Artificial Neural Network–Based Generative Design Optimization of the Energy Performance of Florida Single-Family Houses
Publication: Journal of Computing in Civil Engineering
Volume 38, Issue 2
Abstract
The energy efficiency optimization of Florida’s residential buildings is essential for the reduction of fossil fuel consumption and greenhouse gas emissions. This optimization requires designers to accurately calculate the building energy loads at the design stage to efficiently design the cooling and heating systems. However, due to time and cost constraints, designers usually explore only a few design alternatives and simulate their energy use. This study developed an artificial-neural-network (ANN)-based generative design (GD) framework to automate the design process of detached residences while optimizing their energy performance. The ANN model was developed using the machine learning platform TensorFlow and some Python-based Keras libraries based on a big data set of about 17,000 newly constructed detached residences in Florida between the years 2009 and 2021. The GD framework was established using Autodesk Dynamo and the Autodesk Revit GD add-on that uses the nondominated sorting genetic algorithm (NSGA-II), in which multiple design parameters mainly relating to the house geometry and its energy performance were incorporated. Considering 10 independent variables, including the total wall area, roof area, floor area, and the windows’ U-value, the ANN model predicted the required capacities of the cooling and heating systems in detached houses, with values of 0.955 and 0.904, respectively. The ANN then was integrated within the GD framework for performance evaluation purposes. The findings of this study resulted in a fully automated 3-min design process of an energy-efficient detached house envelope, maximizing the productivity of designers and developers and greatly reducing the financial strain and time consumed using traditional techniques.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
All data, models, or code generated or used during the study are proprietary or confidential in nature and may be provided only with restrictions. This includes the FEECBC data and code used for training, validating, and testing the ANN model, the optimal ANN model integrated within the ANN-based GD system, the Microsoft Visual Studio Python script for ANN predictions, and the ANN-based Dynamo GD code.
References
Al-Saadi, S. N., and K. S. Al-Jabri. 2020. “Optimization of envelope design for housing in hot climates using a genetic algorithm (GA) computational approach.” J. Build. Eng. 32 (Nov): 101712. https://doi.org/10.1016/j.jobe.2020.101712.
Alwisy, A., S. BuHamdan, and M. Gül. 2018. “Criteria-based ranking of green building design factors according to leading rating systems.” Energy Build. 178 (Nov): 347–359. https://doi.org/10.1016/j.enbuild.2018.08.043.
Ascione, F., N. Bianco, C. De Stasio, G. Mauro, and G. P. Vanoli. 2017. “Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach.” Energy 118 (Jan): 999–1017. https://doi.org/10.1016/j.energy.2016.10.126.
Autodesk. 2021. “Generation settings.” Accessed March 8, 2023. https://knowledge.autodesk.com/support/revit/learn-explore/caas/CloudHelp/cloudhelp/2021/ENU/Revit-Model/files/GUID-710D71BE-3376-4647-822A-DA4D793DCBFA-htm.html#:∼:text=A%20sufficient%20size%20depends%20on,times%20the%20number%20of%20inputs.
Caldas, L. 2008. “Generation of energy-efficient architecture solutions applying GENE_ARCH: An evolution-based generative design system.” Adv. Eng. Inf. 22 (1): 59–70. https://doi.org/10.1016/j.aei.2007.08.012.
Caldas, L., and L. Norford. 2001. “Architectural constraints in a generative design system: Interpreting energy consumption levels.” In Proc., 7th Int. IBPSA Conf. IBPSA, 1397–1403. Cambridge, MA: Univ. of Cambridge.
Cebrat, K., and Ł. Nowak. 2018. “Revealing the relationships between the energy parameters of single-family buildings with the use of self-organizing maps.” Energy Build. 178 (Nov): 61–70. https://doi.org/10.1016/j.enbuild.2018.08.028.
Chen, K., P. Janssen, and A. Schlueter. 2018. “Multi-objective optimisation of building form, envelope and cooling system for improved building energy performance.” Autom. Constr. 94 (Oct): 449–457. https://doi.org/10.1016/j.autcon.2018.07.002.
