Empowering smart cities with digital twins of buildings: Applications and implementation considerations of data-driven energy modelling in building management
Restricted accessResearch articleFirst published online July, 2024
Empowering smart cities with digital twins of buildings: Applications and implementation considerations of data-driven energy modelling in building management
Smart buildings and cities are rapidly emerging as solutions to address the challenges of efficiency, urbanisation, and sustainability in the sector. The study proposes deploying data-driven digital twins for smart buildings by utilising the available building’s technology and IT infrastructure to complement and augment existing functions. The digital twin will consist of a core data-driven energy model and a 2D visual representation of the building’s systems, with the potential for future evolution into a 3D model. This study aims to present a preliminary investigation into the idea of data-driven digital twins in building management towards enhancing the operations of smart buildings and empowering the concept of smart cities. It is demonstrated on a building on the campus of Qatar University. With an emphasis on the air conditioning systems of the building, considering their substantial contribution to overall energy consumption, the study maintains an open approach to also encompass other energy systems within the buildings, and presents a comparative evaluation between simulation-based and data-driven modelling on the case study, as well as an exploration of various machine learning algorithms that can be used. Furthermore, exploring essential smart applications of the building’s data-driven digital twin.
Practical Application
The study provides a comprehensive exploration of the practical aspects of deploying data-driven digital twins for smart buildings, addressing challenges related to data collection, model development, integration with building infrastructure, and potential limitations. The paper aims to advance the field of facility management and promote smart and sustainable practices in building operations. By contributing to the existing knowledge in facility services and management, our study offers practical guidance towards optimising building performance, reducing energy consumption, and fostering sustainable urban development.
FranciscoAMohammadiNTaylorJE. Smart city digital twin–enabled energy management: toward real-time urban building energy benchmarking. J Manag Eng2020; 36(2): 04019045.
3.
CaliUDimdBDHajialigolP, et al.Digital twins: shaping the future of energy systems and smart cities through cybersecurity, efficiency, and sustainability. In: 2023 International Conference on Future Energy Solutions (FES), Vaasa, Finland, 12-14 June 2023: IEEE, 2023, pp. 1–6.
4.
YangBLvZWangF, et al.Digital twins for intelligent green buildings. Buildings2022; 12(6): 856.
5.
PereiraPRamosNMMZhaoX, et al.Digital twins in built environments: an investigation of the characteristics, applications, and challenges. Buildings2022; 12(1): 120.
6.
KhajaviSHMotlaghNHJaribionA, et al.Digital twin: vision, benefits, boundaries, and creation for buildings. IEEE Access2019; 7: 14740.
7.
HosamoHHNielsenHKKraniotisD, et al.Digital Twin framework for automated fault source detection and prediction for comfort performance evaluation of existing non-residential Norwegian buildings. Energy Build2023; 281(2): 112732.
8.
LuQXieXParlikadAK, et al.Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance. Autom ConStruct2020; 118: 103277.
9.
ChakrabortySAdhikariS. Machine learning based digital twin for dynamical systems with multiple time-scales. Comput Struct2021; 243(1): 106410.
10.
RittoTGRochinhaFA. Digital twin, physics-based model, and machine learning applied to damage detection in structures. Mech Syst Signal Process2021; 155(6): 107614.
11.
ZhaoLZhangHWangQ, et al.Digital-twin-based evaluation of nearly zero-energy building for existing buildings based on scan-to-BIM. Adv Civ Eng2021: 2021: 6638897.
12.
LuQXieXParlikadAK, et al.Moving from building information models to digital twins for operation and maintenance. Proc Inst Civ Eng: Smart Infrastructure and Construction2020; 174(2): 46–56.
13.
CoupryCNoblecourtSRichardP, et al.BIM-based digital twin and XR devices to improve maintenance procedures in smart buildings: a literature review. Appl Sci2021; 11(7): 6810.
14.
TengSYToušMLeongWD, et al.Recent advances on industrial data-driven energy savings: digital twins and infrastructures. Renew Sustain Energy Rev2021; 135(1): 110208.
15.
LiHYangDCaoH, et al.Data-driven hybrid petri-net based energy consumption behaviour modelling for digital twin of energy-efficient manufacturing system. Energy2022; 239(1): 122178.
16.
