EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 312 (2021) E3S Web Conf., 312 (2021) 09002 References
Open Access
Issue
E3S Web Conf.
Volume 312, 2021
76th Italian National Congress ATI (ATI 2021)
Article Number 09002
Number of page(s) 18
Section Smart Energy Systems
DOI https://doi.org/10.1051/e3sconf/202131209002
Published online 22 October 2021
  1. N. Cetorelli and M. Gambera, “Banking Market Structure, Financial Dependence and Growth: International Evidence from Industry Data,” J. Finance, vol. 56, no. 2, pp. 617–648, Apr. (2001), doi: 10.1111/0022-1082.00339. [CrossRef] [Google Scholar]
  2. D.L. Kay, J.E. Pratt, and M.E. Warner, “Role of Services in Regional Economy Growth,” Growth Change, vol. 38, no. 3, pp. 419–442, Sep. (2007), doi: 10.1111/j.1468-2257.2007.00377.x. [CrossRef] [Google Scholar]
  3. P.A. Stone, “Building economy. Third edition,” Jan. (1983), Accessed: Jun. 10, 2021. Online. Available: https://www.osti.gov/biblio/6025338 [Google Scholar]
  4. A. Aghazadeh Ardebili and E. Padoano, “Change assessment aims to transition to green service-case study of quantitative Risky-Opportunity analysis in a PSP Company,” in Second International Conference on Sustainable Production and Consumption: Explore interactions between technology, the environment, economy, society and policy, (2020) [Google Scholar]
  5. T.M. Hoag and C.F. Lemme, “Animal-Derived Food Industry: Risks And Opportunities Due To Farm Animal Welfare,” Rev. Adm. Empres., vol. 58, pp. 244253, Jun. (2018) doi: 10.1590/S0034-759020180305. [Google Scholar]
  6. R. Janssen and D.D. Rutz, “Sustainability of biofuels in Latin America: Risks and opportunities,” Energy Policy, vol. 39, no. 10, pp. 5717–5725, Oct. (2011), doi: 10.1016/j.enpol.2011.01.047. [CrossRef] [Google Scholar]
  7. V. der Zee and L. Francois, “Modelling of electricity cost risks and opportunities in the gold mining industry,” Thesis, (2014). Accessed: Jun. 10, 2021. Online. Available: https://repository.nwu.ac.za/handle/10394/12267 [Google Scholar]
  8. T. Bakke, J. Klungsoyr, and S. Sanni, “Environmental impacts of produced water and drilling waste discharges from the Norwegian offshore petroleum industry,” Mar. Environ. Res., vol. 92, pp. 154–169, Dec. (2013), doi: 10.1016/j.marenvres.13.09.012. [CrossRef] [Google Scholar]
  9. R. Flin, K. Mearns, M. Fleming, and R. Gordon, “Risk perception and safety in the offshore oil and gas industry,” Nov. (1996), Accessed: Jun. 10, 2021. Online. Available: https://www.osti.gov/etdeweb/biblio/383507 [Google Scholar]
  10. A. Macintosh, K. Dafforn, B. Penrose, A. Chariton, and T. Cresswell, “Ecotoxicological effects of decommissioning offshore petroleum infrastructure: A systematic review,” Crit. Rev. Environ. Sci. Technol., vol. 0, no. 0, pp. 1–39, May (2021), doi: 10.1080/10643389.2021.1917949. [CrossRef] [Google Scholar]
  11. O.J. Tveit, B.J. Myklatun, T.J. Bohler, and O.J. Vesterhaug, “Risk Analysis Of Atypical North Sea Petroleum Production Platform,” in All Days, Houston, Texas, May (1980), p. OTC-3905-MS. doi: 10.4043/3905-MS. [Google Scholar]
  12. D.A. Hensher and K.J. Button, Eds., Handbook of Transport and the Environment, vol. 4. Emerald Group Publishing Limited, (2003). doi: 10.1108/9781786359513. [Google Scholar]
