A Conceptual Model for Sustainable Buildings in Hot and Humid Regions: Energy Consumption Analysis Using Simulation and Artificial Neural Network (Case Study: Hormozgan)
Keywords:
Sustainable buildings, artificial neural network, Energy consumption forecasting, hot and humid areas, Energy simulationAbstract
The study aimed to develop a conceptual model for designing sustainable buildings in hot and humid regions using simulation and artificial neural networks (ANN) to predict and optimize energy consumption. This applied research employed a quantitative–analytical approach. Independent variables included material type, wall and roof thickness, HVAC system type, building orientation, and occupant behavior, while dependent variables covered energy consumption, indoor temperature, and humidity. Data were collected using EnergyPlus simulations and ANN modeling through an MLPRegressor with two hidden layers (ten neurons each), a learning rate of 0.001, and early stopping. Seventy percent of the data were used for training and 30% for testing, with model accuracy assessed via Mean Squared Error (MSE). The ANN model demonstrated high predictive performance in estimating energy consumption in buildings located in hot and humid regions. Comparisons between actual and predicted energy usage revealed some deviations attributed to climate variations and occupant behavior. MSE analysis indicated that wall thickness (94356.66), roof thickness (90021.62), indoor temperature (86698.37), and humidity (92751.18) were the most influential factors. Wooden roofs and low thermal conductivity brick walls contributed significantly to reduced energy use. The proposed conceptual model emphasizes the use of thermally efficient materials, smart control of indoor temperature and humidity, and climate-responsive design strategies. The integration of simulation and ANN modeling provides an effective tool for predicting and optimizing energy consumption in sustainable buildings within hot and humid climates. By identifying critical variables and proposing a sustainable design framework, the study offers practical insights for architects and engineers to reduce energy demand, enhance thermal comfort, and promote environmental sustainability.
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1. Hafez FS, Sa'di B, Gamal S, Taufiq-Yap Y, Alrifaey M, Seyedmahmoudian M, et al. Energy Efficiency in Sustainable Buildings: A Systematic Review with Taxonomy, Challenges, Motivations, Methodological Aspects, Recommendations, and Pathways for Future Research. Energy Strategy Reviews. 2023;45:101013.
2. Khalil M, McGough A, Pourmirza Z, Pazhoohesh M, Walker S. Machine Learning, Deep Learning and Statistical Analysis for forecasting building energy consumption - A systematic review. Eng Appl Artif Intell. 2022;115:105287.
3. Elbeltagi E, Wefki H, Khallaf R. Sustainable Building Optimization Model for Early-Stage Design. Buildings. 2023;13(1):74.
4. Runge J, Zmeureanu R. Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review. Energies. 2019.
5. Mohandes SR, Zhang X, Mahdiyar A. A comprehensive review on the application of artificial neural networks in building energy analysis. Neurocomputing. 2019;340:55-75.
6. Wunsch A, Liesch T, Broda S. Groundwater level forecasting with artificial neural networks: a comparison of long short-term memory (LSTM), convolutional neural networks (CNNs), and non-linear autoregressive networks with exogenous input (NARX). Hydrology and Earth System Sciences. 2021;25:1671-87.
7. Wei Y, Xia L, Pan S, Wu J, Zhang X, Han M, et al. Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks. Applied Energy. 2019.
8. Tamimi S, Farhang. Management of Construction and Creation of an Intelligent Energy Production System for Buildings Utilizing Available Renewable Resources. Pars Project Management. 2025;1(1):124-50.
9. Murathan EK, Manioğlu G. Impact of urban form on energy performance, outdoor thermal comfort, and urban heat Island: A case study in Istanbul. Energy and Buildings. 2025:116109.
10. Karimi S, Liobikienė G, Alitavakoli F. The Effect of Religiosity on Pro-environmental Behavior Based on the Theory of Planned Behavior: A Cross-Sectional Study among Iranian Rural Female Facilitators. Front Psychol. 2022;13:745019.
11. Mansoor M, Wijaksana TI. Predictors of pro-environmental behavior: Moderating role of knowledge sharing and mediatory role of perceived environmental responsibility. J Environ Plan Manag. 2022;66:1-19.
