{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T07:36:13Z","timestamp":1774337773015,"version":"3.50.1"},"reference-count":94,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T00:00:00Z","timestamp":1748822400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Component 5\u2014Capitalization and Business Innovation"},{"name":"Base Funding\u2014UIDB\/04708\/2020"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Urban Science"],"abstract":"<jats:p>Artificial Intelligence has recently expanded across various applications. Machine Learning, a subset of Artificial Intelligence, is a powerful technique for identifying patterns in data to support decision making and managing the increasing volume of information. Simultaneously, Digital Twins have been applied in several fields. In this context, combining Digital Twins, Machine Learning, and Smart Buildings offers significant potential to improve energy efficiency and operational effectiveness in building management. This review aims to identify and analyze studies that explore the application of Machine Learning and Digital Twins for operation and energy management in Smart Buildings, providing an updated perspective on these rapidly evolving topics. The methodology follows the PRISMA guidelines for systematic reviews, using Scopus and Web of Science databases. This review identifies the main concepts, objectives, and trends emerging from the literature. Furthermore, the findings confirm the recent growth in research combining Machine Learning and Digital Twins for building management, revealing diverse approaches, tools, methods, and challenges. Finally, this paper highlights existing research gaps and outlines opportunities for future investigation.<\/jats:p>","DOI":"10.3390\/urbansci9060202","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T05:09:12Z","timestamp":1748840952000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Integrating Machine Learning and Digital Twins for Enhanced Smart Building Operation and Energy Management: A Systematic Review"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3251-5693","authenticated-orcid":false,"given":"Bruno","family":"Palley","sequence":"first","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3792","authenticated-orcid":false,"given":"Jo\u00e3o","family":"Po\u00e7as Martins","sequence":"additional","affiliation":[{"name":"CONSTRUCT\u2014Gequaltec, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5290-6424","authenticated-orcid":false,"given":"Hermano","family":"Bernardo","sequence":"additional","affiliation":[{"name":"INESC TEC\u2014Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1566-7006","authenticated-orcid":false,"given":"Rosaldo","family":"Rossetti","sequence":"additional","affiliation":[{"name":"LIACC\u2014Artificial Intelligence and Computer Science Laboratory, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,2]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2025, May 15). World Population Prospects 2024. Available online: https:\/\/population.un.org\/wpp\/."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Odeh, K., and de Wilde, P. (2023, January 4\u20136). Exploring the Potential of Digital Twins at the District Scale: A Framework for Investigation. Proceedings of the Building Simulation Conference Proceedings, Shanghai, China.","DOI":"10.26868\/25222708.2023.1201"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"102412","DOI":"10.1016\/j.scs.2020.102412","article-title":"Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems","volume":"62","author":"Pan","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cities.2017.01.011","article-title":"A model for the analysis of data-driven innovation and value generation in smart cities\u2019 ecosystems","volume":"64","author":"Abella","year":"2017","journal-title":"Cities"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1080\/10630732.2011.601117","article-title":"Smart Cities in Europe","volume":"18","author":"Caragliu","year":"2011","journal-title":"J. Urban Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Sawhney, A., Riley, M., and Irizarry, J. (2020). Cyber threats and actors confronting the Construction 4.0, Routledge.","DOI":"10.1201\/9780429398100"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s42162-024-00313-7","article-title":"A review of building digital twins to improve energy efficiency in the building operational stage","volume":"7","author":"Jradi","year":"2024","journal-title":"Energy Inform."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hodavand, F., Ramaji, I.J., and Sadeghi, N. (2023). Digital Twin for Fault Detection and Diagnosis of Building Operations: A Systematic Review. Buildings, 13.","DOI":"10.3390\/buildings13061426"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"100135","DOI":"10.1016\/j.adapen.2023.100135","article-title":"Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies","volume":"10","author":"Pan","year":"2023","journal-title":"Adv. Appl. Energy"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Tahmasebinia, F., Lin, L., Wu, S., Kang, Y., and Sepasgozar, S. (2023). Exploring the Benefits and Limitations of Digital Twin Technology in Building Energy. Appl. Sci., 13.","DOI":"10.3390\/app13158814"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jiao, Z., Du, X., Liu, Z., Liu, L., Sun, Z., and Shi, G. (2023). Sustainable Operation and Maintenance Modeling and Application of Building