Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment
The COVID-19 pandemic underscored the need for resilient energy management systems in smart buildings, especially during crises. This study investigates the role of Digital Twins in optimising energy systems, analysing energy use in a residential complex in Cyprus under lockdown conditions. Advanced...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | International Journal of Sustainable Energy |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/14786451.2025.2455134 |
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| author | Konstantinos Chatzikonstantinidis Nicholas Afxentiou Effrosyni Giama Paris A. Fokaides Agis M. Papadopoulos |
| author_facet | Konstantinos Chatzikonstantinidis Nicholas Afxentiou Effrosyni Giama Paris A. Fokaides Agis M. Papadopoulos |
| author_sort | Konstantinos Chatzikonstantinidis |
| collection | DOAJ |
| description | The COVID-19 pandemic underscored the need for resilient energy management systems in smart buildings, especially during crises. This study investigates the role of Digital Twins in optimising energy systems, analysing energy use in a residential complex in Cyprus under lockdown conditions. Advanced predictive models, including Skforecast, XGBoost, LightGBM, CatBoost, LSTM, and RNN, were employed to forecast energy demand. While gradient boosting models performed well, LSTM showed superior accuracy in capturing long-term patterns. These models are crucial for anticipating energy demand fluctuations, especially during unforeseen events such as the COVID-19 pandemic. The use of Digital Twins enabled real-time monitoring, proactive maintenance, and decision-making, significantly improving energy efficiency and resilience. This research underscores the importance of interdisciplinary collaboration and the integration of advanced technologies in building management. The findings advocate for a holistic, human-centric approach to energy management that prioritises adaptability, resilience, and sustainability in the face of ongoing and future challenges. |
| format | Article |
| id | doaj-art-1f51378836354807b4b5eca6895992d7 |
| institution | Kabale University |
| issn | 1478-6451 1478-646X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Sustainable Energy |
| spelling | doaj-art-1f51378836354807b4b5eca6895992d72025-02-01T03:35:59ZengTaylor & Francis GroupInternational Journal of Sustainable Energy1478-64511478-646X2025-12-0144110.1080/14786451.2025.2455134Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environmentKonstantinos Chatzikonstantinidis0Nicholas Afxentiou1Effrosyni Giama2Paris A. Fokaides3Agis M. Papadopoulos4Process Equipment Design Laboratory, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceSchool of Engineering, Frederick University, Nicosia, CyprusProcess Equipment Design Laboratory, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceSchool of Engineering, Frederick University, Nicosia, CyprusProcess Equipment Design Laboratory, Department of Mechanical Engineering, Aristotle University of Thessaloniki, Thessaloniki, GreeceThe COVID-19 pandemic underscored the need for resilient energy management systems in smart buildings, especially during crises. This study investigates the role of Digital Twins in optimising energy systems, analysing energy use in a residential complex in Cyprus under lockdown conditions. Advanced predictive models, including Skforecast, XGBoost, LightGBM, CatBoost, LSTM, and RNN, were employed to forecast energy demand. While gradient boosting models performed well, LSTM showed superior accuracy in capturing long-term patterns. These models are crucial for anticipating energy demand fluctuations, especially during unforeseen events such as the COVID-19 pandemic. The use of Digital Twins enabled real-time monitoring, proactive maintenance, and decision-making, significantly improving energy efficiency and resilience. This research underscores the importance of interdisciplinary collaboration and the integration of advanced technologies in building management. The findings advocate for a holistic, human-centric approach to energy management that prioritises adaptability, resilience, and sustainability in the face of ongoing and future challenges.https://www.tandfonline.com/doi/10.1080/14786451.2025.2455134Smart buildingsenergy managementdigital twinspredictive modelsresiliencesustainability |
| spellingShingle | Konstantinos Chatzikonstantinidis Nicholas Afxentiou Effrosyni Giama Paris A. Fokaides Agis M. Papadopoulos Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment International Journal of Sustainable Energy Smart buildings energy management digital twins predictive models resilience sustainability |
| title | Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| title_full | Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| title_fullStr | Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| title_full_unstemmed | Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| title_short | Energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| title_sort | energy management of smart buildings during crises and digital twins as an optimisation tool for sustainable urban environment |
| topic | Smart buildings energy management digital twins predictive models resilience sustainability |
| url | https://www.tandfonline.com/doi/10.1080/14786451.2025.2455134 |
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