AI-driven design optimization for sustainable buildings: a systematic review
Open accessAbstract
Buildings are major contributors to global carbon emissions, accounting for a substantial portion of energy consumption and environmental impact. This situation presents a critical opportunity for energy conservation through strategic interventions in both building design and operational phases . Artificial Intelligence (AI) has emerged as a transformative approach in this context, enhancing the efficiency and precision of energy management efforts. In the operational phase, AI is extensively utilized as smart controllers for Heating, Ventilation, and Air Conditioning (HVAC) systems and passive energy gains, as well as for fault detection. In the design phase, AI is pivotal as a surrogate model , enabling rapid and accurate evaluation of design options and allowing designers to optimize building performance with minimal computational resources. As the early-stage optimization is more cost-effective than post-construction modifications, design phase optimization has a great potential. Consequently, this paper examines recent advancements in surrogate-assisted design optimization for sustainable buildings, providing a comprehensive overview of the entire optimization process, from data preparation and surrogate model training to final optimization. The review categorizes studies based on experimental approaches and methodologies, identifying trends, gaps, and opportunities in the field. Notably, it highlights how modern AI techniques can incorporate previously unexplored dimensions into surrogate-assisted optimization, broadening the scope and potential of surrogate models. Therefore, this study provides guidance for future research and practical applications of AI-driven strategies in sustainable building practices.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Generative design algorithms can suggest new architectural shapes that meet specific performance needs such as maximization of daylight or reduction of steel.
“Generative design algorithms, utilizing deep learning or genetic algorithms, can suggest new architectural shapes that meet specific performance needs such as maximization of daylight or reduction of steel (Abioye et al., 2021; Manmatharasan et al., 2025).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Manmatharasan et al., 2025
In sustainable building design, AI can examine building form and materials combination to reduce energy usage or carbon use.
“For instance, in sustainable buildingdesign, AI can examine building form and materials combination to reduce energy usage or carbon usea field comprehensively reviewed by Manmatharasan et al. (2025).”
From Artificial intelligence in civil engineering: emerging applications and opportunities (2025) · cites this paper as Manmatharasan et al., 2025
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & The PRISMA Group (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000097 · Cited by 64,042 · see citing works
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053 · see citing works
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · see citing works
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570 · see citing works
McKay, M. D., Beckman, R. J., & Conover, W. J. (2000) · Technometrics · doi:10.2307/1271432 · Cited by 7,551 · see citing works
Deb, K., & Jain, H. (2013) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2013.2281535 · Cited by 7,213 · see citing works
A review on genetic algorithm: past, present, and future
Katoch, S., Chauhan, S. S., & Kumar, V. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-020-10139-6 · Cited by 4,655 · see citing works
McKay, M. D., Beckman, R. J., & Conover, W. J. (1979) · Technometrics · doi:10.1080/00401706.1979.10489755 · Cited by 3,323 · see citing works
1D convolutional neural networks and applications: A survey
Serkan Kiranyaz, B., Avcı, O., Abdeljaber, O., İnce, T., Gabbouj, M., & Inman, D. J. (2020) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2020.107398 · Cited by 3,041 · see citing works
Theory and applications of HVAC control systems – A review of model predictive control (MPC)
Afram, A., & Janabi‐Sharifi, F. (2013) · Building and Environment · doi:10.1016/j.buildenv.2013.11.016 · Cited by 1,322 · see citing works
Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds
Reinhart, C. F., & Walkenhorst, O. (2001) · Energy and Buildings · doi:10.1016/s0378-7788(01)00058-5 · Cited by 736 · see citing works