  13. T.R. Lakshmanan and X. Han, “Factors underlying transportation CO2 emissions in the U.S.A.: A decomposition analysis,” Transp. Res. Part Transp. Environ., vol. 2, no. 1, pp. 1–15, Mar. (1997), doi: 10.1016/S1361-9209(96)00011-9. [CrossRef] [Google Scholar]
  14. Y.F. Zhang, X.Z. Gong, Z.H. Wang, and Y. Liu, “Environment Impact of Freight Transportation in China,” Mater. Sci. Forum, vol. 787, pp. 144–151, (2014), doi: 10.4028/www.scientific.net/MSF.787.144. [CrossRef] [Google Scholar]
  15. T.V. Nguyen, P. Petit, F. Maufay, M. Aillerie, and J.-P. Charles, “Powerline Communication (PLC) on HVDC Bus in a Renewable Energy System,” Energy Procedia, vol. 36, pp. 657–666, Jan. (2013), doi: 10.1016/j.egypro.2013.07.076. [CrossRef] [Google Scholar]
  16. H. Guozhen, C. tao, C. Changsong, and D. Shanxu, “Solutions for SCADA system communication reliability in photovoltaic power plants,” in 2009 IEEE 6th International Power Electronics and Motion Control Conference, May (2009), pp. 2482–2485. doi: 10.1109/IPEMC.2009.5157821. [CrossRef] [Google Scholar]
  17. J. Augutis, B. Joksas, R. Krikstolaitis, and R. Urbonas, “The assessment technology of energy critical infrastructure,” Appl. Energy, vol. 162, pp. 1494–1504, Jan. (2016), doi: 10.1016/j.apenergy.2015.02.079. [CrossRef] [Google Scholar]
  18. A. Aghazadeh Ardebili and E. Padoano, “A Literature Review of the Concepts of Resilience and Sustainability in Group Decision-Making,” Sustainability, vol. 12, no. 7, p. 2602, Mar. (2020), doi: 10.3390/su12072602. [CrossRef] [Google Scholar]
  19. D. Ivanov, “New Drivers for Supply Chain Structural Dynamics and Resilience: Sustainability, Industry 4.0, Self-Adaptation,” in Structural Dynamics and Resilience in Supply Chain Risk Management, D. Ivanov, Ed. Cham: Springer International Publishing, (2018), pp. 293–313. doi: 10.1007/978-3-319-69305-7_10. [CrossRef] [Google Scholar]
  20. M.S. Mubarik et al., “Resilience and cleaner production in industry 4.0: Role of supply chain mapping and visibility,” J. Clean. Prod., vol. 292, p. 126058, Apr. (2021), doi: 10.1016/jjclepro.2021.126058. [CrossRef] [Google Scholar]
  21. G. Marcucci, S. Antomarioni, F.E. Ciarapica, and M. Bevilacqua, “The impact of Operations and IT-related Industry 4.0 key technologies on organizational resilience,” Prod. Plan. Control, pp. 1–15, Jan. (2021), doi: 10.1080/09537287.2021.1874702. [CrossRef] [Google Scholar]
  22. P. Ralston and J. Blackhurst, “Industry 4.0 and resilience in the supply chain: a driver of capability enhancement or capability loss?,” Int. J. Prod. Res., vol. 58, no. 16, pp. 5006–5019, Aug. (2020), doi: 10.1080/00207543.2020.1736724. [CrossRef] [Google Scholar]
  23. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (loT): A vision, architectural elements, and future directions,” Future Gener. Comput. Syst., vol. 29, no. 7, pp. 1645–1660, Sep. (2013), doi: 10.1016/j.future.2013.01.010. [CrossRef] [Google Scholar]
  24. S. Krco, B. Pokric, and F. Carrez, “Designing IoT architecture(s): A European perspective,” in 2014 IEEE World Forum on Internet of Things (WF-IoT), Mar. (2014), pp. 79–84. doi: 10.1109/WF-IoT.2014.6803124. [CrossRef] [Google Scholar]
  25. R. van Kranenburg and A. Bassi, “IoT Challenges,” Commun. Mob. Comput., vol. 1, no. 1, p. 9, Nov. (2012), doi: 10.1186/2192-1121-1-9. [CrossRef] [Google Scholar]