12. Zeng Z, Zhong W, Naz S. Can Environmental Knowledge and Risk Perception Make a Difference? The Role of Environmental Concern and Pro-Environmental Behavior in Fostering Sustainable Consumption Behavior. Sustainability. 2023;15:4791.
13. Díaz MF, Charry A, Sellitti S, Ruzzante M, Enciso K, Burkart S. Psychological Factors Influencing Pro-environmental Behavior in Developing Countries: Evidence From Colombian and Nicaraguan Students. Front Psychol. 2020;11:580730.
14. Saari UA, Damberg S, Frömbling L, Ringle CM. Sustainable consumption behavior of Europeans: The influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecol Econ. 2021;189:107155.
15. Yusliza M, Amirudin A, Rahadi R, Athirah NNS, Ramayah T, Muhammad Z, et al. An Investigation of Pro-Environmental Behaviour and Sustainable Development in Malaysia. Sustainability. 2020;12:7083.
16. Ruiz-Mallén I, Heras M. What Sustainability? Higher Education Institutions' Pathways to Reach the Agenda 2030 Goals. Sustainability. 2020;12:1290.
17. Weiss M, Barth M, Wiek A, von Wehrden H. Drivers and Barriers of Implementing Sustainability Curricula in Higher Education-Assumptions and Evidence. High Educ Stud. 2021;11:42-64.
18. Kohl K, Hopkins C, Barth M, Michelsen G, Dlouhá J, Razak DA, et al. A whole-institution approach towards sustainability: A crucial aspect of higher education's individual and collective engagement with the SDGs and beyond. Int J Sustain High Educ. 2021;23:218-36.
19. Longoria LC, López-Forniés I, Sáenz DC, Sierra-Pérez J. Promoting sustainable consumption in Higher Education Institutions through integrative co-creative processes involving relevant stakeholders. Sustain Prod Consum. 2021;28:445-58.
20. Mayhoub MMG, El Sayad ZMT, Ali AAM, Ibrahim MG. Assessment of Green Building Materials' Attributes to Achieve Sustainable Building Façades Using AHP. Buildings. 2021;11(10):474.
21. Baqer NR, Rashidi-Khazaee P. Residential Building Energy Usage Prediction Using Bayesian-Based Optimized XGBoost Algorithm. IEEE Access. 2025.
22. Sharston R, Singh M. Urban morphology, urban heat island (UHI) and building energy consumption: A critical review of methods and relationships among influential parameters. Building Services Engineering Research & Technology. 2025:01436244251339727.
23. Gholami H, Kamelnia H, Mahdavinejad MJ, Sangin H. Optimizing Building Configuration and Orientation for Social Housing Projects in Iran. Iranica Journal of Energy & Environment. 2025;16(2):289-308.
24. Yafi E, Tehseen S, Haider S. Impact of Green Training on Environmental Performance through Mediating Role of Competencies and Motivation. Sustainability. 2021;13:5624.
25. Wang J, Yang M, Maresova P. Sustainable Development at Higher Education in China: A Comparative Study of Students' Perception in Public and Private Universities. Sustainability. 2020;12:2158.
26. Nahar N, Hossain Z, Mahiuddin S. Assessment of the environmental perceptions, attitudes, and awareness of city dwellers regarding sustainable urban environmental management: a case study of Dhaka, Bangladesh. Environ Dev Sustain. 2022.
27. Quoquab F, Mohammad J, Sukari NN. A multiple-item scale for measuring "sustainable consumption behaviour" construct: Development and psychometric evaluation. Asia Pac J Mark Logist. 2019;31:791-816.
28. Jafri A, Villenuve E, Agelin-Chaab M, Hangan H. Experimental investigation of building mock-ups and air source heat pumps in cold climates. Energy and Built Environment. 2025.
29. Escamilla-García A, Soto-Zarazúa GM, Toledano-Ayala M, Rivas-Araiza E, Gastélum-Barrios A. Applications of Artificial Neural Networks in Greenhouse Technology and Overview for Smart Agriculture Development. Applied Sciences. 2020.
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Copyright (c) 2025 Seyedshahab Sadri (Author); Mohammad Behzadpour; Cyrus Bavar, Hossein Aali (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