Magnier, L., & Haghighat, F. (2009) · Building and Environment · doi:10.1016/j.buildenv.2009.08.016 · Cited by 731 · see citing works
Application of neural networks to modelling nonlinear relationships in ecology
Lek, S., Delacoste, M., Baran, P., Dimopoulos, I. F., Lauga, J., & Aulagnier, S. (1996) · Ecological Modelling · doi:10.1016/0304-3800(95)00142-5 · Cited by 718 · see citing works
Afram, A., Janabi-Sharifi, F., Fung, A. S., & Raahemifar, K. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.02.012 · Cited by 655 · see citing works
Zhao, Y., Li, T., Zhang, X., & Zhang, C. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.04.021 · Cited by 594 · see citing works
Roudsari, M. S., Michelle, P. A. K., & VIOLA, A. (2013) · Building Simulation Conference proceedings · doi:10.26868/25222708.2013.2499 · Cited by 556 · see citing works
Asadi, E., da Silva, M. G., Antunes, C. H., Dias, L. C., & Glicksman, L. R. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.06.009 · Cited by 526 · see citing works
Yu, W. B., Li, B., Jia, H., Zhang, M., & Wang, D. (2014) · Energy and Buildings · doi:10.1016/j.enbuild.2014.11.063 · Cited by 458 · see citing works
Neural networks based predictive control for thermal comfort and energy savings in public buildings
Ferreira, P., Ruano, A. E., Silva, S., & Conceição, E. Z. E. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.08.002 · Cited by 446 · see citing works
Optimization problems for machine learning: A survey
Gambella, C., Ghaddar, B., & Naoum‐Sawaya, J. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2020.08.045 · Cited by 383 · see citing works
Surrogate modelling for sustainable building design – A review
Westermann, P., & Evins, R. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.05.057 · Cited by 345 · see citing works
Gou, S., Nik, V. M., Scartezzini, J., Zhao, Q. C., & Li, Z. (2017) · Energy and Buildings · doi:10.1016/j.enbuild.2017.09.095 · Cited by 316 · see citing works
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 302 · see citing works
Optimal control of HVAC and window systems for natural ventilation through reinforcement learning
Chen, Y., Norford, L. K., Samuelson, H. W., & Malkawi, A. M. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.03.051 · Cited by 289 · see citing works
Negendahl, K. (2015) · Automation in Construction · doi:10.1016/j.autcon.2015.03.002 · Cited by 268 · see citing works
Modeling and optimization of HVAC energy consumption
Kusiak, A., Li, M., & Tang, F. (2010) · Applied Energy · doi:10.1016/j.apenergy.2010.04.008 · Cited by 268 · see citing works
Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method
Liang, J., & Du, R. (2007) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2006.12.012 · Cited by 250 · see citing works
Tuning machine learning models for prediction of building energy loads
Seyedzadeh, S., Rahimian, F. P., Rastogi, P., & Glesk, I. (2019) · Sustainable Cities and Society · doi:10.1016/j.scs.2019.101484 · Cited by 246 · see citing works
Ghahramani, A., Zhang, K., Dutta, K., Yang, Z., & Becerik-Gerber, B. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.12.115 · Cited by 218 · see citing works
Deep Learning for Load Forecasting: Sequence to Sequence Recurrent Neural Networks With Attention
Sehovac, L., & Grolinger, K. (2020) · IEEE Access · doi:10.1109/access.2020.2975738 · Cited by 214 · see citing works
Self-citationChegari, B., Tabaa, M., Simeu, E., Moutaouakkil, F., & Medromi, H. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.110839 · Cited by 213 · see citing works
Dai, X., Liu, J., & Zhang, X. (2020) · Energy and Buildings · doi:10.1016/j.enbuild.2020.110159 · Cited by 204 · see citing works
Meng, D., Yang, S., Jesus, A. M. P. D., Fazeres-Ferradosa, T., & Zhu, S. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116083 · Cited by 204 · see citing works
Meng, D., Yang, H., Yang, S., Zhang, Y., Jesus, A. M. P. D., Correia, J. A. F. O., Fazeres-Ferradosa, T., Macek, W., Branco, R., & Zhu, S. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.116842 · Cited by 192 · see citing works
Si, B., Wang, J., Yao, X., Shi, X., Jin, X., & Zhou, X. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.03.006 · Cited by 189 · see citing works
Shen, Y., & Pan, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2022.120575 · Cited by 188 · see citing works
Huang, H. M., Chen, L., & Hu, E. J. (2015) · Building and Environment · doi:10.1016/j.buildenv.2015.01.037 · Cited by 182 · see citing works
Transformer-Based Model for Electrical Load Forecasting
