load depends on the climatic condition of each location. Ahmedabad has the highest energy consumption load followed by Delhi, Guwahati, and Bangalore, irrespective of the distance and height of the surrounding buildings from the main building. Based on these findings, the guidelines were drawn for the selection of a suitable flat based on the requirement of the customer.
1. Introduction
Analyzing a building’s heat, light, and ventilation (HLV) properties within its environment is crucial, impacting comfort, energy efficiency, and sustainability. Traditional methods involve physical testing, but building information modeling (BIM) has emerged as a more efficient approach [1]. BIM enhances design, optimizes energy use, and creates sustainable environments. Comprehensive studies are lacking, often focusing on isolated aspects like thermal comfort or daylighting. Building orientation affects energy usage [2].
A London study used a Revit model and the Green Building Studio, finding that a south-facing orientation at 180 degrees is the most efficient [3]. In the UAE, BIM and building energy modeling assessed orientation, window size, and type, resulting in up to a 1.27% energy bill reduction with optimal adjustments [4,5]. Thermal Imagery-Based BIM models, using thermal cameras, accurately assess heating and cooling. Building shape affects energy use; compactness and window-to-wall ratio impacts consumption [6] and also optimizes the daylighting performance in the building [7]. Natural ventilation affects energy usage [8]; studies which used BIM-based methodologies and CFD simulations showed savings in mechanical ventilation energy [9]. Surrounding buildings influence natural ventilation [10]. Studies from Hong Kong and Germany revealed wind reduction due to nearby structures, impacting residential ventilation [11]. Solar radiation’s effect on Istanbul’s heating and cooling loads varied across floors [12]. Tokyo’s surrounding buildings affected the pressure on low-rise structures [13]. A new energy daylight index balances energy performance and daylighting in envelope design [14]. Window size, orientation, and reflectance impact daylighting and energy demand [15]. Natural ventilation studies emphasize understanding urban impacts [16,17]. Integrating active and passive systems optimizes energy use. Historical analysis aids tall building energy strategies [18,19]. In-depth overviews exist for natural ventilation design in multi-storey buildings, covering strategy selection, integration, and evaluation.
2. Materials and Methods/Methodology
2.1. Characteristics of the Building
The main building analyzed is a 10-storey residential building. On each floor of this building, there are provisions for a staircase, lifts, and lobby are made, and 4 apartments, each consisting of 1 living room (LR), 2 bedrooms (BR), 2 toilets, and a kitchen, as in Figure 1. The WWR for the living rooms and kitchens is 30%, and for the bedrooms the WWR is 18%. The windows are equipped with external shading devices and are placed on the external walls. The external walls are made of 4 layers, the outermost layer is 0.1 m thick which is the brickwork, the second layer of thickness is 0.0795 m, consisting of XPS extruded polystyrene, and the third layer is 0.1 m of thick concrete block. The innermost layer is gypsum plastering with a thickness of 0.013 m. The partition walls have 3 layers with two outer layers being a gypsum plasterboard with a thickness of 0.025 m each, with an air gap of a thickness of 0.1 m between them. Figure 1 shows the floor plan of the case study building and 3D view of the same.
2.2. Characteristics of Urban Environment
Buildings with the same geometry but different heights and distances surround the main building, which is taken into consideration for analysis. As depicted in Figure 1, cases were constructed taking into account changes in the height, direction, and distance of the nearby buildings. In each of the combinations, nine examples are examined. In one instance, a five-storey building is situated 12 m from the main structure in the north. In another instance, a five-storey building is situated in each of the eight directions separately, and in a third instance, the main structure is encircled by five-storey buildings on all eight sides, simultaneously. The cases being studied are spread across four different Indian cities: Ahmedabad (west), Guwahati (east), Bangalore (south), and Delhi (north).
2.3. Simulation Settings
All the zones are set for domestic circulation. All the windows have external shading devices. WWR for the bedrooms is set as 18% and for the living rooms it is set as 30% with the glazing area as 100%. In HVAC, the simulation was carried out for both mechanical ventilation and natural ventilation, including all of the buildings in shading calculation. Heating setpoint temperatures and cooling setpoint temperatures were set according to ASHRAE 90.1 standards [20]. The simulation duration was calculated for the year 2022. For the daylight simulation settings, the working plane height was set to 0.75 m, and included all of the surrounding buildings in the calculation.
