Journal of sustainable Energy Systems

Journal of sustainable Energy Systems

Artificial Intelligence in Low-Carbon Buildings: A Systematic Review and Integrated Framework

Document Type : Original Article

Authors
Mojtaba Masoumnezhad 1
Minoochehr Marzban 2
1 Department of Mechanical Engineering, Technical and Vocational University (TVU), Tehran, Iran
2 Department of Architecture, FSh.C., Islamic Azad University, Fouman, Iran
10.22059/ses.2026.420084.1281
Abstract
The growing adoption of artificial intelligence (AI) in buildings has created new opportunities to improve energy efficiency, intelligent control, and carbon emissions reduction. However, the existing literature has largely focused on algorithm development and predictive accuracy, while the relationship between algorithmic performance, system integration, and practical deployment in low-carbon buildings remains insufficiently addressed in a systematic manner. This study aims to identify research trends, application domains, prevailing methods, and existing research gaps, and to develop an integrated framework for the widespread application of AI in low-carbon buildings. Accordingly, 128 studies published between January 2015 and June 2025 were examined by combining systematic review, bibliometric analysis, content analysis, and critical analysis. Quantitative findings showed that energy prediction, with 42 studies (32.8%) , and optimization and control, with 36 studies (28.1%) , were the dominant application domains. Machine learning, with 48 studies (37.5%), accounted for the largest share among the identified method families. Regarding validation approaches, 47 studies (36.7%) were primarily simulation-based, while 35 studies (27.3%) relied on real building data. Qualitative findings indicated that data quality, model generalizability, system integration, implementation feasibility, security and privacy, and economic considerations were among the major challenges. The novelty of this study lies in integrating bibliometric, content-based, and critical evidence into a five-layer integrated framework linking building infrastructure, data, AI, intelligent decision-making, and performance evaluation. Furthermore, preliminary expert-based evaluation indicated the conceptual acceptability and comprehensiveness of the proposed framework; however, its effectiveness and practical implementability at scale require field validation and long-term operational studies.
Keywords
Artificial Intelligence
Building Energy Efficiency
Carbon Emissions
Intelligent Buildings
Low-Energy Buildings
Subjects
[1]. (IEA) IEA. Energy Efficiency 2025: Buildings. Paris, France; 2025.
[2]. SeyedAboutorabi RS, Yousefi H, Abdoos M. A comparative analysis of the carbon footprint in green building materials: a case study of Norway. Environmental Science and Pollution Research. 2024;31: 59320–59341.
[3]. Hernández JL, Miguel Id, Vélez F, Vasallo A. Challenges and opportunities in European smart buildings energy management: A critical review. Renewable and Sustainable Energy Reviews. 2024;199:114472.
[4]. Mathumitha R, Rathika P, Manimala K. Intelligent deep learning techniques for energy consumption forecasting in smart buildings: a review. Artificial Intelligence Review. 2024;57:35.
[5]. Cai S, Gou Z. Defining the energy role of buildings as flexumers: A review of definitions, technologies, and applications. Energy and Buildings. 2024;303:113821.
[6]. Yeom S, Kim J, Kang H, Jung S, Hong T. Digital twin (DT) and extended reality (XR) for building energy management. Energy and Buildings. 2024;323:114746.
[7]. 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.
[8]. Abbasian Hamedani E, Talebi S. Modeling and long-term forecasting of CO2 emissions in Asia: An optimized Artificial Neural Network approach with consideration of renewable energy scenarios. Energy Conversion and Management: X. 2025;26:101030.
[9]. Nadalipour Kaldeh Sh, Yousefi H, Noorollahi Y, Abdoos M. Integration of renewable sources in buildings: A review of energy savings, feasibility, and challenges. Energy Reports. 2025;14: 3905-393.
[10]. Abbasian Hamedani E, Roghani Araghi A, Hajinezhad A, Yousefi H. A scenario-based techno-economic optimization framework for power-to-X residential energy hubs across diverse climate conditions. Energy. 2026;356: 141155.
[11]. Katal A, Dahiya S, Choudhury T. Energy efficiency in cloud computing data centers: a survey on software technologies. Cluster Computing. 2023;26:1845–75.
[12]. Nayak SC, Dehuri S, Cho S-B, Favorskaya M, editors. Green Artificial Intelligence and Industrial Applications (G-AIIA)2025; Cham, Switzerland: Springer Nature Switzerland.
[13]. Tabbakh A, Amin LA, Islam M, Mahmud GMI, Chowdhury IK, Mukta MSH. Towards sustainable AI: a comprehensive framework for Green AI. Discover Sustainability. 2024;5, article number 408.
[14]. Al-Shargabi AA, Almhafdy A, Ibrahim DM, Alghieth M, Chiclana F. Buildings' energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures. Journal of Building Engineering. 2022;54:104577.
[15]. Yu J, Chang W-S, Dong Y. Building Energy Prediction Models and Related Uncertainties: A Review. Buildings 2022;12(8):1284.
[16]. Lu C, Li S, Lu Z. Building energy prediction using artificial neural networks: A literature survey. Energy and Buildings. 2022;262:111718.
[17]. Weinberg D, Wang Q, Timoudas TO, Fischione C. A Review of Reinforcement Learning for Controlling Building Energy Systems From a Computer Science Perspective. Sustainable Cities and Society. 2023;89:104351.