L’Heureux, A., Grolinger, K., & Capretz, M. A. M. (2022) · Energies · doi:10.3390/en15144993 · Cited by 180 · see citing works
Self-citationGossard, D. C., Lartigue, B., & Thellier, F. (2013) · Energy and Buildings · doi:10.1016/j.enbuild.2013.08.026 · Cited by 175 · see citing works
Xue, Q., Wang, Z., & Chen, Q. (2021) · Building Simulation · doi:10.1007/s12273-021-0796-5 · Cited by 164 · see citing works
Li, X., & Yao, R. (2020) · Energy · doi:10.1016/j.energy.2020.118676 · Cited by 158 · see citing works
Razmi, A., Rahbar, M., & Bemanian, M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117828 · Cited by 157 · see citing works
Multi-objective optimization of HVAC system with an evolutionary computation algorithm
Kusiak, A., Tang, F., & Xu, G. (2011) · Energy · doi:10.1016/j.energy.2011.01.030 · Cited by 140 · see citing works
Predicting the energy consumption in buildings using the optimized support vector regression model
Cai, W. H., Wen, X., Li, C., Shao, J., & Xu, J. (2023) · Energy · doi:10.1016/j.energy.2023.127188 · Cited by 140 · see citing works
Kharvari, F. (2020) · Solar Energy · doi:10.1016/j.solener.2020.07.054 · Cited by 138 · see citing works
An efficient metamodel-based method to carry out multi-objective building performance optimizations
Bre, F., Román, N. D., & Fachinotti, V. D. (2019) · Energy and Buildings · doi:10.1016/j.enbuild.2019.109576 · Cited by 135 · see citing works
Wang, S., Yi, Y. K., & Liu, N. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108263 · Cited by 134 · see citing works
Xu, Y., Zhang, G., Yan, C., Wang, G., Jiang, Y., & Zhao, K. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108142 · Cited by 116 · see citing works
AI in HVAC fault detection and diagnosis: A systematic review
Bi, J. Q., Wang, H., Yan, E., Wang, C., Yan, K., Jiang, L., & Yang, B. (2024) · Energy Reviews · doi:10.1016/j.enrev.2024.100071 · Cited by 112 · see citing works
Magni, M., Ochs, F., de Vries, S., Maccarini, A., & Sigg, F. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111260 · Cited by 106 · see citing works
Chen, R., Tsay, Y.-S., & Zhang, T. (2022) · Energy · doi:10.1016/j.energy.2022.125373 · Cited by 106 · see citing works
Kim, W., Jeon, Y., & Kim, Y. (2015) · Applied Energy · doi:10.1016/j.apenergy.2015.10.153 · Cited by 105 · see citing works
Chen, R., Tsay, Y.-S., & Ni, S. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131978 · Cited by 105 · see citing works
He, Q., Li, Z., Gao, W., Chen, H., Wu, X., Cheng, X., & Lin, B. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108346 · Cited by 104 · see citing works
Saryazdi, S. M. E., Etemad, A., Shafaat, A., & Bahman, A. M. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109633 · Cited by 104 · see citing works
Liu, Y., Li, T., Xu, W., Wang, Q., Huang, H., & He, B. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113665 · Cited by 103 · see citing works
Płoszaj-Mazurek, M., Ryńska, E. D., & Grochulska-Salak, M. (2020) · Energies · doi:10.3390/en13205289 · Cited by 102 · see citing works
Survey on Explainable AI: Techniques, challenges and open issues
Abusitta, A., Li, M. Q., & Fung, B. C. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124710 · Cited by 98 · see citing works
Westermann, P., Welzel, M., & Evins, R. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115563 · Cited by 94 · see citing works
Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review
Hodavand, F., Ramaji, I. J., & Sadeghi, N. (2023) · Buildings · doi:10.3390/buildings13061426 · Cited by 93 · see citing works
Wu, X., Feng, Z., Chen, H., Qin, Y., Zheng, S., Wang, L., Liu, Y., & Skibniewski, M. J. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112703 · Cited by 91 · see citing works
Deep reinforcement learning for multi-objective optimization in BIM-based green building design
Pan, Y., Shen, Y., Qin, J., & Zhang, L. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105598 · Cited by 90 · see citing works
Li, K., Pan, L., Xue, W., Jiang, H., & Mao, H. (2017) · Energies · doi:10.3390/en10020245 · Cited by 89 · see citing works
Intelligent multiobjective optimization design for NZEBs in China: Four climatic regions
Wu, X., Li, X., Qin, Y., Xu, W. H., & Liu, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120934 · Cited by 84 · see citing works
On the performance of meta-models in building design optimization
Prada, A., Gasparella, A., & Baggio, P. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.04.129 · Cited by 80 · see citing works
Multi-objective building design optimization considering the effects of long-term climate change