3. Results and Discussions
3.1. Effect of Heat
The simulation was conducted to calculate the temperature difference between the inside and outside temperatures of the building. When the main building was surrounded by a 5-floor building at a distance of 12 m in the east direction in Bengaluru, a temperature difference of 5.3 °C was observed with the inside operative temperature being higher than the outside dry bulb temperature. Similarly, when a building was kept in the other 7 directions, i.e., north, south, west, north east, north west, south east, and south west individually, the same temperature difference of 5.3 °C was observed. But, when the building was surrounded by 5-floor buildings at a distance of 12 m in all eight directions at the same time, an average temperature difference of 5 °C was observed, from which we observed that there is no significant effect in the building. When the 5-floor buildings were placed 30 m apart from the main building in all eight directions individually, the temperature difference observed was 5.35 °C with the inside operative temperature being higher than the outside dry bulb temperature. There was a difference of 0.3 °C, which is of no effect on the building when 5-floor buildings surround in all eight directions at the same time. A similar pattern was observed when the location was changed to Delhi, Ahmedabad, and Gauhati when the main building was surrounded by 5-floor buildings at 12 m and 30 m distances from the main building.
A similar pattern was observed, as shown in the Table 1 below, when the main building was surrounded by 10-floor buildings and 15-floor buildings when kept at a distance of 12 m and 30 m.
3.2. Effect of Light
When the data was analyzed for the case when a building of 10 floors (which is the main building) was surrounded by a 10-floor building, the effect due to shading could be observed only up to the fourth floor of the main building. Depending on the plan of the building, the effect of UDI can vary. As shown in the figures below, when the main building was obstructed by a building in the east at a distance of 12 m, the UDI of rooms which are present in the east side of the main building was affected majorly due to the shading, and this effect can be seen up to the eighth floor. Similarly, when buildings were presented individually in any direction, the rooms present in that particular direction in the main building are affected and this effect can be seen only up to the eighth floor. Similarly when a 5-floor building was kept at a distance of 12 m, the effect could be seen only up to the fourth floor, and when a 15-floor building was kept at a distance of 12 m, the whole of the main building was affected. But when the neighboring buildings were placed at a distance of 30 m from the main building in any direction, no significant effect could be seen. Figure 2 below shows the UDI map of main building when a 10-floor building is situated in the east direction at a distance of 12 m; Figure 3 shows the UDI map of main building when a 10-floor building is situated in the east direction at a distance of 30 m.
3.3. Effect of Ventilation
After simulating the cases, the air change rate per hour (ac/h) in the main building located in Bangalore, when surrounded by 5-floor buildings individually in different directions at a distance of 12 m, was an average of 2.24 ac/h. But, when the main building was surrounded in all eight directions at the same time at a distance of 12 m, an ac/h of 2.21 was observed, i.e., a difference of 0.03, which has no significant effect on ventilation when compared. When the buildings were placed at a distance of 30 m in either of the cases there was no effect on the ac/h of the main building. A similar pattern was observed in Delhi, Guwahati, and Ahmedabad.
The air change rate per hour (ac/h) in the main building located in Bangalore, when surrounded by 10-floor buildings individually in different directions at a distance of 12 m, was an average of 2.24 ac/h. But, when the main building was surrounded in all eight directions at the same time at a distance of 12 m, an ac/h of 2.12 is observed, i.e., a difference of 0.12, which has no significant effect on ventilation when compared. Whereas the buildings which were placed at a distance of 30 m, in either of the cases, showed no effect on the ac/h of the main building. A similar pattern was observed in Delhi, Guwahati, and Ahmedabad. The air change rate per hour (ac/h) in the main building located in Ahmedabad, when surrounded by 15-floor buildings individually in different directions at a distance of 12 m, was an average of 2.17 ac/h. But, when the main building was surrounded in all eight directions at the same time at a distance of 12 m, an ac/h of 1.98 was observed, i.e., a difference of 0.19, which has no significant effect on ventilation when compared. A similar pattern was observed in Delhi, Guwahati, and Bengaluru. The following table shows the percentage reduction of the ac/h when the main building is surrounded on all sides as shown in Table 2.
3.4. Effect of Energy Consumption
Energy consumption is the highest in Ahmedabad, followed by Delhi, Guwahati, and Bangalore being the lowest in energy consumption as shown in Table 3. When the main building was surrounded by buildings of 5 floors in all directions at the same time at a distance of 12 m, a decrease of 3% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 12 m from any direction. When the main building was surrounded by buildings of 5 floors in all directions at the same time at a distance of 30 m, a decrease of 1% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 30 m from any direction.
When the main building was surrounded by buildings of 10 floors in all directions at the same time at a distance of 12 m, a decrease of 12% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 12 m from any direction. When the main building was surrounded by buildings of 10 floors in all directions at the same time at a distance of 30 m, a decrease of 4.5% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 30 m from any direction.