Infrastructures Combined with Digital Twin Framework. Sensors, 23.","DOI":"10.3390\/s23094182"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ma, T., Xu, K., Chen, Z., Xiao, F., Ho, J., Leung, C., and Yeung, S. (2023, January 6\u20138). Smart Data-Driven Building Management Framework and Demonstration. Proceedings of the 3rd Energy-Informatics-Academy Conference (EI.A), Campinas, Brazil.","DOI":"10.1007\/978-3-031-48649-4_10"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1007\/s11192-009-0146-3","article-title":"Software survey: VOSviewer, a computer program for bibliometric mapping","volume":"84","author":"Waltman","year":"2010","journal-title":"Scientometrics"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_15","unstructured":"Scopus (2024, July 09). Analyze Search Results. Available online: www.scopus.com."},{"key":"ref_16","unstructured":"The EndNote Team (2013). EndNote 21, Clarivate."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Aruta, G., Ascione, F., Boettcher, O., De Masi, R.F., Mauro, G.M., and Vanoli, G.P. (2022, January 20\u201323). Machine learning to predict building energy performance in different climates. Proceedings of the IOP Conference Series: Earth and Environmental Science, Berlin, Germany.","DOI":"10.1088\/1755-1315\/1078\/1\/012137"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Agouzoul, A., Simeu, E., and Tabaa, M. (2023, January 10\u201312). Enhancement of Building Energy Consumption Using a Digital Twin based Neural Network Model Predictive Control. Proceedings of the 2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023, Rome, Italy.","DOI":"10.1109\/ICCAD57653.2023.10152308"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Agouzoul, A., Simeu, E., and Tabaa, M. (2024). Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency. Sustainability, 16.","DOI":"10.3390\/su16072879"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"113146","DOI":"10.1016\/j.enbuild.2023.113146","article-title":"An ontology-based innovative energy modeling framework for scalable and adaptable building digital twins","volume":"292","author":"Bjornskov","year":"2023","journal-title":"Energy Build."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113171","DOI":"10.1016\/j.enbuild.2023.113171","article-title":"Building performance simulation in the brave new world of artificial intelligence and digital twins: A systematic review","volume":"292","year":"2023","journal-title":"Energy Build."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"El-Gohary, M., El-Abed, R., and Omar, O. (2023). Prediction of an Efficient Energy-Consumption Model for Existing Residential Buildings in Lebanon Using an Artificial Neural Network as a Digital Twin in the Era of Climate Change. Buildings, 13.","DOI":"10.3390\/buildings13123074"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hosamo, H.H., Nielsen, H.K., Alnmr, A.N., Svennevig, P.R., and Svidt, K. (2022). A review of the Digital Twin technology for fault detection in buildings. Front. Built Environ., 8.","DOI":"10.3389\/fbuil.2022.1013196"},{"key":"ref_24","unstructured":"Jain, A., Nong, D., Nghiem, T.X., and Mangharam, R. (2018, January 26\u201328). Digital twins for efficient modeling and control of buildings an integrated solution with scada systems. Proceedings of the ASHRAE and IBPSA-USA Building Simulation Conference, Chicago, IL, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112067","DOI":"10.1016\/j.enbuild.2022.112067","article-title":"Simplified data-driven models for model predictive control of residential buildings","volume":"265","author":"Lee","year":"2022","journal-title":"Energy Build."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.enbuild.2016.08.014","article-title":"System identification and data fusion for on-line adaptive energy forecasting in virtual and real commercial buildings","volume":"129","author":"Li","year":"2016","journal-title":"Energy Build."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Manfren, M., Gonzalez-Carreon, K.M., and James, P.A.B. (2024). Interpretable Data-Driven Methods for Building Energy Modelling\u2014A Review of Critical Connections and Gaps. Energies, 17.","DOI":"10.3390\/en17040881"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"103667","DOI":"10.1016\/j.scs.2022.103667","article-title":"Development of a prediction model tuning method with a dual-structured optimization framework for an entire heating, ventilation and air-conditioning system","volume":"79","author":"Matsuda","year":"2022","journal-title":"Sustain. Cities Soc."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"113810","DOI":"10.1016\/j.enbuild.2023.113810","article-title":"A study of deep learning-based multi-horizon building energy forecasting","volume":"303","author":"Ni","year":"2024","journal-title":"Energy Build."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Schmitt, T., Engel, J., and Rodemann, T. (2023, January 16\u201318). Regression-Based Model Error Compensation for a Hierarchical MPC Building Energy Management System. Proceedings of the 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados.","DOI":"10.1109\/CCTA54093.2023.10252861"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111460","DOI":"10.1016\/j.enbuild.2021.111460","article-title":"Scalable Bayesian optimization for model calibration: Case study on coupled building and HVAC dynamics","volume":"253","author":"Chakrabarty","year":"2021","journal-title":"Energy Build."