  26. S.O. Muhanji, A.E. Flint, and A.M. Farid, eIoT: The Development of the Energy Internet of Things in Energy Infrastructure. Springer Nature, (2019). Accessed: Jun. 10, (2021). Online. Available: https://library.oapen.org/handle/20.500.12657/22898 [Google Scholar]
  27. S.O. Muhanji, A.E. Flint, and A.M. Farid, “eIoT as a Solution to EnergyManagement Change Drivers,” in eIoT: The Development of the Energy Internet of Things in Energy Infrastructure, S.O. Muhanji, A.E. Flint, and A.M. Farid, Eds. Cham: Springer International Publishing, (2019), pp. 1–15. doi: 10.1007/978-3-030-10427-6_1. [Google Scholar]
  28. S.O. Muhanji, A.E. Flint, and A.M. Farid, “Transactive Energy Applications of eIoT,” in eIoT: The Development of the Energy Internet of Things in Energy Infrastructure, S.O. Muhanji, A.E. Flint, and A.M. Farid, Eds. Cham: Springer International Publishing, (2019), pp. 91–113. doi: 10.1007/978-3-030-10427-6_4. [CrossRef] [Google Scholar]
  29. C. Semeraro, M. Lezoche, H. Panetto, and M. Dassisti, “Digital twin paradigm: A systematic literature review,” Comput. Ind., vol. 130, p. 103469, Sep. (2021), doi: 10.1016/j.compind.2021.103469. [CrossRef] [Google Scholar]
  30. E. Glaessgen and D. Stargel, “The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles,” in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.2012-1818. [Google Scholar]
  31. The Digital Twin Paradigm for Smarter Systems and Environments: The Industry Use Cases. Academic Press, (2020). [Google Scholar]
  32. A. Lamba, “Protecting ‘Cybersecurity & Resiliency’ of Nation’s Critical Infrastructure - Energy, Oil & Gas,” SSRN Electron. J., (2018), doi: 10.2139/ssrn.3535434. [Google Scholar]
  33. J.L. Sawin, “Fossil Fuel Use Up,” in Vital Signs 2003-2004, Routledge, (2003). [Google Scholar]
  34. K. Vohra, A. Vodonos, J. Schwartz, E.A. Marais, M.P. Sulprizio, and L.J. Mickley, “Global mortality from outdoor fine particle pollution generated by fossil fuel combustion: Results from GEOS-Chem,” Environ. Res., vol. 195, p. 110754, Apr. (2021), doi: 10.1016/j.envres.2021.110754. [CrossRef] [Google Scholar]
  35. Y. Yang, H.S. Solgaard, and W. Haider, “Wind, hydro or mixed renewable energy source: Preference for electricity products when the share of renewable energy increases,” Energy Policy, vol. 97, pp. 521–531, Oct. (2016), doi: 10.1016/j.enpol.2016.07.030. [CrossRef] [Google Scholar]
  36. J.D. Moss, “Solar Panels, Tax Incentives, and Your House,” Probate Prop., vol. 24, p. 17, (2010). [Google Scholar]
  37. M. Cheng, S.S. Sami, and J. Wu, “Benefits of using virtual energy storage system for power system frequency response,” Appl. Energy, vol. 194, pp. 376–385, May (2017), doi: 10.1016/j.apenergy.2016.06.113. [CrossRef] [Google Scholar]
  38. M. Cheng, S.S. Sami, and J. Wu, “Virtual Energy Storage System for Smart Grids,” Energy Procedia, vol. 88, pp. 436–442, Jun. (2016), doi: 10.1016/j.egypro.2016.06.021. [CrossRef] [Google Scholar]
  39. M.T. Iqbal, “Modeling and control of a wind fuel cell hybrid energy system,” Renew. Energy, vol. 28, no. 2, pp. 223–237, Feb.(2003), doi: 10.1016/S0960-1481(02)00016-2. [CrossRef] [Google Scholar]