Similarly, when the main building was surrounded by buildings of 15 floors in all directions at the same time at a distance of 12 m, a decrease of 17% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 12 m from any direction. When the main building was surrounded by buildings of 15 floors in all directions at the same time at a distance of 30 m, a decrease of 6% in energy consumption was seen in the main building when compared to only one building surrounding it at a distance of 30 m from any direction.
4. Conclusions
Out of the locations and parameters considered, the analysis of the effect of heat shows that higher density and taller surrounding buildings have a more pronounced effect on reducing the temperature difference. In the analysis of light, the height and distance of the surrounding buildings play a significant role in casting shadows on the main building. The taller the surrounding building and the closer it is to the main building, the greater the shading effect. Buildings situated to the north, south, east, or west of the main building can cast shadows on specific areas, such as kitchens or bedrooms, depending on their relative position and distance. The density and arrangement of surrounding buildings have a significant impact on energy consumption. When it comes to ventilation, the higher floors have better ventilation compared to the lower floors, and an increase in distance from the surrounding building increases the ac/h. But, if the height of the surrounding building increases, then the ac/h of the main building decreases.
When it comes to energy consumption, when the main building is surrounded by multiple buildings, energy consumption tends to decrease. This suggests that a higher building density can lead to improved energy efficiency due to factors such as shading and airflow patterns. The distance between the main building and surrounding buildings also influences energy consumption.
These results indicate that as the building distance increases, energy consumption increases. Similar patterns are shown in all the locations which were simulated, but the energy consumption load depends on the climatic condition of each location. Ahmedabad has the highest energy consumption load, followed by Delhi, Guwahati, and Bangalore, irrespective of the distance and height of the surrounding buildings from the main building.
If the client is looking for better daylighting when there is a higher surrounding building density and the distance from the main building is equal to or lesser than 12 m, the client should consider apartments on higher floors for better daylighting which reduces the use of artificial lighting during the day, in turn leading to a lesser contribution to energy consumption. But, when the client is looking for better ventilation and lower temperatures inside the building, they should choose the apartments in the middle floors of the building. This is preferred as the use of mechanical ventilators will be less due to better ventilation leading to lesser energy consumption. When there is a single building in the surroundings, irrespective of the direction and distance it is from the main building, there is no significant effect on heat and ventilation, except for natural lighting when the surrounding buildings’ height is greater or equal to the main building.
Author Contributions
Conceptualization, B.R.K.H. and A.B.; methodology, R.D.H.; software, A.B. and A.R.L.; validation, R.D.H. and A.R.L.; formal analysis, A.B.; investigation, B.R.K.H.; writing—original draft preparation, R.D.H.; writing—review and editing, A.R.L.; visualization, R.D.H.; supervision, B.R.K.H.; project administration, B.R.K.H. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon request.
Acknowledgments
We would like to thank Anil Rana, Director of the Manipal Institute of Technology, Manipal for providing us with the necessary support and infrastructure to carry out this project. We would also like to extend our heartiest gratitude to Purushotham G Sarvade, Professor and Head of the Department, Civil Engineering, for providing us with the opportunity to undertake this project. We want to thank Raghavendra K. Holla, Associate Professor of the Department of Civil Engineering, our guide, for his steadfast support throughout the project. Nandineni Rama Devi, Director of the Manipal School of Architecture and Planning, Manipal for providing us with the necessary support and access to the laboratory to carry our research.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Casini, M. Building information modeling. In Construction 40; Elsevier: Amsterdam, The Netherlands, 2022; pp. 189–219. [Google Scholar]