WangXWangYTaoF, et al.New paradigm of data-driven smart customisation through digital twin. J Manuf Syst2021; 58(1): 270–280.
17.
BlumeCBlumeSThiedeS, et al.Data-driven digital twins for technical building services operation in factories: a cooling tower case study. Journal of Manufacturing and Materials Processing2020; 4(9): 97.
18.
VishnuVSVargheseKGGurumoorthyB. A data-driven digital twin framework for key performance indicators in CNC machining processes. Int J Comput Integrated Manuf2023; 36(1).
19.
GattulliVFranchiFGraziosiF, et al.Design and evaluation of 5G-based architecture supporting data-driven digital twins updating and matching in seismic monitoring. Bull Earthq Eng2022; 20(7): 4345–4365.
20.
LuoWHuTYeY, et al.A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin. Robot Comput Integrated Manuf2020; 65: 101974.
21.
HongTWangZLuoX, et al.State-of-the-art on research and applications of machine learning in the building life cycle. Energy Build2020; 212: 109831.
22.
GSAKZhangTArdakanianO, et al.Mitigating an adoption barrier of reinforcement learning-based control strategies in buildings. Energy Build2023; 285: 112878.
23.
HimeurYElnourMFadliF, et al.AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives. Artif Intell Rev2022; 56(10): 1–93.
24.
WangZLiuJZhangY, et al.Practical issues in implementing machine-learning models for building energy efficiency: moving beyond obstacles. Renew Sustain Energy Rev2021; 143: 110929.
25.
SolaACorcheroCSalomJ, et al.Simulation tools to build urban-scale energy models: a review. Energies2018; 11(12): 3269.
26.
ManandharPRafiqHRodriguez-UbinasE. Current status, challenges, and prospects of data-driven urban energy modeling: a review of machine learning methods. Energy Rep2023; 9: 2757–2776.
27.
DebCSchlueterA. Review of data-driven energy modelling techniques for building retrofit. Renew Sustain Energy Rev2021; 144: 110990.
28.
AmasyaliKEl-GoharyNM. A review of data-driven building energy consumption prediction studies. Renew Sustain Energy Rev2018; 81: 1192–1205.
29.
ManfrenMJamesPATronchinL. Data-driven building energy modelling–An analysis of the potential for generalisation through interpretable machine learning. Renew Sustain Energy Rev2022; 167: 112686.
30.
SunYHaghighatFFungBC. A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy Build2020; 221: 110022.
31.
DivinaFGarcia TorresMGoméz VelaFA, et al.A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings. Energies2019; 12(10): 1934.
32.
FanCYanDXiaoF, et al.Advanced data analytics for enhancing building performances: from data-driven to big data-driven approaches. In Build Simulat. Springer2021; 14: pp. 3–24.
33.
DingYLiuX. A comparative analysis of data-driven methods in building energy benchmarking. Energy Build2020; 209: 109711.
34.
StamatescuGStamatescuIArghiraN, et al.Data-driven modelling of smart building ventilation subsystem. J Sens2019; 2019.
PaleyesAUrmaRGLawrenceND. Challenges in deploying machine learning: a survey of case studies. ACM Comput Surv2022; 55(6): 1–29.
40.
AmershiSBegelABirdC, et al.Software engineering for machine learning: a case study. In: 2019 IEEE/ACM 41st international conference on software engineering: software engineering in practice (ICSE-SEIP), Montreal Canada, 25-31 May 2019: IEEE, 2019. p. 291–300.
41.
GargSPundirPRatheeG, et al.On continuous integration/continuous delivery for automated deployment of machine learning models using mlops. In: 2021 IEEE fourth international conference on artificial intelligence and knowledge engineering (AIKE), CA, USA, 1-3 Dec. 2021: IEEE, 2021. p. 25.
42.
LwakatareLECrnkovicIRångeE, et al.From a data science driven process to a continuous delivery process for machine learning systems. In: Proceedings 21 Product-Focused Software Process Improvement: 21st International Conference, PROFES 2020, Turin, Italy, November 25–27, 2020: Springer, 2020. p. 185–201.
43.
ShahbaziZByunYC. Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing. Sensors2021; 21(4): 1467.
44.