  40. J.J. Ding and J.S. Buckeridge, “Design Considerations for a Sustainable Hybrid Energy System,” Trans. Inst. Prof. Eng. N.Z. Civ. Eng. Sect., Nov. (2000), Accessed: Jun. 11, 2021. Online. Available: https://search.informit.org/doi/abs/10.3316/INFORMIT.403268692365109 [Google Scholar]
  41. C.V. Nayar, S.J. Phillips, W.L. James, T.L. Pryor, and D. Remmer, “Novel wind/diesel/battery hybrid energy system,” Sol. Energy, vol. 51, no. 1, pp. 65–78, Jul. (1993), doi: 10.1016/0038-092X(93)90043-N. [CrossRef] [Google Scholar]
  42. I. COLAK, R. BAYINDIR, and S. SAGIROGLU, “The Effects of the Smart Grid System on the National Grids,” in 2020 8th International Conference on Smart Grid (icSmartGrid), Jun. (2020), pp. 122–126. doi: 10.1109/icSmartGrid49881.2020.9144891. [CrossRef] [Google Scholar]
  43. L.R. Gorjâo et al., “Data-Driven Model of the Power-Grid Frequency Dynamics,” IEEE Access, vol. 8, pp. 43082–43097, (2020), doi: 10.1109/ACCESS.2020.2967834. [CrossRef] [Google Scholar]
  44. L.A. Kumar and S. Karthikeyan, “Modeling of Phasor Measurement Unit for Wide Area Monitoring and control of Smart Grids with Distributed Energy Resources,” in 2016 IEEE Conference on Technologies for Sustainability (SusTech), Oct. (2016), pp. 188–194. doi: 10.1109/SusTech.2016.7897165. [CrossRef] [Google Scholar]
  45. A.K. Verma, B. Singh, D.T. Shahani, and C. Jain, “Grid-interfaced Solar Photovoltaic Smart Building with Bidirectional Power Flow Between Grid and Electric Vehicle with Improved Power Quality,” Electr. Power Compon. Syst., vol. 44, no. 5, pp. 480–494, Mar. (2016), doi: 10.1080/15325008.2015.1120818. [CrossRef] [Google Scholar]
  46. M.K. Rafique et al., “An Intelligent Hybrid Energy Management System for a Smart House Considering Bidirectional Power Flow and Various EV Charging Techniques,” Appl. Sci., vol. 9, no. 8, Art. no. 8, Jan. (2019), doi: 10.3390/app9081658. [CrossRef] [Google Scholar]
  47. S. Saponara, R. Saletti, and L. Mihet-Popa, “Hybrid Micro-Grids Exploiting Renewables Sources, Battery Energy Storages, and Bi-Directional Converters,” Appl. Sci., vol. 9, no. 22, Art. no. 22, Jan. (2019), doi: 10.3390/app9224973. [CrossRef] [Google Scholar]
  48. K. Ashenayi and R. Ramakumar, “IRES—A program to design integrated renewable energy systems,” Energy, vol. 15, no. 12, pp. 1143–1152, Dec. (1990), doi: 10.1016/0360-5442(90)90105-B. [CrossRef] [Google Scholar]
  49. H. Zsiboracs et al., “Intermittent Renewable Energy Sources: The Role of Energy Storage in the European Power System of 2040,” Electronics, vol. 8, no. 7, Art. no. 7, Jul. (2019), doi: 10.3390/electronics8070729. [Google Scholar]
  50. J.P. Barton and D.G. Infield, “Energy storage and its use with intermittent renewable energy,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 441–448, Jun. (2004), doi: 10.1109/TEC.2003.822305. [CrossRef] [Google Scholar]
  51. J. Joe, J. Dong, J. Munk, T. Kuruganti, and B. Cui, “Virtual storage capability of residential buildings for sustainable smart city via model-based predictive control,” Sustain. Cities Soc., vol. 64, p. 102491, Jan. (2021), doi: 10.1016/j.scs.2020.102491. [CrossRef] [Google Scholar]
  52. P. Palensky and D. Bruckner, “Anticipative virtual storage power plants,” in 2009 35th Annual Conference of IEEE Industrial Electronics, Nov. (2009), pp. 3571–3574. doi: 10.1109/IECON.2009.5415158. [CrossRef] [Google Scholar]