Zou, Y., Lou, S., Xia, D., Lun, I. Y.-F., & Yin, J. (2021) · Journal of Building Engineering · doi:10.1016/j.jobe.2021.102904 · Cited by 77 · see citing works
Han, Y., Shen, L., & Sun, C. T. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107932 · Cited by 76 · see citing works
Homod, R. Z., Sahari, K. S. M., & Almurib, H. A. F. (2014) · Renewable Energy · doi:10.1016/j.renene.2014.06.015 · Cited by 75 · see citing works
Yue, N., Li, L., Morandi, A., & Zhao, Y. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111513 · Cited by 73 · see citing works
Building Heating and Cooling Load Prediction Using Ensemble Machine Learning Model
Chaganti, R., Rustam, F., Daghriri, T., Díez, I. D. L. T., Mazón, J. L. V., Rodríguez, C. L., & Ashraf, I. (2022) · Sensors · doi:10.3390/s22197692 · Cited by 72 · see citing works
Elwy, I., & Hagishima, A. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114769 · Cited by 71 · see citing works
Cruz, A. S., Caldas, L. R., Mendes, V. M., Mendes, J. C., & Bastos, L. E. G. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.112147 · Cited by 71 · see citing works
Building energy optimization using surrogate model and active sampling
Bamdad, K., Cholette, M. E., & Bell, J. M. (2020) · Journal of Building Performance Simulation · doi:10.1080/19401493.2020.1821094 · Cited by 68 · see citing works
Kerdan, I. G., & Gálvez, D. M. (2020) · Applied Energy · doi:10.1016/j.apenergy.2020.115862 · Cited by 64 · see citing works
Chegari, B., Tabaa, M., Simeu, E., Moutaouakkil, F., & Medromi, H. (2021) · Energy · doi:10.1016/j.energy.2022.123584 · Cited by 64 · see citing works
A probabilistic approach to diagnose faults of air handling units in buildings
Dey, D., & Dong, B. (2016) · Energy and Buildings · doi:10.1016/j.enbuild.2016.08.017 · Cited by 63 · see citing works
A meta-model-based optimization approach for fast and reliable calibration of building energy models
Chen, J., Gao, X., Hu, Y., Zeng, Z., & Liu, Y. (2019) · Energy · doi:10.1016/j.energy.2019.116046 · Cited by 61 · see citing works
Yang, H., Xu, Z., Shi, Y., Tang, W., Liu, C., Yunusa‐Kaltungo, A., & Cui, H. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107807 · Cited by 60 · see citing works
Sun, C. T., Liu, Q., & Han, Y. (2020) · Applied Sciences · doi:10.3390/app10072435 · Cited by 57 · see citing works
Toosi, H. A., Lavagna, M., Leonforte, F., Pero, C. D., & Aste, N. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108656 · Cited by 56 · see citing works
Jin, Z., He, W., & Huang, J. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.122423 · Cited by 55 · see citing works
Dey, M., Rana, S. P., & Dudley, S. (2020) · Smart Cities · doi:10.3390/smartcities3020021 · Cited by 51 · see citing works
Improved thermal comfort modeling for smart buildings: A data analytics study
Zhang, W., Liu, F., & Fan, R. (2018) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2018.06.026 · Cited by 49 · see citing works
Alsharif, R., Arashpour, M., Golafshani, E. M., Rashidi, A., & Li, H. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112840 · Cited by 49 · see citing works
Improving the performance of prefabricated houses through multi-objective optimization design
Ji, Y., Lv, J., Li, H., Liu, Y., Yao, F., Liu, X., & Wang, S. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.108579 · Cited by 47 · see citing works
Shen, Y., Hu, Y., Cheng, K., Yan, H., Cai, K., Hua, J., Fei, X., & Wang, Q. (2024) · Building Simulation · doi:10.1007/s12273-024-1108-7 · Cited by 45 · see citing works
BIM Integration with XAI Using LIME and MOO for Automated Green Building Energy Performance Analysis
Khan, A. M., Tariq, M. A., Rehman, S. K. U., Saeed, T., Alqahtani, F. K., & Sherif, M. (2024) · Energies · doi:10.3390/en17133295 · Cited by 44 · see citing works
Lin, Y., Zhao, L., Liu, X., Yang, W. H., Hao, X., & Tian, L. (2021) · Buildings · doi:10.3390/buildings11050192 · Cited by 41 · see citing works
Li, C., & Chen, Y. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.112893 · Cited by 41 · see citing works
A Novel Approach for Optimizing Building Energy Models Using Machine Learning Algorithms
Kubwimana, B., & Najafi, H. (2023) · Energies · doi:10.3390/en16031033 · Cited by 40 · see citing works
Fault diagnosis of HVAC AHUs based on a BP-MTN classifier
Yan, Y., Cai, J., Tang, Y., & Chen, L. (2022) · Building and Environment · doi:10.1016/j.buildenv.2022.109779 · Cited by 39 · see citing works
A multi-objective optimization framework for building performance under climate change
