Preprint / Version 1

Advancing Sustainable Architecture Through AI Technologies for Energy Efficiency

A Literature Review

##article.authors##

  • Jiayu Wen Leland High School

DOI:

https://doi.org/10.58445/rars.1787

Keywords:

sustainable architecture, urban planning, sustainability, regeneration, ecological design, artificial intelligence, data science

Abstract

Rapid, global urbanization has contributed to an expansion of the built environment and a subsequent increase in global energy consumption and carbon emissions. Examining the double-edged nature of technological development shows the unprecedented opportunities offered by the growing trend of innovation, as well as the detrimental social, economic, and environmental costs. Thus, it is important to question how the current fundamentally unsustainable fields of architecture and technology can intersect, collaborating to support environmental regeneration. A review of recent advancements highlights the potential of artificial intelligence (AI) technologies for curbing these impacts and reversing ecological destruction. This research specifically focuses on the theme of energy efficiency, delving into various studies on smart energy management systems, predictive modeling and simulation, computational design, and material design. In assessing how the goals of sustainable architecture and urban planning can be supported by AI, future research avenues for holistic approaches to sustainability are uncovered. These findings present the advantage of leveraging the rapidly developing AI technology to support a momentous shift towards regeneration. Such a transition is crucial for designing buildings and urban spaces that are seamlessly integrated into the natural environment, mitigating the effects of climate change and contributing positively to our world.

References

Acemoglu, D. (2021). Harms of AI. National Bureau of Economic Research. https://doi.org/10.3386/w29247

Alsukkar, M., Hu, M., Alkhater, M., & Su, Y. (2023). Daylighting performance assessment of a split louver with parametrically incremental slat angles: Effect of slat shapes and PV glass transmittance. Solar Energy, 264, 112069. https://doi.org/10.1016/j.solener.2023.112069

Badini, S., Regondi, S., & Pugliese, R. (2023). Unleashing the power of artificial intelligence in materials design. Materials, 16(17), 5927. https://doi.org/10.3390/ma16175927

Bibri, S. E., Krogstie, J., Kaboli, A., & Alahi, A. (2024). Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review. Environmental Science and Ecotechnology, 19, 100330. https://doi.org/10.1016/j.ese.2023.100330

Bibri, S. E. (2021). Data-driven smart sustainable cities of the future: An evidence synthesis approach to a comprehensive state-of-the-art literature review. Sustainable Futures, 3, 100047. https://doi.org/10.1016/j.sftr.2021.100047

Bungau, C. C., Bungau, T., Prada, I. F., & Prada, M. F. (2022). Green buildings as a necessity for sustainable environment development: dilemmas and challenges. Sustainability, 14(20), 13121. https://doi.org/10.3390/su142013121

Cafaro, P. (2014). Gluttony, Arrogance, Greed and Apathy: an Exploration of Environmental Vice. Colostate. https://www.academia.edu/4968255/Gluttony_Arrogance_Greed_and_Apathy_an_Exploration_of_Environmental_Vice

Casali, Y., Aydin, N. Y., & Comes, T. (2022). Machine learning for spatial analyses in urban areas: a scoping review. Sustainable Cities and Society, 85, 104050. https://doi.org/10.1016/j.scs.2022.104050

Catalano, C., Meslec, M., Boileau, J., Guarino, R., Aurich, I., Baumann, N., Chartier, F., Dalix,

P., Deramond, S., Laube, P., Lee, A. K. K., Ochsner, P., Pasturel, M., Soret, M., & Moulherat, S. (2021). Smart Sustainable Cities of the new Millennium: Towards Design for Nature. Circular Economy and Sustainability, 1(3), 1053–1086. https://doi.org/10.1007/s43615-021-00100-6

Chen, Z., Lu, J., Wen, J., Wang, X., Deveci, M., & Skibniewski, M. J. (2023). BIM-enabled decision optimization analysis for architectural glass material selection considering sustainability. Information Sciences, 647, 119450. https://doi.org/10.1016/j.ins.2023.119450