[18]. Wang D, Zheng W, Wang Z, Wang Y, Pang X, Wang W. Comparison of reinforcement learning and model predictive control for building energy system optimization. Applied Thermal Engineering. 2023;228:120430.
[19]. Solinas FM, Macii A, Patti E, Bottaccioli L. An online reinforcement learning approach for HVAC control. Expert Systems with Applications. 2024;238, Part A:121749.
[20]. Lin C-H, Tsay Y-S. A practical decision process for building façade performance optimization by integrating machine learning and evolutionary algorithms. Journal of Asian Architecture and Building Engineering. 2024 23(2):740-53.
[21]. Batres R, Dadras Y, Mostafazadeh F, Kavgic M. MEVO: A Metamodel-Based Evolutionary Optimizer for Building Energy Optimization. Energies. 2023;16(20):7026.
[22]. Li Z, Zhao Y, Xia H, Xie S. A multi-objective optimization framework for building performance under climate change. Journal of Building Engineering. 2023;80:107978.
[23]. Chen Z, O’Neill Z, Wen J, Pradhan O, Yang T, Lu X. A review of data-driven fault detection and diagnostics for building HVAC systems. Applied Energy. 2023;339:121030.
[24]. Zhang F, Saeed N, Sadeghian P. Deep learning in fault detection and diagnosis of building HVAC systems: A systematic review with meta analysis. Energy and AI. 2023;12:100235.
[25]. Zhang B, Rezgui Y, Luo Z, Zhao T. Fault detection research on novel transfer learning-based method for cross-condition, cross-system and cross-operation in public building HVAC sensors. Energy. 2024;313:133704.
[26]. Cespedes-Cubides AS, Jradi, M .A review of building digital twins to improve energy efficiency in the building operational stage. Energy Informatics. 2024;7(Article 11).
[27]. Boukaf M, Fadli F, Meskin N. A Comprehensive Review of Digital Twin Technology in Building Energy Consumption Forecasting. IEEE Access. 2024;12:187778-99.
[28]. Benfer R, Müller J. Semantic digital twin creation of building systems through time series based metadata inference – A review. Energy and Buildings. 2024;321:114637.
[29]. Raymand F, Najafi B, Mamaghani AH, Moazami A, Rinaldi F. Machine learning-based estimation of buildings' characteristics employing electrical and chilled water consumption data: Pipeline optimization. Energy and Buildings. 2023;295:113327.
[30]. Abramczyk J. Parametric building forms rationalizing the incident direct solar irradiation. Building and Environment. 2022;215:108963.
[31]. Soleimanijavid A, Konstantzos I, Liu X. Challenges and opportunities of occupant-centric building controls in real-world implementation: A critical review. Energy and Buildings. 2024;308:113958.
[32]. Awuzie B, Ngowi A, Aghimien D. Towards built environment Decarbonisation: A review of the role of Artificial intelligence in improving energy and Materials’ circularity performance. Energy and Buildings. 2024;319:114491.
[33]. Gachkar D, Gachkar S, Martínez AG, Aghlmand S, Ahmadi J. Artificial intelligence in building life cycle assessment. Architectural Science Review 2024; 67(6):484-502.
[34]. Yussuf RO, Asfour OS. Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview. Energy and Buildings. 2024;305:113903.
[35]. Gunasena, L. U. E. G., Alazab, A., Talukder, M. A. Artificial intelligence for energy optimization in smart buildings: A systematic review and meta-analysis. Energy Informatics. 2025;8:135.
[36]. Li, Y., Antwi-Afari, M. F., Anwer, S., Mehmood, I., et al. Artificial intelligence in net-zero carbon emissions for sustainable building projects: A systematic literature and science mapping review. Buildings. 2024; 14(9): 2752.
[37]. Zhang L, Chen, Z., Zhang, X. et al. Challenges and opportunities of machine learning control in building operations. Building Simulation. 2023;16:831–52.
[38]. Mousavi S, Villarreal-Marroquín MG, Hajiaghaei-Keshteli M, Smith NR. Data-driven prediction and optimization toward net-zero and positive-energy buildings: A systematic review. Building and Environment. 2023;242:110578.
[39]. Bakar AA, Yussof S, Ghapar AA, Sameon SS, Jørgensen BN. A Review of Privacy Concerns in Energy-Efficient Smart Buildings: Risks, Rights, and Regulations. Energies 2024;17(5):977.
[40]. Rjoub G, Bentahar J, Wahab OA, Mizouni R, Song A, Cohen R. A Survey on Explainable Artificial Intelligence for Cybersecurity. IEEE Transactions on Network and Service Management. 2023;20(4):5115-40.
[41]. Murthy A, Asghar MR, Tu W. A lightweight Intrusion Detection for Internet of Things-based smart buildings. Security and Privacy. 2024;7(4):e386.
[42]. Eck NJv, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010;84:523–38.
[43]. Donthu N, Kumar S, Mukherjee D, Pandey N, Lim WM. How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research. 2021;133:285-96.
[44]. Masoumnezhad M. Development of an Operational Framework for Smart Management and Energy Flexibility in Net-Zero Energy Buildings. Amirkabir Journal of Mechanical Engineering. 2026;57(12).
[45]. Diamond IR, Grant, R. C., Feldman, B. M., et al. . Defining consensus: A systematic review recommends methodologic criteria for reporting of Delphi studies. Journal of Clinical Epidemiology. 2014;67(4):401-9.
[46]. Okoli C, & Pawlowski, S. D. The Delphi method as a research tool: An example, design considerations and applications. Information & Management. 2004;42(1):15-29.
Journal of sustainable Energy Systems
Volume 6, Issue 1
Autumn 2027
Pages 37-63