  53. R.-C. Dai, B. Zhao, X.-D. Zhang, J.-W. Yu, B. Fan, and B. Liu, “Joint Virtual Energy Storage Modeling with Electric Vehicle Participation in Energy Local Area Smart Grid,” Complexity, vol. 2020, p. e3102729, Oct. (2020), doi: 10.1155/2020/3102729. [Google Scholar]
  54. E. Oh, “Reinforcement-Learning-Based Virtual Energy Storage System Operation Strategy for Wind Power Forecast Uncertainty Management,” Appl. Sci., vol. 10, no. 18, Art. no. 18, Jan. (2020), doi: 10.3390/app10186420. [Google Scholar]
  55. H. Ning and H. Liu, “Cyber-physical-social-thinking space based science and technology framework for the Internet of Things,” Sci. China Inf. Sci., vol. 58, no. 3, pp. 1–19, Mar. (2015), doi: 10.1007/s11432-014-5209-2. [CrossRef] [Google Scholar]
  56. R.K. Lenka et al., “Building Scalable Cyber-Physical-Social Networking Infrastructure Using IoT and Low Power Sensors,” IEEE Access, vol. 6, pp. 30162–30173, (2018), doi: 10.1109/ACCESS.2018.2842760. [CrossRef] [Google Scholar]
  57. H. Ning and H. Liu, “Cyber-Physical-Social Based Security Architecture for Future Internet of Things,” Adv. Internet Things, vol. 02, no. 01, Art. no. 01, Jan. (2012), doi: 10.4236/ait.2012.21001. [CrossRef] [Google Scholar]
  58. H. Ning, H. Liu, J. Ma, L.T. Yang, and R. Huang, “Cybermatics: Cyber-physicalsocial-thinking hyperspace based science and technology,” Future Gener. Comput. Syst., vol. 56, pp. 504–522, Mar. (2016), doi: 10.1016/jfuture.2015.07.012. [CrossRef] [Google Scholar]
  59. H. Muccini and M.T. Moghaddam, “IoT Architectural Styles,” in Software Architecture, Cham, (2018), pp. 68–85. doi: 10.1007/978-3-030-00761-4_5. [CrossRef] [Google Scholar]
  60. A.A. Zaidan et al., “A survey on communication components for IoT-based technologies in smart homes,” Telecommun. Syst., vol. 69, no. 1, pp. 1–25, Sep. (2018), doi: 10.1007/s11235-018-0430-8. [CrossRef] [Google Scholar]
  61. M.T. Lazarescu, “Wireless Sensor Networks for the Internet of Things: Barriers and Synergies,” in Components and Services for IoT Platforms: Paving the Way for IoT Standards, G. Keramidas, N. Voros, and M. Hübner, Eds. Cham: Springer International Publishing, (2017), pp. 155–186. doi: 10.1007/978-3-319-42304-3_9. [CrossRef] [Google Scholar]
  62. G. Hafeez et al., “Efficient Energy Management of IoT-Enabled Smart Homes Under Price-Based Demand Response Program in Smart Grid,” Sensors, vol. 1, no. 11, Art. no. 11, Jan. (2020), doi: 10.3390/s20113155. [Google Scholar]
  63. L. Barbierato et al., “A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids,” IEEE Internet Things J., vol. 1, no. 1, pp. 11361146, Feb. (2019), doi: 10.1109/JIOT.2018.2867511. [Google Scholar]
  64. C. Pop, T. Cioara, M. Antal, I. Anghel, I. Salomie, and M. Bertoncini, “Blockchain Based Decentralized Management of Demand Response Programs in Smart Energy Grids,” Sensors, vol. 18, no. 1, Art. no. 1, Jan. (2018), doi: 10.3390/s18010162. [Google Scholar]
  65. J.W. Heron, J. Jiang, H. Sun, V. Gezerlis, and T. Doukoglou, “Demand-Response Round-Trip Latency of IoT SmartGrid Network Topologies,” IEEE Access, vol. 6, pp. 22930–22937, (2018), doi: 10.1109/ACCESS.2018.2831254. [CrossRef] [Google Scholar]
