Persian
Language
Geographical Research is Published in Persian Fulltext.
Volume 39, Issue 3 (2024)
GeoRes 2024, 39(3): 299-308 |
Back to browse issues page
Article Type:
Subject:
Research code: ART-1634
History
Received: 2024/07/27 | Accepted: 2024/08/28 | Published: 2024/09/28
Received: 2024/07/27 | Accepted: 2024/08/28 | Published: 2024/09/28
How to cite this article
Heydaripour O, Taher Tolou Del M. Energy Performance Modeling of Residential Isfahan Apartments Based on Geometric Characteristics. GeoRes 2024; 39 (3) :299-308
URL: http://georesearch.ir/article-1-1634-en.html
URL: http://georesearch.ir/article-1-1634-en.html
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rights and permissions
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Authors
O. Heydaripour1, M.S. Taher Tolou Del *2
1- Department of Architecture, Faculty of Architectural Engineering and Urban Design, Shahid Rajaee Teacher Training University, Tehran, Iran
2- Department of Architecture, Faculty of Architectural Engineering and Urban Design, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
2- Department of Architecture, Faculty of Architectural Engineering and Urban Design, Shahid Rajaee Teacher Training University, Lavizan, Tehran, Iran
Abstract (312 Views)
Aim: The exponential increase in energy consumption in the residential sector and the unregulated design and construction of residential apartments in Isfahan have led to a critical energy consumption situation in the city. Therefore, the aim of this research was to model the relationship between the geometric characteristics of typical residential apartments and their energy performance for use in urban planning.
Methodology: This was an applied study carried out in 2024 using quantitative research method including modeling, building energy simulation, regression modeling, and optimization. Geometric factors affecting building energy consumption were identified, coded for automated modeling, and entered into the cyclic process of performance-based computational design. The tools used in this research include Rhino software, the Grasshopper environment, and the Ladybug Tools, Colibri, and Wallacei plugins.
Findings: The energy performance models could predict actual residential apartment energy consumption with over 88% accuracy and a correlation coefficient greater than 0.7. Based on the findings, increasing the land plot area led to a decrease in energy consumption per square meter. Additionally, a nearly square building layout with limited north-south elongation resulted in optimal energy performance. Reducing the height of internal spaces and maintaining a zero-degree orientation angle also contributed to optimal energy performance.
Conclusion: The geometric characteristics of apartment housing significantly impact building energy consumption. Increasing the built-up area, having a length-to-width ratio close to one, a zero-degree orientation angle, and reducing the height of internal spaces in urban planning and municipal construction regulations reduces energy consumption in Isfahan.
Methodology: This was an applied study carried out in 2024 using quantitative research method including modeling, building energy simulation, regression modeling, and optimization. Geometric factors affecting building energy consumption were identified, coded for automated modeling, and entered into the cyclic process of performance-based computational design. The tools used in this research include Rhino software, the Grasshopper environment, and the Ladybug Tools, Colibri, and Wallacei plugins.
Findings: The energy performance models could predict actual residential apartment energy consumption with over 88% accuracy and a correlation coefficient greater than 0.7. Based on the findings, increasing the land plot area led to a decrease in energy consumption per square meter. Additionally, a nearly square building layout with limited north-south elongation resulted in optimal energy performance. Reducing the height of internal spaces and maintaining a zero-degree orientation angle also contributed to optimal energy performance.
Conclusion: The geometric characteristics of apartment housing significantly impact building energy consumption. Increasing the built-up area, having a length-to-width ratio close to one, a zero-degree orientation angle, and reducing the height of internal spaces in urban planning and municipal construction regulations reduces energy consumption in Isfahan.
References
1. Al-Shargabi AA, Almhafdy A, Ibrahim DM, Alghieth M, Chiclana F (2022). Buildings' energy consumption prediction models based on buildings' characteristics: Research trends, taxonomy, and performance measures. Journal of Building Engineering. 54:104577. [Link] [DOI:10.1016/j.jobe.2022.104577]
2. Ali A, Jayaraman R, Azar E, Maalouf M (2024). A comparative analysis of machine learning and statistical methods for evaluating building performance: A systematic review and future benchmarking framework. Building and Environment. 252:111268. [Link] [DOI:10.1016/j.buildenv.2024.111268]
3. Asad Poor J, Goh Y W, Thorpe D (2021). A human-centric participatory approach to energy-efficient housing based on occupants' collaborative image. Open House International. 46(4):615-635. [Link] [DOI:10.1108/OHI-11-2020-0163]
4. Chen Y, Guo M, Chen Z, Chen Z, Ji Y (2022). Physical energy and data-driven models in building energy prediction: A review. Energy Reports. 8:2656-2671. [Link] [DOI:10.1016/j.egyr.2022.01.162]
5. Chong A, Augenbroe G, Yan D (2021). Occupancy data at different spatial resolutions: Building energy performance and model calibration. Applied Energy. 286:116492. [Link] [DOI:10.1016/j.apenergy.2021.116492]