},{"key":"ref_32","unstructured":"Kalkhorani, V.A., and Clark, J.D. (2021, January 9\u201311). Creating an Energy Model of an Entire University Campus-Part 1: Preliminary Assessment of Building Modeling Techniques. Proceedings of the ASHRAE Virtual Winter Conference, Electr Network, Virtual."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Kotha, R., L\u00e9d\u00e9e, F., Shamsi, M.H., and Evins, R. (2023, January 25\u201329). Time-Resolved Neural Network Surrogate Models as Digital Twins. Proceedings of the 5th International Conference on Building Energy and Environment, Montreal, QC, Canada.","DOI":"10.1007\/978-981-19-9822-5_157"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Corticos, N.D., and Duarte, C.C. (2023, January 21\u201323). Artificial Inteligence Impact on Buildings Energy Efficiency. Proceedings of the Proceedings\u20142023 7th International Conference on Computer, Software and Modeling, ICCSM 2023, Paris, France.","DOI":"10.1109\/ICCSM60247.2023.00020"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114184","DOI":"10.1016\/j.rser.2023.114184","article-title":"A co-simulated material-component-system-district framework for climate-adaption and sustainability transition","volume":"192","author":"Zhou","year":"2024","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.enbenv.2023.05.004","article-title":"Digital twin technology for thermal comfort and energy efficiency in buildings: A state-of-the-art and future directions","volume":"5","author":"Arowoiya","year":"2024","journal-title":"Energy Built Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Bernal, W., Behl, M., Nghiem, T.X., and Mangharam, R. (2012, January 6). MLE+ a tool for integrated design and deployment of energy efficient building controls. Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, Toronto, ON, Canada.","DOI":"10.1145\/2422531.2422553"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s12273-024-1112-y","article-title":"Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook","volume":"17","author":"Mondal","year":"2024","journal-title":"Build. Simul."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103179","DOI":"10.1016\/j.autcon.2020.103179","article-title":"Towards a semantic Construction Digital Twin: Directions for future research","volume":"114","author":"Boje","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_40","first-page":"1","article-title":"Digital twin: Manufacturing excellence through virtual factory replication","volume":"1","author":"Grieves","year":"2014","journal-title":"White Pap."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"111110","DOI":"10.1016\/j.buildenv.2023.111110","article-title":"How occupant positioning systems can be applied to help historic residences manage energy consumption: A case study in China","volume":"249","author":"Wang","year":"2024","journal-title":"Build. Environ."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"117261","DOI":"10.1109\/ACCESS.2023.3325767","article-title":"An End-to-End Implementation of a Service-Oriented Architecture for Data-Driven Smart Buildings","volume":"11","author":"Chamari","year":"2023","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Kannari, L., Piira, K., Bistrom, H., and Vainio, T. (2022, January 16\u201318). Energy-data-related digital twin for office building and data centre complex. Proceedings of the DCIS 2022\u201437th Conference on Design of Circuits and Integrated Systems, Pamplona, Spain.","DOI":"10.1109\/DCIS55711.2022.9970040"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Brunone, F., Cucuzza, M., Imperadori, M., and Vanossi, A. (2021). From Cognitive Buildings to Digital Twin: The Frontier of Digitalization for the Management of the Built Environment. Springer Tracts in Civil Engineering, Springer.","DOI":"10.1007\/978-3-030-78136-1_5"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4614","DOI":"10.1109\/JIOT.2023.3300447","article-title":"Building a Smart Campus Digital Twin: System, Analytics, and Lessons Learned from a Real-World Project","volume":"11","author":"Cirillo","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Cao, Z., Wang, R., Zhou, X., and Wen, Y. (2022, January 9\u201310). Reducio: Model Reduction for Data Center Predictive Digital Twins via Physics-Guided Machine Learning. Proceedings of the BuildSys 2022\u20149th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Boston, MA, USA.","DOI":"10.1145\/3563357.3564050"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"128248","DOI":"10.1016\/j.energy.2023.128248","article-title":"Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system","volume":"280","author":"Liu","year":"2023","journal-title":"Energy"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Norouzi, P., Maalej, S., and Mora, R. (2023). Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems. Buildings, 13.","DOI":"10.3390\/buildings13061542"},{"key":"ref_49","first-page":"103664","article-title":"Machine learning for optimal net-zero energy consumption in smart buildings","volume":"64","author":"Zhao","year":"2024","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"108964","DOI":"10.1016\/j.buildenv.2022.108964","article-title":"Real-time prediction of indoor humidity with limited sensors using cross-sample learning","volume":"215","author":"Zhou","year":"2022","journal-title":"Build. Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"164858","DOI":"10.1016\/j.scitotenv.2023.164858","article-title":"Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges","volume":"895","author":"Dai","year":"2023","journal-title":"Sci. Total Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"113738","DOI":"10.1016\/j.enbuild.2023.113738","article-title":"Digital twin-enhanced predictive maintenance for indoor climate: A parallel LSTM-autoencoder failure prediction approach","volume":"301","author":"Hu","year":"2023","journal-title":"Energy Build."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"112992","DOI":"10.1016\/j.enbuild.2023.112992","article-title":"Improving building occupant comfort through a digital twin approach: A Bayesian network model and predictive maintenance method","volume":"288","author":"Hosamo","year":"2023","journal-title":"Energy Build."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1080\/09613218.2019.1691488","article-title":"A framework of developing machine learning models for facility life-cycle cost analysis","volume":"48","author":"Gao","year":"2020","journal-title":"Build. Res. Inf."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1108\/SASBE-07-2023-0170","article-title":"Development of an ontology-based asset information model for predictive maintenance in building facilities","volume":"14","author":"Gispert","year":"2025","journal-title":"Smart Sustain. Built Environ."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1080\/17512549.2022.2136240","article-title":"Digital Twin of HVAC system (HVACDT) for multiobjective optimization of energy consumption and thermal comfort based on BIM framework with ANN-MOGA","volume":"17","author":"Hosamo","year":"2023","journal-title":"Adv. Build. Energy Res."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"111988","DOI":"10.1016\/j.enbuild.2022.111988","article-title":"A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics","volume":"261","author":"Hosamo","year":"2022","journal-title":"Energy Build."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Wang, N., Liu, Z., and Mu, E. (2022). Construction Theory for a Building Intelligent Operation and Maintenance System Based on Digital Twins and Machine Learning. Buildings, 12.","DOI":"10.3390\/buildings12020087"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Blume, C., Blume, S., Thiede, S., and Herrmann, C. (2020). Data-driven digital twins for technical building services operation in factories: A cooling tower case study. J. Manuf. Mater. Process., 4.","DOI":"10.3390\/jmmp4040097"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1184","DOI":"10.1016\/j.renene.2022.01.044","article-title":"Data engineering for digital twining and optimization of naturally ventilated solar fa\u00e7ade with phase changing material under global projection scenarios","volume":"187","author":"Tariq","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wang, Y., Qi, Y., Li, J., Huan, L., Li, Y., Xie, B., and Wang, Y. (2023). The Wind and Photovoltaic Power Forecasting Method Based on Digital Twins. Appl. Sci., 13.","DOI":"10.3390\/app13148374"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"108387","DOI":"10.1016\/j.engappai.2024.108387","article-title":"Artificial Neural Network-based digital twin for a flat plate solar collector field","volume":"133","author":"Castilla","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Roth-Dietrich, G., and Gerten, R. (2023). Machine Learning for Energy Management Optimization. Apply Data Science: Introduction, Applications and Projects, Springer Vieweg.","DOI":"10.1007\/978-3-658-38798-3_10"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Seo, B., Yoon, Y., Lee, K.H., and Cho, S. (2023). Comparative Analysis of ANN and LSTM Prediction Accuracy and Cooling Energy Savings through AHU-DAT Control in an Office Building. Buildings, 13.","DOI":"10.3390\/buildings13061434"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Oulefki, A., Amira, A., Kurugollu, F., and Alshoweky, M. (2023, January 30\u201331). Twining Buildings: A Methodological Framework for Design and Implementation using Home Assistant Technology. Proceedings of the 4th International Conference on Electrical, Communication and Computer Engineering, ICECCE 2023, Dubai, United Arab Emirates.","DOI":"10.1109\/ICECCE61019.2023.10442609"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"012147","DOI":"10.1088\/1742-6596\/2069\/1\/012147","article-title":"Study of power demand forecasting of a hospital by ensemble machine learning","volume":"2069","author":"Nakai","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1080\/19401493.2013.765506","article-title":"Modelica Buildings library","volume":"7","author":"Wetter","year":"2014","journal-title":"J. Build. Perform. Simul."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Al-Isawi, O.A., Amirah, L.H., Al-Mufti, O.A., and Ghenai, C. (2023, January 20\u201323). Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output. Proceedings of the 2023 Advances in Science and Engineering Technology International Conferences, ASET 2023, Dubai, United Arab Emirates.","DOI":"10.1109\/ASET56582.2023.10180431"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"185059","DOI":"10.1109\/ACCESS.2020.3029943","article-title":"Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts","volume":"8","author":"Ahmed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5756","DOI":"10.1007\/s11227-023-05609-1","article-title":"An intelligent returned energy model of cell and grid using a gain sharing knowledge enhanced long short-term memory neural network","volume":"80","author":"Mohammed","year":"2024","journal-title":"J. Supercomput."