Li, Z., Zhao, Y., Xia, H., & Xie, S. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.107978 · Cited by 38 · see citing works
Li, H., Wang, S., & Tang, R. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113683 · Cited by 36 · see citing works
Si, B., Ni, Z., Xu, J., Li, Y., & Liu, F. (2024) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2024.104124 · Cited by 36 · see citing works
Automated fault detection and diagnosis of HVAC subsystems using statistical machine learning
West, S. R., Guo, Y., Wang, X. R., & Wall, J. (2011) · Cited by 36 · see citing works
Bereketeab, L., Zekeria, A., Aloqaily, M., Guizani, M., & Debbah, M. A. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3355229 · Cited by 33 · see citing works
Zong, C., Chen, X., Deghim, F., Staudt, J., Geyer, P., & Lang, W. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111211 · Cited by 32 · see citing works
Xu, X., Feng, G., Chi, D., Liu, M., & Dou, B. (2018) · Energies · doi:10.3390/en11123252 · Cited by 30 · see citing works
Toward the Application of a Machine Learning Framework for Building Life Cycle Energy Assessment
Venkatraj, V., Dixit, M., Yan, W., Caffey, S. M., Sideris, P., & Aryal, A. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113444 · Cited by 27 · see citing works
Yue, N., Caini, M., Li, L., Zhao, Y., & Li, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120481 · Cited by 24 · see citing works
Sustainable Building Optimization Model for Early-Stage Design
Elbeltagi, E. E., Wefki, H., & Khallaf, R. (2022) · Buildings · doi:10.3390/buildings13010074 · Cited by 24 · see citing works
Modeling and optimization method for building energy performance in the design stage
Li, C., & Chen, Y. (2024) · Journal of Building Engineering · doi:10.1016/j.jobe.2024.109019 · Cited by 24 · see citing works
Lahmar, S., Maalmi, M., & Idchabani, R. (2023) · Journal of Building Engineering · doi:10.1016/j.jobe.2023.108341 · Cited by 22 · see citing works
Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need
Barbaresi, A., Ceccarelli, M., Menichetti, G., Torreggiani, D., Tassinari, P., & Bovo, M. (2022) · Energies · doi:10.3390/en15041266 · Cited by 21 · see citing works
MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization
Batres, R., Dadras, Y., Mostafazadeh, F., & Kavgic, M. (2023) · Energies · doi:10.3390/en16207026 · Cited by 20 · see citing works
Arjomandnia, R., Ilbeigi, M., Kazemidemneh, M., & Hashemi, A. N. (2023) · Indoor and Built Environment · doi:10.1177/1420326x231151244 · Cited by 19 · see citing works
Kerdan, I. G., & Gálvez, D. M. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.133500 · Cited by 18 · see citing works
PSACONN mining algorithm for multi-factor thermal energy-efficient public building design
Himmetoğlu, S., Delice, Y., & Aydoğan, E. K. (2020) · Journal of Building Engineering · doi:10.1016/j.jobe.2020.102020 · Cited by 16 · see citing works
Yu, F., & Leng, J. (2020) · Indoor and Built Environment · doi:10.1177/1420326x20941220 · Cited by 15 · see citing works
You, X., Yan, G., & Thwin, M. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16593 · Cited by 14 · see citing works
Zheng, Z., Zhou, J. Z., Yang, Y., Xu, F., & Liu, H. (2024) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2024.102582 · Cited by 12 · see citing works
Zhao, S. (2021) · Journal of Computational Design and Engineering · doi:10.1093/jcde/qwab005 · Cited by 11 · see citing works
Thermal performance optimization for housing unit design in a cold region of China
Wang, S., Liu, N., & Zhang, J. (2021) · Journal of Building Performance Simulation · doi:10.1080/19401493.2021.1970811 · Cited by 10 · see citing works
Optimising Building Energy and Comfort Predictions with Intelligent Computational Model
Alghamdi, S., Tang, W., Kanjanabootra, S., & Alterman, D. (2024) · Sustainability · doi:10.3390/su16083432 · Cited by 9 · see citing works
Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation
Storcz, T., Várady, G., Kistelegdi, I., & Ercsey, Z. (2023) · Energies · doi:10.3390/en16165896 · Cited by 7 · see citing works
Didwania, S. K., Reddy, T. A., & Addison, M. S. (2023) · Journal of Architectural Engineering · doi:10.1061/jaeied.aeeng-1521 · Cited by 4 · see citing works
Performance Evaluation of Chiller Fault Detection and Diagnosis Using Only Field-Installed Sensors
Wang, Z., Guo, J., Zhou, S., & Xia, P. (2023) · Processes · doi:10.3390/pr11123299 · Cited by 4 · see citing works
Residential Building Design and Optimization in Arid Climates using Multiple Objectives and ANN-GA