  66. A.K. Sultania, F. Mahfoudhi, and J. Famaey, “Real-Time Demand Response Using NB-IoT,” IEEE Internet Things J., vol. 7, no. 12, pp. 11863–11872, Dec. (2020), doi: 10.1109/JIOT.2020.30043 90. [CrossRef] [Google Scholar]
  67. P. Zhou et al., “Research on Mining of Transmission Grid Assets of Heterogeneous System Based on Digital Twin,” in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Wuhan, China, Oct. (2020), pp. 3051–3056. doi: 10.1109/EI250167.2020.9346945. [CrossRef] [Google Scholar]
  68. E. O’Dwyer, I. Pan, R. Charlesworth, S. Butler, and N. Shah, “Integration of an energy management tool and digital twin for coordination and control of multi-vector smart energy systems,” Sustain. Cities Soc., vol. 62, p. 102412, Oct. (2020), doi: 10.1016/j.scs.2020.102412. [CrossRef] [Google Scholar]
  69. W. Tushar et al., “Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence,” IEEE Signal Process. Mag., vol. 35, no. 5, pp. 100–110, Sep. (2018), doi: 10.1109/MSP.2018.2842096. [CrossRef] [Google Scholar]
  70. K.G. Dangi and S.P. Panda, “Challenges in Wireless Body Area Network-A survey,” in 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), Feb. (2014), pp. 204–207. doi: 10.1109/ICROIT.2014.6798337. [CrossRef] [Google Scholar]
  71. S.M.A. Group et al., “Internet of Things (IoT): A Literature Review,” J. Comput. Commun., vol. 03, no. 05, Art. no. 05, (2015), doi: 10.4236/jcc.2015.35021. [Google Scholar]
  72. A. Colakovic and M. Hadzialic, “Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues,” Comput. Netw., vol. 144, pp. 1739, Oct. (2018), doi: 10.1016/j.comnet.2018.07.017. [CrossRef] [Google Scholar]
  73. A. Rasheed, O. San, and T. Kvamsdal, “Digital Twin: Values, Challenges and Enablers From a Modeling Perspective,” IEEE Access, vol. 8, pp. 21980–22012, (2020), doi: 10.1109/ACCESS.2020.2970143. [CrossRef] [Google Scholar]
  74. M. Zhou, J. Yan, and D. Feng, “Digital twin framework and its application to power grid online analysis,” CSEE J. Power Energy Syst., vol. 5, no. 3, pp. 391–398, Sep. (2019), doi: 10.17775/CSEEJPES.2018.01460. [Google Scholar]
  75. Y.-L. Huang and W.-L. Sun, “An AHP-Based Risk Assessment for an Industrial IoT Cloud,” in 2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C), Jul. (2018), pp. 637–638. doi: 10.1109/QRS-C.2018.00112. [CrossRef] [Google Scholar]
  76. P. Ganguly, “Selecting the right IoT cloud platform,” in 2016 International Conference on Internet of Things and Applications (IOTA), Jan. (2016), pp. 316–320. doi: 10.1109/IOTA.2016.7562744. [CrossRef] [Google Scholar]
  77. V. Malik and S. Singh, “Cloud, Big Data IoT: Risk Management,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Feb. (2019), pp. 258–262. doi: 10.1109/COMITCon.2019.8862445. [Google Scholar]
  78. A. L’Heureux, K. Grolinger, H.F. Elyamany, and M.A.M. Capretz, “Machine Learning With Big Data: Challenges and Approaches,” IEEE Access, vol. 5, pp. 77767797, (2017), doi: 10.1109/ACCESS.2017.2696365. [Google Scholar]
  79. A. Blum and K. Stangl, “Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?,” ArXiv191201094 Cs Stat, Dec. (2019), Accessed: Jun. 11, 2021. Online. Available: http://arxiv.org/abs/1912.01094 [Google Scholar]
  80. W. Blanzeisky and P. Cunningham, “Algorithmic Factors Influencing Bias in Machine Learning,” ArXiv210414014 Cs Stat, Apr. (2021), Accessed: Jun. 11, 2021. Online. Available: http://arxiv.org/abs/2104.14014 [Google Scholar]