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"29357","DOI":"10.1109\/ACCESS.2022.3158303","article-title":"MILP Optimized Management of Domestic PV-Battery Using Two Days-Ahead Forecasts","volume":"10","author":"Sorour","year":"2022","journal-title":"IEEE Access"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Martins, R., Musilek, P., and Hesse, H.C. (2016, January 7\u201310). Optimization of photovoltaic power self-consumption using linear programming. Proceedings of the 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy.","DOI":"10.1109\/EEEIC.2016.7555581"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1080\/10630732.2014.942092","article-title":"Smart Cities: Definitions, Dimensions, Performance, and Initiatives","volume":"22","author":"Albino","year":"2015","journal-title":"J. Urban Technol."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.cities.2015.05.004","article-title":"Smart cities: A conjuncture of four forces","volume":"47","author":"Angelidou","year":"2015","journal-title":"Cities"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.cities.2013.12.010","article-title":"Current trends in Smart City initiatives: Some stylised facts","volume":"38","author":"Neirotti","year":"2014","journal-title":"Cities"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/j.techfore.2018.07.022","article-title":"Smart innovative cities: The impact of Smart City policies on urban innovation","volume":"142","author":"Caragliu","year":"2019","journal-title":"Technol. Forecast. Soc. Change"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Fathy, Y., Jaber, M., and Nadeem, Z. (2021). Digital Twin-Driven Decision Making and Planning for Energy Consumption. J. Sens. Actuator Netw., 10.","DOI":"10.3390\/jsan10020037"},{"key":"ref_78","first-page":"391","article-title":"Digital twin framework and its application to power grid online analysis","volume":"5","author":"Zhou","year":"2019","journal-title":"CSEE J. Power Energy Syst."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Ruohom\u00e4ki, T., Airaksinen, E., Huuska, P., Kes\u00e4niemi, O., Martikka, M., and Suomisto, J. (2018, January 25\u201327). Smart City Platform Enabling Digital Twin. Proceedings of the 2018 International Conference on Intelligent Systems (IS), Funchal, Madeira, Portugal.","DOI":"10.1109\/IS.2018.8710517"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.egyr.2021.01.090","article-title":"Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review","volume":"7","author":"Onile","year":"2021","journal-title":"Energy Rep."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Niavis, H., Laskari, M., and Fergadiotou, I. (2022, January 5\u20138). Trusted DBL: A Blockchain-based Digital Twin for Sustainable and Interoperable Building Performance Evaluation. Proceedings of the 2022 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022, Split\/Bol, Croatia.","DOI":"10.23919\/SpliTech55088.2022.9854287"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.apenergy.2019.02.052","article-title":"Deep learning-based feature engineering methods for improved building energy prediction","volume":"240","author":"Fan","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/j.apenergy.2017.03.064","article-title":"A short-term building cooling load prediction method using deep learning algorithms","volume":"195","author":"Fan","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_84","unstructured":"Van Rossum, G., and Drake, F.L. (1995). Python Reference Manual, Centrum voor Wiskunde en Informatica."},{"key":"ref_85","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_86","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst., 32."},{"key":"ref_87","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv."},{"key":"ref_88","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), Savannah, GA, USA."},{"key":"ref_89","unstructured":"R Core Team (2025). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Available online: https:\/\/www.R-project.org\/."},{"key":"ref_90","unstructured":"Kalinowski, T., Falbel, D., Allaire, J.J., Chollet, F., RStudio, Google, Tang, Y., Van Der Bijl, W., Studer, M., and Keydana, S. (2025, May 26). R Package Version 2.15.0. Available online: https:\/\/CRAN.R-project.org\/package=keras."},{"key":"ref_91","unstructured":"European Parliament and Council (2025, May 26). Directive (EU) 2024\/1275 of 24 April 2024 on the Energy Performance of Buildings (Recast), Available online: https:\/\/eur-lex.europa.eu\/eli\/dir\/2024\/1275\/oj."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"1192","DOI":"10.1016\/j.rser.2017.04.095","article-title":"A review of data-driven building energy consumption prediction studies","volume":"81","author":"Amasyali","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.segan.2016.02.005","article-title":"Deep learning for estimating building energy consumption","volume":"6","author":"Mocanu","year":"2016","journal-title":"Sustain. Energy Grids Netw."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"108673","DOI":"10.1016\/j.epsr.2022.108673","article-title":"Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review","volume":"213","author":"Dash","year":"2022","journal-title":"Electr. Power Syst. 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