Belahya, H., Boubekri, A., Belarbi, R., & Romani, Z. (2023) · Journal of Advanced Research in Fluid Mechanics and Thermal Sciences · doi:10.37934/arfmts.110.2.1431 · Cited by 3 · see citing works
An Innovative Multicriteria Decision-Making Tool for Building Performance Optimization
Serbouti, A., Rattal, M., Oualim, E. M., & Mouhsen, A. (2021) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.3953 · Cited by 2 · see citing works
Automated passive measures: the next step in reducing the carbon footprint of our buildings
Ramessur, N., & Gooroochurn, M. (2021) · IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) · doi:10.1109/csde53843.2021.9718458 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Bahadori‐Jahromi, A., Room, S., Paknahad, C., Al‐tekreeti, M., Tariq, Z. Z., & Tahayori, H. (2025) · Applied Sciences · doi:10.3390/app151910499 · Cited by 50 · see citing works
Tan, Q., & Siroux, M. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115922 · Cited by 35 · see citing works
Artificial intelligence in civil engineering: emerging applications and opportunities
Nyokum, T., & Tamut, Y. (2025) · Frontiers in Built Environment · doi:10.3389/fbuil.2025.1622873 · Cited by 33 · see citing works
A Decentralized Hierarchical Multi-Agent Framework for Smart Grid Sustainable Energy Management
Dragomir, O. E., & Dragomir, F. (2025) · Sustainability · doi:10.3390/su17125423 · Cited by 23 · see citing works
Kyriaki, E., Giama, E., & Theodoridou, I. (2025) · International Journal of Sustainable Energy · doi:10.1080/14786451.2025.2544238 · Cited by 18 · see citing works
Ehtsham, M., Parisi, G., Pedone, F., Rossi, F., Zincani, M., Congiu, E., & Marchionni, C. (2025) · Sustainability · doi:10.3390/su17178005 · Cited by 18 · see citing works
Woldegiyorgis, T. A., Li, H., Asmare, E., Assamnew, A. D., Chekol, F., Desalegn, G. A., Asefa, S. K., & Mossie, S. Y. (2025) · Energy Informatics · doi:10.1186/s42162-025-00589-3 · Cited by 15 · see citing works
Moghadam, M. A., & Besiktepe, D. (2025) · Buildings · doi:10.3390/buildings15183258 · Cited by 14 · see citing works
Xu, L., Zhu, H., Shen, Y., Liu, T., Zhou, L. W., & Feng, S. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116251 · Cited by 10 · see citing works
Xie, X., Ni, Y., & Zhang, T. (2025) · Sustainability · doi:10.3390/su17094090 · Cited by 9 · see citing works
Zhang, B., Chen, J., Song, Y., Zhong, L., Chen, L., & Zhang, Y. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117238 · Cited by 8 · see citing works
Tran, V., & Lee, J. (2025) · Engineering Structures · doi:10.1016/j.engstruct.2025.121146 · Cited by 7 · see citing works
Are AI and environmental technology innovations converging?
Herman, K. S., Amortegui, T. G., & Griffiths, S. (2026) · Technology in Society · doi:10.1016/j.techsoc.2026.103222 · Cited by 7 · see citing works
Recent advances in machine learning for advanced building envelopes: From prediction to optimisation
Li, X., Zhang, L., Tang, Y., Chen, Q., Sun, W., Fang, X., Tao, Y., Shang, B., & Tu, J. Y. (2026) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2026.129747 · Cited by 7 · see citing works
Lyu, H., Herring, D. H., Chen, H., Zhou, S., Zhang, J., Wang, L., Zuo, Z., Andrews, J., Kočvara, M., Spill, F., et al. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116100 · Cited by 6 · see citing works
Alana, H. A., Fikry, M., & Hasan, A. E.-S. (2026) · Buildings · doi:10.3390/buildings16071445 · Cited by 6 · see citing works
Tian, Y., Jiang, Y., Jiang, Y., & Mrzljak, V. (2026) · Buildings · doi:10.3390/buildings16132569 · Cited by 6 · see citing works
Integrating life cycle assessment with GIS and AI for the building sector: a comprehensive review
Xie, H., Li, S., Zaman, A. U., Song, Y., & Wu, P. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117095 · Cited by 5 · see citing works
Manmatharasan, P., Bitsuamlak, G., & Grolinger, K. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117251 · Cited by 5 · see citing works
Self-citationChi, F., & Zhu, G. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.127227 · Cited by 4 · see citing works
Çelik, T. (2025) · Artificial intelligence for engineering design analysis and manufacturing · doi:10.1017/s0890060425100085 · Cited by 4 · see citing works
AI and 6E evaluation on solar cooking system for sustainable development growth in solar industry
Kumar, A., Muthusamy, E., Kumar, R., Kumar, I., & Kapoor, G. (2025) · Journal of Renewable and Sustainable Energy · doi:10.1063/5.0292806 · Cited by 4 · see citing works