WuestTWeimerDIrgensC, et al.Machine learning in manufacturing: advantages, challenges, and applications. Production & Manufacturing Research2016; 4(1): 23–45.
45.
LengJWangDShenW, et al.Digital twins-based smart manufacturing system design in Industry 4.0: a review. J Manuf Syst2021; 60: 119–137.
46.
XiaKSaccoCKirkpatrickM, et al.A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J Manuf Syst2021; 58: 210–230.
47.
GenkinMMcArthurJSMARTB-. A reference architecture for artificially intelligent autonomic smart buildings. Eng Appl Artif Intell2023; 121: 106063.
48.
AbioyeSOOyedeleLOAkanbiL, et al.Artificial intelligence in the construction industry: a review of present status, opportunities and future challenges. J Build Eng2021; 44: 103299.
49.
BaierLJöhrenFSeebacherS. Challenges in the deployment and operation of machine learning in practice. ECIS2019; 1.
50.
ElnourMHimeurYFadliF, et al.Neural network-based model predictive control system for optimizing building automation and management systems of sports facilities. Appl Energy2022; 318: 119153.
51.
ElnourMFadliFMeskinN, et al.Analysis of unsupervised consumption anomaly detection in sports facilities using artificial intelligence-based data analytics: a case study. In: 2023 15th International Conference on Machine Learning and Computing (ICMLC). ICMLC 2023, Zhuhai China, February 17–20, 2023: Association for Computing Machinery, 2023.
52.
ElnourMMeskinNAl-NaemiM. Sensor data validation and fault diagnosis using Auto-Associative Neural Network for HVAC systems. J Build Eng2020; 27: 100935.
53.
JollyK. Machine learning with scikit-learn quick start guide: classification, regression, and clustering techniques in Python. Birmingham, UK: Packt Publishing Ltd, 2018.
54.
BonaccorsoG. Machine Learning Algorithms: popular algorithms for data science and machine learning. Birmingham, UK: Packt Publishing Ltd, 2018.
55.
PedregosaFVaroquauxGGramfortA, et al.Scikit-learn: machine learning in Python. J Mach Learn Res2011; 12: 2825–2830.
56.
HastieTTibshiraniRFriedmanJH, et al.The elements of statistical learning: data mining, inference, and prediction. Berlin, Germany: Springer, 2009.
57.
WadeCGlynnK. Hands-On Gradient Boosting with XGBoost and scikit-learn: perform accessible machine learning and extreme gradient boosting with Python. Birmingham, UK: Packt Publishing Ltd, 2020.
58.
HimeurYGhanemKAlsalemiA, et al.Artificial intelligence based anomaly detection of energy consumption in buildings: a review, current trends and new perspectives. Appl Energy2021; 287: 116601.
59.
ElnourMMeskinNKhanK, et al.Application of data-driven attack detection framework for secure operation in smart buildings. Sustain Cities Soc2021; 69: 102816.
60.
ZhaoYWenJXiaoF, et al.Diagnostic Bayesian networks for diagnosing air handling units faults–part I: faults in dampers, fans, filters and sensors. Appl Therm Eng2017; 111: 1272–1286.
61.
OliveiraDFNVismariLFNascimentoAM, et al.A new interpretable unsupervised anomaly detection method based on residual explanation. IEEE Access2022; 10: 1401–1409.
62.
JungD. Residual generation using physically-based grey-box recurrent neural networks for engine fault diagnosis2020. arXiv preprint arXiv:200804644.
63.
JlassiICardosoAJM. A single method for multiple IGBT, current, and speed sensor faults diagnosis in regenerative PMSM drives. IEEE Journal of Emerging and Selected Topics in Power Electronics2019; 8(3): 2583–2599.
64.
OhSYoonJChoiY, et al.Genetic algorithm for the optimization of a building power consumption prediction model. Electronics2022; 11(21): 3591.
ElnourMFadliFHimeurY, et al.Performance and energy optimization of building automation and management systems: towards smart sustainable carbon-neutral sports facilities. Renew Sustain Energy Rev2022; 162: 112401.
67.
SolimanKNajiKGunduzM, et al.BIM-based facility management models for existing buildings. Journal of Engineering Research2022; 10(1A): 21–37.
68.
DongBO’NeillZLiZ. A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics. Autom ConStruct2014; 44: 197–211.