Cheng, C., J. Ninic, and W. Tizani. 2019. “Parametric virtual design-based multi-objective optimization for sustainable building design.” In Proc., EG-ICE 2019 26th Int. Workshop on Intelligent Computing in Engineering. Aachen, Germany: RWTH Aachen Univ.
Collette, Y., and P. Siarry. 2003. Multiobjective optimization: Principles and case studies. Berlin: Springer.
Deb, K., A. Pratap, S. Agarwal, and T. Meyarivan. 2002. “A fast and elitist multiobjective genetic algorithm: NSGA-II.” IEEE Trans. Evol. Comput. 6 (2): 182–197. https://doi.org/10.1109/4235.996017.
De Buck, V., P. Nimmegeers, I. Hashem, C. A. Muñoz López, and J. Van Impe. 2021. “Exploiting trade-off criteria to improve the efficiency of genetic multi-objective optimisation algorithms.” Front. Chem. Eng. 3 (Mar): 582123. https://doi.org/10.3389/fceng.2021.582123.
EIA (US Energy Information Administration). 2021. International energy outlook 2021. Washington, DC: EIA.
EIA (US Energy Information Administration). 2023. Florida state energy profile. Washington, DC: EIA.
Elias, R., and R. R. A. Issa. 2019. “Big data: A decade of energy characteristics of single-family homes in Florida.” In Proc., 1st ACM Int. Workshop on Urban Building Energy Sensing, Controls, Big Data Analysis, and Visualization (UrbSys’19), 101–111. New York: Association for Computing Machinery. https://doi.org/10.1145/3363459.3363533.
FBC (Florida Building Code). 2020. Energy conservation. 7th ed. Washington, DC: International Code Council.
Gotshall, S. P., and B. Rylander. 2002. “Optimal population size and the genetic algorithm.” In Proc., World Scientific and Engineering Academy and Society Int. Conf. on Soft Computing, Optimization, Simulation, and Manufacturing Systems, 1–5. Cancun, Mexico: Engineering Academy and Society.
Grierson, D. E. 2006. “Welfare economics applied to design engineering.” In Intelligent computing in engineering and architecture, edited by I. F. C. Smith. Berlin: Springer. https://doi.org/10.1007/11888598_28.
Holland, J. H. 1975. Adaptation in natural and artificial systems. 2nd ed. Ann Arbor, MI: Univ. of Michigan Press.
Hong, T., Z. Wang, X. Luo, and W. Zhang. 2020. “State-of-the-art on research and applications of machine learning in the building life cycle.” Energy Build. 212 (Apr): 109831. https://doi.org/10.1016/j.enbuild.2020.109831.
ICC (International Code Council). 2008. “2007 Florida code: Residential: Includes 2009 supplement.” Accessed January 5, 2021. https://codes.iccsafe.org/content/FLBCR2007S09/table-of-contents>.
Karaboga, D., and E. Kaya. 2018. “Adaptive network based fuzzy inference system (ANFIS) training approaches: A comprehensive survey.” Artif. Intell. Rev. 52 (4): 2263–2293. https://doi.org/10.1007/s10462-017-9610-2.
Kerdan, G. I., and M. D. Gálvez. 2020. “Artificial neural network structure optimisation for accurately prediction of exergy, comfort and life cycle cost performance of a low energy building.” Appl. Energy 280 (Dec): 115862. https://doi.org/10.1016/j.apenergy.2020.115862.
Khayatian, F., L. Sarto, and G. Dall’O’. 2016. “Application of neural networks for evaluating energy performance certificates of residential buildings.” Energy Build. 125 (Aug): 45–54. https://doi.org/10.1016/j.enbuild.2016.04.067.
Kocabay, S., and S. Alaçam. 2017. “A multi-objective genetic algorithm framework for earlier phases of architectural design.” In Proc., 22nd Int. Conf. of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), 293–303. Hong Kong: Association for Computer-Aided Architectural Design Research in Asia.
Kumar, H., and S. Yadav. 2019. “Hybrid NSGA-II based decision-making in fuzzy multi-objective reliability optimization problem.” SN Appl. Sci. 1 (11): 1–4. https://doi.org/10.1007/s42452-019-1512-2.