- Lu, Y.; Wang, S.; Zhao, Y.; Yan, C. Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods. Energy Build. 2015, 89, 61–75. [Google Scholar] [CrossRef]
- Abanda, F.H.; Byers, L. An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (Building Information Modelling). Energy 2016, 97, 517–527. [Google Scholar] [CrossRef]
- Adi, A.R.; Suryabrata, J.A.; Pradipto, E. Optimizing shading devices through the shading effect of surrounding buildings. Dimens. J. Archit. Built Environ. 2020, 46, 79–86. [Google Scholar] [CrossRef]
- Watfa, M.K.; Hawash, A.E.; Jaafar, K. Using Building Information & Energy Modelling for Energy Efficient Designs. J. Inf. Technol. Constr. 2021, 26, 427–440. [Google Scholar] [CrossRef]
- Ham, Y.; Golparvar-Fard, M. Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling. Autom Constr. 2015, 49, 214–224. [Google Scholar] [CrossRef]
- Xue, P.; Mak, C.M.; Huang, Y. Quantification of luminous comfort with dynamic daylight metrics in residential buildings. Energy Build. 2016, 117, 99–108. [Google Scholar] [CrossRef]
- Omrani, S.; Garcia-Hansen, V.; Capra, B.; Drogemuller, R. Natural ventilation in multi-storey buildings: Design process and review of evaluation tools. Build. Environ. 2017, 116, 182–194. [Google Scholar] [CrossRef]
- Gan, V.J.L.; Deng, M.; Tan, Y.; Chen, W.; Cheng, J.C.P. BIM-based framework to analyze the effect of natural ventilation on thermal comfort and energy performance in buildings. Energy Procedia 2019, 158, 3319–3324. [Google Scholar] [CrossRef]
- Gao, C.F.; Lee, W.L. The Influence of Surrounding Buildings on the Natural Ventilation Performance of Residential Dwellings in Hong Kong. Int. J. Vent. 2012, 11, 297–310. [Google Scholar] [CrossRef]
- Daneshfar, M.; Hartmann, T.; Rabe, J. Sensitivity analysis of building energy models due to the shading effect of surrounding buildings to support building renovation. IOP Conf. Ser. Earth Environ. Sci. 2022, 1078, 12126. [Google Scholar] [CrossRef]
- Kocagil, I.E.; Oral, G. 0083-The Impact of Solar Gain and Daylight Availability on Building Energy Loads Related to Urban Geometry. In Proceedings of the SOLARTR 2018, Istanbul, Turkey, 17–29 November 2018; pp. 190–199. [Google Scholar]
- Sun, Y.; Tamura, Y.; Quan, Y.; Matsui, M.; Xu, S. The interference effect of surrounding buildings on wind loads of low-rise buildings. In Proceedings of the BBAA VI International Colloquium on: Bluff Bodies Aerodynamics & Applications, Milan, Italy, 20–24 July 2008. [Google Scholar]
- Xie, J.C.; Xue, P.; Mak, C.M.; Liu, J.P. Balancing energy and daylighting performances for envelope design: A new index and proposition of a case study in Hong Kong. Appl. Energy 2017, 205, 13–22. [Google Scholar] [CrossRef]
- Mangkuto, R.A.; Rohmah, M.; Asri, A.D. Design optimisation for window size, orientation, and wall reflectance with regard to various daylight metrics and lighting energy demand: A case study of buildings in the tropics. Appl. Energy 2016, 164, 211–219. [Google Scholar] [CrossRef]
- Ramponi, R.; Gaetani, I.; Angelotti, A. Influence of the urban environment on the effectiveness of natural night-ventilation of an office building. Energy Build. 2014, 78, 25–34. [Google Scholar] [CrossRef]
- Geros, V.; Santamouris, M.; Karatasou, S.; Tsangrassoulis, A.; Papanikolaou, N. On the cooling potential of night ventilation techniques in the urban environment. Energy Build. 2005, 37, 243–257. [Google Scholar] [CrossRef]
- Sun, B.; Luh, P.B.; Jia, Q.S.; Jiang, Z.; Wang, F.; Song, C. Building Energy Management: Integrated Control of Active and Passive Heating, Cooling, Lighting, Shading, and Ventilation Systems. IEEE Trans. Autom. Sci. Eng. 2013, 10, 588–602. [Google Scholar] [CrossRef]
- Oldfield, P.; Trabucco, D.; Wood, A. Five energy generations of tall buildings: An historical analysis of energy consumption in high-rise buildings. J. Archit. 2009, 14, 591–613. [Google Scholar] [CrossRef]
- ASHRAE 90.1-2022; Energy Standard for Sites and Buildings Except Low-Rise Residential Buildings. ASHRAE: Peachtree Corners, GA, USA, 2022.
Figure 1.
(a) Floor plan of the main building; (b) 3D view of the main building.
Figure 2.
UDI Map of the case study building when 10 floor adjacent building in East Direction at a distance of 12 m. (a) UDI map of the first floor; (b) UDI map of the fifth floor; (c) UDI map of the tenth floor.
Figure 2.
UDI Map of the case study building when 10 floor adjacent building in East Direction at a distance of 12 m. (a) UDI map of the first floor; (b) UDI map of the fifth floor; (c) UDI map of the tenth floor.
Figure 3.
UDI Map of the case study building when 10 floor adjacent building in East Direction at a distance of 30 m. (a) UDI map of the first floor; (b) UDI map of the fifth floor; (c) UDI map of the tenth floor.
Figure 3.