6. Coakley D, Raftery P, Keane M (2014). A review of methods to match building energy simulation models to measured data. Renewable and Sustainable Energy Reviews. 37:123-141. [Link] [DOI:10.1016/j.rser.2014.05.007]
7. Dino I. (2016). An evolutionary approach for 3D architectural space layout design exploration. Automation in construction. 69:131-150. [Link] [DOI:10.1016/j.autcon.2016.05.020]
8. Dino I G, Üçoluk G (2017). Multiobjective design optimization of building space layout, energy, and daylighting performance. Journal of Computing in Civil Engineering. 31(5):04017025. [Link] [DOI:10.1061/(ASCE)CP.1943-5487.0000669]
9. Ekici B, Cubukcuoglu C, Turrin M, Sariyildiz IS (2019). Performative computational architecture using swarm and evolutionary optimisation: A review. Building and Environment. 147:356-371. [Link] [DOI:10.1016/j.buildenv.2018.10.023]
10. Elbeltagi E, Wefki H (2021). Predicting energy consumption for residential buildings using ANN through parametric modeling. Energy Reports. 7:2534-2545. [Link] [DOI:10.1016/j.egyr.2021.04.053]
11. Feng Z, Zhang M, Wei N, Zhao J, Zhang T, He X (2022). An office building energy consumption forecasting model with dynamically combined residual error correction based on the optimal model. Energy Reports. 8:12442-12455. [Link] [DOI:10.1016/j.egyr.2022.09.022]
12. Gao H, Koch C, Wu Y (2019). Building information modelling based building energy modelling: A review. Applied energy. 238:320-343. [Link] [DOI:10.1016/j.apenergy.2019.01.032]
13. González-Torres M, Pérez-Lombard L, Coronel J F, Maestre I R, Yan D. (2022). A review on buildings energy information: Trends, end-uses, fuels and drivers. Energy Reports. 8:626-637. [Link] [DOI:10.1016/j.egyr.2021.11.280]
14. Jin X, Zhang C, Xiao F, Li A, Miller C (2023). A review and reflection on open datasets of city-level building energy use and their applications. Energy and Buildings. 285:112911. [Link] [DOI:10.1016/j.enbuild.2023.112911]
15. Kabir S, Islam RU, Hossain MS, Andersson K (2020). An integrated approach of belief rule base and deep learning to predict air pollution. Sensors. 20(7):1956. [Link] [DOI:10.3390/s20071956]
16. Liu H, Liang J, Liu Y, Wu H (2023). A review of data-driven building energy prediction. Buildings. 13(2):532. [Link] [DOI:10.3390/buildings13020532]
17. Liu K, Xu X, Zhang R, Kong L, Wang W, Deng W (2023). Impact of urban form on building energy consumption and solar energy potential: A case study of residential blocks in Jianhu, China. Energy and Buildings: 280:112727. [Link] [DOI:10.1016/j.enbuild.2022.112727]
18. Liu XH, Zhang DG, Yan HR, Cui YY, Chen L (2019). A new algorithm of the best path selection based on machine learning. IEEE Access. 7:126913-126928. [Link] [DOI:10.1109/ACCESS.2019.2939423]
19. Mousavi S, Villarreal-Marroquín MG, Hajiaghaei-Keshteli M, Smith NR (2023). Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review. Building and Environment. 242:110578. [Link] [DOI:10.1016/j.buildenv.2023.110578]
20. Musau F, Steemers K (2008). Space planning and energy efficiency in office buildings: the role of spatial and temporal diversity. Architectural Science Review. 51(2):133-145. [Link] [DOI:10.3763/asre.2008.5117]
21. Olu-Ajayi R, Alaka H, Sulaimon I, Balogun H, Wusu G, Yusuf W, et al (2023). Building energy performance prediction: A reliability analysis and evaluation of feature selection methods. Expert Systems with Applications. 225:120109. [Link] [DOI:10.1016/j.eswa.2023.120109]
22. Olu-Ajayi R, Alaka H, Sulaimon I, Sunmola F, Ajayi S (2022). Machine learning for energy performance prediction at the design stage of buildings. Energy for Sustainable Development. 66:12-25. [Link] [DOI:10.1016/j.esd.2021.11.002]
23. Pan Y, Zhu M, Lv Y, Yang Y, Liang Y, Yin R, et al (2023). Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies. Advances in Applied Energy. 10:100135. [Link] [DOI:10.1016/j.adapen.2023.100135]
24. Poirazis H (2008). Single and double skin glazed office buildings. Division of Energy and Building Design [Report]. Lund: Lund University. [Link]
25. Sariyildiz S (2012). Performative computational design. Proceedings of the ICONARCH-International Congress of Architecture and Technology; 2012 Nov 15-17. Konya: Selcuk University. [Link]
26. Souza C, Alsaadani S (2012). Thermal zoning in speculative office buildings: discussing the connections between space layout and inside temperature control. Proceedings of the 1st Building Simulation and Optimization Conference; 2012; Loughborough University, England. Loughborough: IBPSA Publication. [Link]
27. Wei S, Jones R, De Wilde P (2014). Driving factors for occupant-controlled space heating in residential buildings. Energy and Buildings. 70:36-44. [Link] [DOI:10.1016/j.enbuild.2013.11.001]
28. Wei Y, Zhang X, Shi Y, Xia L, Pan S, Wu J, et al (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews. 82(1):1027-1047. [Link] [DOI:10.1016/j.rser.2017.09.108]
29. Yi H (2016). User-driven automation for optimal thermal-zone layout during space programming phases. Architectural Science Review. 59(4):279-306. [Link] [DOI:10.1080/00038628.2015.1021747]
30. Zhang Y, Bai X, Mills FP, Pezzey JC (2018). Rethinking the role of occupant behavior in building energy performance: A review. Energy and Buildings. 172:279-294. [Link] [DOI:10.1016/j.enbuild.2018.05.017]
31. Zhuang D, Zhang X, Lu Y, Wang C, Jin X, Zhou X, Shi X (2021). A performance data integrated BIM framework for building life-cycle energy efficiency and environmental optimization design. Automation in Construction. 127:103712. [Link] [DOI:10.1016/j.autcon.2021.103712]