Lawrence, C., R. Richman, M. Kordjamshidi, and C. Skarupa. 2021. “Application of surrogate modelling to improve the thermal performance of single-family homes through archetype development.” Energy Build. 237 (Apr): 110812. https://doi.org/10.1016/j.enbuild.2021.110812.
Lee, S., S. Jung, and J. Lee. 2019. “Prediction model based on an artificial neural network for user-based building energy consumption in South Korea.” Energies 12 (4): 608. https://doi.org/10.3390/en12040608.
Li, X., and R. Yao. 2020. “A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour.” Energy 212 (Dec): 118676. https://doi.org/10.1016/j.energy.2020.118676.
Moradzadeh, A., A. Mansour-Saatloo, B. Mohammadi-Ivatloo, and A. Anvari-Moghaddam. 2020. “Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings.” Appl. Sci. 10 (11): 3829. https://doi.org/10.3390/app10113829.
Rahmani Asl, M., S. Zarrinmehr, M. Bergin, and W. Yan. 2015. “BPOpt: A framework for BIM-based performance optimization.” Energy Build. 108 (Dec): 401–412. https://doi.org/10.1016/j.enbuild.2015.09.011.
Seo, J., S. Kim, S. Lee, H. Jeong, T. Kim, and J. Kim. 2022. “Data-driven approach to predicting the energy performance of residential buildings using minimal input data.” Build. Environ. 214 (Apr): 108911. https://doi.org/10.1016/j.buildenv.2022.108911.
Seyedzadeh, S., F. Rahimian, I. Glesk, and M. Roper. 2018. “Machine learning for estimation of building energy consumption and performance: A review.” Visualization Eng. 6 (1): 1–20. https://doi.org/10.1186/s40327-018-0064-7.
Sholahudin, A., A. G. Alam, C. I. Baek, and H. Han. 2016. “Prediction and analysis of building energy efficiency using artificial neural network and design of experiments.” Appl. Mech. Mater. 819 (Feb): 541–545.
Sun, Y., F. Haghighat, and B. C. M. Fung. 2020. “A review of the-state-of-the-art in data-driven approaches for building energy prediction.” Energy Build. 221 (Aug): 110022. https://doi.org/10.1016/j.enbuild.2020.110022.
Thill, J. 2020. Generative design in energy efficient buildings. Houghton, MI: Michigan Technological Univ.
Toutou, A., M. Fikry, and W. Mohamed. 2018. “The parametric based optimization framework daylighting and energy performance in residential buildings in hot arid zone.” Alexandria Eng. J. 57 (4): 3595–3608. https://doi.org/10.1016/j.aej.2018.04.006.
Tuhus-Dubrow, D., and M. Krarti. 2010. “Genetic-algorithm based approach to optimize building envelope design for residential buildings.” Build. Environ. 45 (7): 1574–1581. https://doi.org/10.1016/j.buildenv.2010.01.005.
Vrajitoru, D. 2000. “Large population or many generations for genetic algorithms? Implications in information retrieval.” In Soft computing in information retrieval: Techniques and applications, 199–222. Berlin: Springer.
Wang, Z., and R. Srinivasan. 2015. “A review of artificial intelligence based building energy prediction with a focus on ensemble prediction models.” In Proc., Winter Simulation Conf., 3438–3445. New York: IEEE.
Wang, Z., R. Srinivasan, and J. Shi. 2016. “Artificial intelligent models for improved prediction of residential space heating.” J. Energy Eng. 142 (4): 04016006. https://doi.org/10.1061/(ASCE)EY.1943-7897.0000342.
Zaraza, J., B. McCabe, M. Duhamel, and D. Posen. 2022. “Generative design to reduce embodied GHG emissions of high-rise buildings.” Autom. Constr. 139 (Jul): 104274. https://doi.org/10.1016/j.autcon.2022.104274.
Zhang, J., N. Liu, and S. Wang. 2021. “Generative design and performance optimization of residential buildings based on parametric algorithm.” Energy Build. 244 (Aug): 111033. https://doi.org/10.1016/j.enbuild.2021.111033.
Information & Authors
Information
Published In
Journal of Computing in Civil Engineering
Volume 38 • Issue 2 • March 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 30, 2023
Accepted: Nov 3, 2023
Published online: Jan 3, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 3, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.