Chew, Z. X., Wong, J. Y., Tang, Y. H., Yip, C. C., & Maul, T. (2024). Generative design in the built environment. Automation in Construction, 166, 105638. https://doi.org/10.1016/j.autcon.2024.105638

Dervishaj, A. (2023). From Sustainability to Regeneration: a digital framework with BIM and computational design methods. Architecture Structures and Construction, 3(3), 315–336. https://doi.org/10.1007/s44150-023-00094-9

Falk, S., & Van Wynsberghe, A. (2023). Challenging AI for Sustainability: what ought it mean? AI And Ethics. https://doi.org/10.1007/s43681-023-00323-3

Golafshani, E., Chiniforush, A. A., Zandifaez, P., & Ngo, T. (2024). An artificial intelligence framework for predicting operational energy consumption in office buildings. Energy and Buildings, 317, 114409. https://doi.org/10.1016/j.enbuild.2024.114409

Hazbei, M., Rafati, N., Kharma, N., & Eicker, U. (2024). Optimizing architectural multi-dimensional forms; a hybrid approach integrating approximate evolutionary search, clustering and local optimization. Energy and Buildings, 114460. https://doi.org/10.1016/j.enbuild.2024.114460

Hu, Z., Zhang, L., Shen, Q., Chen, X., Wang, W., & Li, K. (2023). An integrated framework for residential layout designs: Combining parametric modeling, neural networks, and multi-objective optimization for outdoor activity space optimization. Alexandria Engineering Journal, 80, 202–216. https://doi.org/10.1016/j.aej.2023.08.049

Kashkooli, A. M. S., Mahya, P., Habibi, A., & Sharif, H. R. (2018). Developing of evolution analysis

algorithms in regenerative design and decision-making; demonstrated through a case study in Shiraz, Iran. In Creative Construction Conference (Vol. 2018).

Kumar, N., Poonia, V., Gupta, B., & Goyal, M. K. (2021). A novel framework for risk assessment and resilience of critical infrastructure towards climate change. Technological Forecasting and Social Change, 165, 120532. https://doi.org/10.1016/j.techfore.2020.120532

Krausková, V., & Pifko, H. (2021). Use of artificial intelligence in the field of sustainable architecture: Current knowledge. Architecture Papers of the Faculty of Architecture and Design STU, 26(1), 20–29. https://doi.org/10.2478/alfa-2021-0004

Milojevic-Dupont, N., & Creutzig, F. (2021). Machine learning for geographically differentiated climate change mitigation in urban areas. Sustainable Cities and Society, 64, 102526. https://doi.org/10.1016/j.scs.2020.102526

Nazari, M., & Matusiak, B. (2024). Daylighting simulation and visualisation: Navigating challenges in accuracy and validation. Energy and Buildings, 312, 114188. https://doi.org/10.1016/j.enbuild.2024.114188

Park, H., Li, Z., & Walsh, A. (2024). Has generative artificial intelligence solved inverse materials design? Matter, 7(7), 2355–2367. https://doi.org/10.1016/j.matt.2024.05.017

Pena, M. L. C., Carballal, A., Rodríguez-Fernández, N., Santos, I., & Romero, J. (2021). Artificial intelligence applied to conceptual design. A review of its use in architecture. Automation in Construction, 124, 103550. https://doi.org/10.1016/j.autcon.2021.103550

Płoszaj-Mazurek, M., Ryńska, E., & Grochulska-Salak, M. (2020). Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design. Energies, 13(20), 5289. https://doi.org/10.3390/en13205289

Rane, N. (2023). Integrating Leading-Edge Artificial Intelligence (AI), Internet of things (IoT), and big Data technologies for smart and Sustainable Architecture, Engineering and Construction (AEC) industry: challenges and future directions. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4616049