Hashemi, A., Yeganeh, M., & Ghayehbashi, A. R. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.107846 · Cited by 4 · see citing works
Breaking-down building design problems with decomposition approaches: A review
Bonyadi, N., Talami, R., & Lee, B. (2025) · Energy and Built Environment · doi:10.1016/j.enbenv.2025.05.005 · Cited by 4 · see citing works
AI-driven design: a comprehensive review of its methodologies, applications, and challenges
Yan, J., Hu, W., Zhao, Y., Zhang, T., Zhao, F., Fang, J., Shi, W. Y., & Tan, J. (2026) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-026-04331-9 · Cited by 4 · see citing works
Kumar, A., Bag, R., Nayak, B. P., & Kumar, D. R. (2026) · Geothermics · doi:10.1016/j.geothermics.2026.103709 · Cited by 3 · see citing works
Generative AI for Sustainable Architectural Design Optimization
Yang, L. (2025) · Scientific Journal of Technology · doi:10.54691/czaf4b21 · Cited by 2 · see citing works
Stojiljković, M., Jovanović, V. S., Ranđelović, D., Vučković, G., & Ignjatović, M. (2025) · Thermal Science · doi:10.2298/tsci250227102s · Cited by 2 · see citing works
Liu, H., & Ma, W. (2025) · Microchemical Journal · doi:10.1016/j.microc.2025.115466 · Cited by 2 · see citing works
Sapnken, F. E., Wang, Y., Xie, N., Hamaidi, M., Ntegmi, G. J. B., & Molu, R. J. J. (2026) · Energy 360. · doi:10.1016/j.energ.2026.100061 · Cited by 2 · see citing works
Surrogate model-based design optimization of modular façades under multifunctional scenarios
Zhang, C., Zhong, W., Li, K., Hu, M., Zheng, H., & Zhang, T. (2026) · Building and Environment · doi:10.1016/j.buildenv.2026.114475 · Cited by 2 · see citing works
Gao, P., Mohd-Nor, M. F. I., & Sulaiman, M. K. A. M. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117807 · Cited by 2 · see citing works
Artificial Intelligence in Civil Engineering
Selvapriya, R., Gopinath, M., Tamilchelvan, P., Sampathkumar, P., Velmurugan, D., & Suresh, S. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-4839-1.ch004 · Cited by 1 · see citing works
Rahman, S., Ahsan, A., & Pramanik, N. I. (2025) · Sustainability · doi:10.3390/su17156891 · Cited by 1 · see citing works
A hyper-intelligent framework for large-scale structural optimisation
Jalili, S., & Talatahari, S. (2025) · Structures · doi:10.1016/j.istruc.2025.110858 · Cited by 1 · see citing works
Chen, W.-H., Tayag, C. R., & Ubando, A. T. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117410 · Cited by 1 · see citing works
Assareh, E., Izadyar, N., Mobayen, S., Jamei, E., & Monzavian, M. A. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114886 · Cited by 1 · see citing works
Hamzah, M. K., Shafie, H. E., & Althobaiti, M. M. (2026) · Sustainability · doi:10.3390/su18105067 · Cited by 1 · see citing works
Harnessing AI for sustainable engineering: innovations and challenges
Umar, T. (2026) · Proceedings of the Institution of Civil Engineers - Engineering Sustainability · doi:10.1680/jensu.25.00054 · Cited by 1 · see citing works
Whole-process AI in building applications: A framework of integrating AI with domain knowledge
Yan, D., Dong, B., O’Neill, Z. D., Chen, Z., Wang, X., Jiang, Z., Song, R., Kang, X., & Zhou, X. (2026) · Building Simulation · doi:10.1007/s12273-026-1487-z · Cited by 1 · see citing works
Zhu, P., & Sun, Y. (2025) · Sustainable Futures · doi:10.1016/j.sftr.2025.100884 · Cited by 1 · see citing works
Benefits of Artificial Intelligence for Achieving Durable and Sustainable Building Design
Alariyan, A. R., Amin, R. A. M., Youns, A. M., Alhashash, M., Ghreivati, F., Habib, A., & Habib, M. (2025) · Structural durability & health monitoring · doi:10.32604/sdhm.2025.069821 · Cited by 1 · see citing works
AI Innovation for Energy Efficiency Floor Plan Design
Dwiananda, M. F., Sari, D. P., Harsritanto, B. I., & Putri, M. N. H. (2025) · E3S Web of Conferences · doi:10.1051/e3sconf/202565001003 · Cited by 1 · see citing works
Chauhan, R. G. S., Mothe, Y., & Gupta, C. (2026) · Asian Journal of Civil Engineering · doi:10.1007/s42107-026-01635-5 · Cited by 1 · see citing works
Hong, S. M., & Choo, S. (2025) · Buildings · doi:10.3390/buildings15193477
Xu, Y. (2025) · International Journal of Environmental Sciences · doi:10.64252/vyj20756
Chirinos, D. R. S., Rosa-Boggio, D. O. L., & Samillán, R. A. T. (2025) · LACCEI International Multiconference on Entrepreneurship, Innovation and Regional Development (LEIRD) · doi:10.18687/leird2025.1.1.800
Application of Ant Colony Optimization in Pathfinding for Drones
Obaideen, K., Gadsden, S. A., Alshabi, M., & Wilkerson, S. A. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-0433-6_38