UDI Map of the case study building when 10 floor adjacent building in East Direction at a distance of 30 m. (a) UDI map of the first floor; (b) UDI map of the fifth floor; (c) UDI map of the tenth floor.
Table 1.
Analysis on the effect of heat.
| Surrounding Buildings Height | No. of Surrounding Buildings | Distance from the Main Building (m) | * ΔT = Ti − To (°C) | Remarks |
|---|---|---|---|---|
| 5 Floors | 1 | 12 m | 5.3 | Difference is same in all locations |
| 8 | 12 m | 5 | ||
| 5 Floors | 1 | 30 m | 5.35 | Difference is same in all locations |
| 8 | 30 m | 5.3 | ||
| 10 Floors | 1 | 12 m | 5.2 | Difference is same in all locations |
| 8 | 12 m | 4.2 | ||
| 10 Floors | 1 | 30 m | 5.3 | Difference is same in all locations |
| 8 | 30 m | 4.9 | ||
| 15 Floors | 1 | 12 m | 5.55 | Difference is same in all locations |
| 8 | 12 m | 3.75 | ||
| 15 Floors | 1 | 30 m | 5.6 | Difference is same in all locations |
| 8 | 30 m | 5 |
* ΔT is the temperature difference, Ti is the inside operative temperature, To is the outside temperature.
Table 2.
Analysis on the effect of ventilation.
| Surrounding Buildings Height | No. of Surrounding Buildings | Distance from the Main Building (m) | % Reduction |
|---|---|---|---|
| 5 Floors | 1 | 12 m | |
| 8 | 12 m | * N:1.57, S:1.33, E:1.48, W:1.37 | |
| 5 Floors | 1 | 30 m | |
| 8 | 30 m | N:0.52, S:0.44, E:0.49, W:0.45 | |
| 10 Floors | 1 | 12 m | |
| 8 | 12 m | N:5.58, S:4.93, E:5.44, W:5.06 | |
| 10 Floors | 1 | 30 m | |
| 8 | 30 m | N:1.57, S:1.33, E:1.48, W:1.37 | |
| 15 Floors | 1 | 12 m | |
| 8 | 12 m | N:12.9, S:9.8, E:10.9, W:10.1 | |
| 15 Floors | 1 | 30 m | |
| 8 | 30 m | N:4.7, S:4.04, E:4.43, W:4.1 |
* N—north of India, S—south of India, E—east of India, W—west of India.
Table 3.
Analysis on the effect of energy consumption.
| Surrounding Buildings Height | No. of Surrounding Buildings | Distance from the Main Building (m) | % Reduction When Surrounded on All Sides |
|---|---|---|---|
| 5 Floors | 1 | 12 m | |
| 8 | 12 m | * N:2.74, S:2.80, W:2.63, E:3.00 | |
| 5 Floors | 1 | 30 m | |
| 8 | 30 m | N:0.89, S:1.31, E:0.92, W:0.80 | |
| 10 Floors | 1 | 12 m | |
| 8 | 12 m | N:10.5, S:13.6, E:11.29, W:10.72 | |
| 10 Floors | 1 | 30 m | |
| 8 | 30 m | N:3.75, S:4.67, E:3.92, W:3.20 | |
| 15 Floors | 1 | 12 m | |
| 8 | 12 m | N:15.2, S:17.97, E:15.99, W:16.98 | |
| 15 Floors | 1 | 30 m | |
| 8 | 30 m | N:7.56, S:7.06, E:7.64, W:6.91 |
* N—north of India, S—south of India, E—east of India, W—west of India.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
MDPI and ACS Style
Bansal, A.; Horabyle, R.D.; Holla, B.R.K.; Lotliker, A.R. Selecting a Suitable Flat in a High-Rise Apartment by Evaluation of Heat, Light, and Ventilation. Eng. Proc. 2023, 59, 40. https://doi.org/10.3390/engproc2023059040
AMA Style
Bansal A, Horabyle RD, Holla BRK, Lotliker AR. Selecting a Suitable Flat in a High-Rise Apartment by Evaluation of Heat, Light, and Ventilation. Engineering Proceedings. 2023; 59(1):40. https://doi.org/10.3390/engproc2023059040
Chicago/Turabian StyleBansal, Aniket, Rohan Dinesh Horabyle, B. R. K. Holla, and Arya Rajiv Lotliker. 2023. "Selecting a Suitable Flat in a High-Rise Apartment by Evaluation of Heat, Light, and Ventilation" Engineering Proceedings 59, no. 1: 40. https://doi.org/10.3390/engproc2023059040