Regona, M., Yigitcanlar, T., Hon, C., & Teo, M. (2024). Artificial Intelligence and Sustainable Development Goals: Systematic Literature Review of the construction industry. Sustainable Cities and Society, 105499. https://doi.org/10.1016/j.scs.2024.105499

Sajjadian, S. M. (2024). Architecting net zero: from drawings to bytes. Journal of Building Engineering, 95, 110094. https://doi.org/10.1016/j.jobe.2024.110094

Stergiou, K., Ntakolia, C., Varytis, P., Koumoulos, E., Karlsson, P., & Moustakidis, S. (2023). Enhancing property prediction and process optimization in building materials through machine learning: A review. Computational Materials Science, 220, 112031. https://doi.org/10.1016/j.commatsci.2023.112031

Shashwat, S., Zingre, K. T., Thurairajah, N., Kumar, D. K., Panicker, K., Anand, P., & Wan, M. P. (2023). A review on bioinspired strategies for an energy-efficient built environment. Energy and Buildings, 296, 113382. https://doi.org/10.1016/j.enbuild.2023.113382

Tamburrini, Guglielmo. 2022. "The AI Carbon Footprint and Responsibilities of AI Scientists"

Philosophies 7, no. 1: 4. https://doi.org/10.3390/philosophies7010004

T. Kadar and M. Kadar, "Sustainability Is Not Enough: Towards AI Supported Regenerative Design,"

IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), Cardiff, UK, 2020, pp. 1-6, doi: 10.1109/ICE/ITMC49519.2020.9198554

Ullah, A., Anwar, S. M., Li, J., Nadeem, L., Mahmood, T., Rehman, A., & Saba, T. (2023). Smart cities: the role of Internet of Things and machine learning in realizing a data-centric smart environment. Complex & Intelligent Systems, 10(1), 1607–1637. https://doi.org/10.1007/s40747-023-01175-4

Umoh, N. a. A., Adefemi, N. A., Ibewe, N. K. I., Etukudoh, N. E. A., Ilojianya, N. V. I., & Nwokediegwu, N. Z. Q. S. (2024). GREEN ARCHITECTURE AND ENERGY EFFICIENCY: A REVIEW OF INNOVATIVE DESIGN AND CONSTRUCTION TECHNIQUES. Engineering Science & Technology Journal, 5(1), 185–200. https://doi.org/10.51594/estj.v5i1.743

Van Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI And Ethics, 1(3), 213–218. https://doi.org/10.1007/s43681-021-00043-6

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M., & Nerini, F. F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11(1). https://doi.org/10.1038/s41467-019-14108-y

Wang, H., Wang, Y., Zhao, L., Wang, W., Luo, Z., Wang, Z., Luo, J., & Lv, Y. (2024). Integrating BIM and machine learning to predict carbon emissions under foundation materialization stage: Case study of China’s 35 public buildings. Frontiers of Architectural Research, 13(4), 876–894. https://doi.org/10.1016/j.foar.2024.02.008

Xiang, Y., Chen, Y., Xu, J., & Chen, Z. (2022). Research on sustainability evaluation of green building engineering based on artificial intelligence and energy consumption. Energy Reports, 8, 11378–11391. https://doi.org/10.1016/j.egyr.2022.08.266

Yang, B., Lv, Z., & Wang, F. (2022). Digital twins for intelligent green buildings. Buildings, 12(6), 856. https://doi.org/10.3390/buildings12060856.

Zang, Z., & Ding, W. (2024). Eco-Centric Generative Design Workflow: Extending Sustainability in Architecture. In Frontiers in artificial intelligence and applications. https://doi.org/10.3233/faia240006

Zhang, C., & Lu, Y. (2021). Study on artificial intelligence: The state of the art and future prospects. Journal of Industrial Information Integration, 23, 100224. https://doi.org/10.1016/j.jii.2021.100224

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2024-10-18