Shin, J., Lee, S., Bae, S., Kim, H., & Oh, S. (2025) · 한국생활환경학회지 · doi:10.21086/ksles.2025.12.32.6.857
Exploring Green and Low-Carbon Community Design Through a Dual-Carbon Lens
Zheng, Y., Li, X.-X., & Xu, L. (2026) · Environmental science and engineering · doi:10.1007/978-3-032-11910-0_42
Efstathiou, A., Vergopoulou, M., & Vergopoulou, C. (2026) · Springer proceedings in business and economics · doi:10.1007/978-3-032-12968-0_53
The Role of Artificial Intelligence in Sustainable Floor Plan Generation: A Review
Ojha, I., Dwivedi, P., & Singh, A. (2026) · Contemporary Computing Innovations Conference (CCIC) · doi:10.1109/ccic68129.2026.11485897
Xushnazarova, M. G., Dilfuza, R., Bakhromov, A., Iroda, A., & Makhbuba, A. (2026) · EPJ Web of Conferences · doi:10.1051/epjconf/202636902009
Dai, R., Zhang, G., Bai, G., yang, L., Wang, M., & Wang, Z. (2026) · Energy and Buildings · doi:10.1016/j.enbuild.2026.117675
A Review of AI-Based Optimization and Monitoring Using Wearable Data in Smart Buildings
Varzaneh, S. G., Zaman, M., Zohrabi, N., & Abdelwahed, S. (2026) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech67720.2026.11536515
Efficient graph neural networks for predicting the responses of truss structures
Tran, V., & Lee, J. I. (2026) · Engineering With Computers · doi:10.1007/s00366-026-02365-7
The importance of demand response for large scale renewable integration
Campagna, N., de Lima, T. D., Lezama, F., & Yahyaoui, I. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-29210-1.00133-3
Tran, V., & Nguyen-Thoi, T. (2026) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-026-14430-9
Okula, H. E. (2026) · Iconic Research and Engineering Journals · doi:10.64388/irev10i1-1719816
Digital design integration: BIM, parametric design, and AI in smart architecture
Habibi, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-30223-7.00001-2
Polisetty, S., Klemmt, C., & Anand, S. (2026) · Manufacturing Letters · doi:10.1016/j.mfglet.2026.08.091
gholami, H., Sabetfard, M., & Rahbar, M. (2026) · International Journal of Architectural Computing · doi:10.1177/14780771261467183
Serrano-Arellano, J., Gómez, Y. C. R., Hernández-López, M. I., Demesa, N., Abarca–Jiménez, G. S., Aguilar-Castro, K. M., & Belman-Flores, J. M. (2026) · Sustainability · doi:10.3390/su182010280
From Design Intent to Low-Carbon Outcome: AI as a Tool for Embodied-Carbon Reduction
Thompson, A., Muri, O. H., O’Born, R., & Bohne, R. A. (2026) · Lecture notes in civil engineering · doi:10.1007/978-3-032-34930-9_173
Shanbhag, S. S., & Dixit, M. (2026) · Architectural Engineering Conference (AEI) · doi:10.1061/9780784486955.003
Berad, P. P. B. C. O. H., & Jadhav, D. H. B. (2026) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-38960
A conceptual AI-driven architecture coupling sustainable materials data and structural performance
Mahamat, A. A., & Leklou, N. (2026) · Innovative Infrastructure Solutions · doi:10.1007/s41062-026-03063-5
Reffat, R. M. (2026) · International Conference on Robotics, Control and Automation (ICRCA) · doi:10.1109/icrca69813.2026.11749832
Explainable AI for Multiobjective Design Decisions in University Teaching Buildings
李国锋, Dai, A., Jiang, X., 郑卫民, & Han, L. (2026) · Buildings · doi:10.3390/buildings16214374
Generative AI for Designing Energy-Efficient Mechanical Systems in Industry
Chimankar, A. G., Alase, S. R., Khan, M. Y., A.Thirumoorthy, Joshi, P. G., & Karthick, L. (2026) · doi:10.1109/icact70625.2026.11748646
- Authors
- Manmatharasan, P., Bitsuamlak, G., & Grolinger, K.
- Affiliations
- Western University (CA)
- Corresponding author
- Grolinger, Katarina
- Venue
- Energy and Buildings
- Publisher
- Elsevier BV
- Published
- 2025-04
- Volume
- 332
- Pages
- 115440
- ISSN
- 0378-7788 (ISSN-L)
- OpenAlex
- W4407352559
- Cited by
- 63
- References
- 123
- Citations by year
- 2026: 36 · 2025: 27
- FWCI
- 7.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Energy and Buildings (publishedVersion, cc-by-nc)
- Funders
- Environment Canada (EDF-CA-2021i018)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Advanced Multi-Objective Optimization Algorithms
- Physical Sciences › Engineering › Building and Construction › Building energy efficiency and sustainability
- Keywords
- artificial intelligence, building design optimization, sustainable construction, surrogate models, building energy performance, HVAC control, fault detection, building energy consumption, CO2 emissions
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review