  81. J. Gu and D. Oelke, “Understanding Bias in Machine Learning,” ArXiv190901866 Cs Stat, Sep. (2019), Accessed: Jun. 11, 2021. Online. Available: http://arxiv.org/abs/1909.01866 [Google Scholar]
  82. M. Grieves, “Digital twin: manufacturing excellence through virtual factory replication,” White Pap., vol. 1, pp. 1–7, (2014). [Google Scholar]
  83. W. Michael, “Grieves Digital Twin: Manufacturing Excellence through Virtual Factory Replication-LLC,” (2014). [Google Scholar]
  84. F. Tao and M. Zhang, “Digital Twin Shop-Floor: A New Shop-Floor Paradigm Towards Smart Manufacturing,” IEEE Access, vol. 5, pp. 20418–20427, (2017), doi: 10.1109/ACCESS.2017.2756069. [CrossRef] [Google Scholar]
  85. Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, (2018), doi: 10.1109/ACCESS.2018.2793265. [CrossRef] [Google Scholar]
  86. E.J. Tuegel, A.R. Ingraffea, T.G. Eason, and S.M. Spottswood, “Reengineering Aircraft Structural Life Prediction Using a Digital Twin,” Int. J. Aerosp. Eng., vol. 2011, p. e154798, Oct. (2011), doi: 10.1155/2011/154798. [CrossRef] [Google Scholar]
  87. S. Moghadaszadeh Bazaz, “Digital twin and its application on plywood production line,” (2019), Accessed: Jun. 13, 2021. Online. Available: https://lutpub.lut.fi/handle/10024/159166 [Google Scholar]
  88. X. Wang, L. Li, Y. Yuan, P. Ye, and F.-Y. Wang, “ACP-based social computing and parallel intelligence: Societies 5.0 and beyond,” CAAI Trans. Intell. Technol., vol. 1, no. 4, pp. 377–393, Oct. (2016), doi: 10.1016/j.trit.2016.11.005. [CrossRef] [Google Scholar]
  89. B. Gockel, A. Tudor, M. Brandyberry, R. Penmetsa, and E. Tuegel, “Challenges with Structural Life Forecasting Using Realistic Mission Profiles,” in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.20121813. [Google Scholar]
  90. E. Tuegel, “The Airframe Digital Twin: Some Challenges to Realization,” in 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, American Institute of Aeronautics and Astronautics. doi: 10.2514/6.20121812. [Google Scholar]
  91. M. Ciavotta, M. Alge, S. Menato, D. Rovere, and P. Pedrazzoli, “A Microservicebased Middleware for the Digital Factory,” Procedia Manuf., vol. 11, pp. 931–938, (2017), doi: 10.1016/j.promfg.2017.07.197. [CrossRef] [Google Scholar]
  92. G. Chen, P. Wang, B. Feng, Y. Li, and D. Liu, “The framework design of smart factory in discrete manufacturing industry based on cyber-physical system,” Int. J. Comput. Integr. Manuf., vol. 33, no. 1, pp. 79–101, (2020), doi: 10.1080/0951192X.2019.1699254. [CrossRef] [Google Scholar]
  93. R. Rosen, G. von Wichert, G. Lo, and K.D. Bettenhausen, “About The Importance of Autonomy and Digital Twins for the Future of Manufacturing,” IFAC-Pap., vol. 48, no. 3, pp. 567–572, Jan. (2015), doi: 10.1016/j.ifacol.2015.06.141. [Google Scholar]
  94. J. Lee, E. Lapira, B. Bagheri, and H. Kao, “Recent advances and trends in predictive manufacturing systems in big data environment,”Manuf. Lett., vol. 1, no. 1, pp. 38–41, Oct. (2013), doi: 10.1016/j.mfglet.2013.09.005. [Google Scholar]
  95. M. Grieves and J. Vickers, “Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems,” in Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches, (2016), pp. 85–113. doi: 10.1007/978-3-319-38756-7_4. [Google Scholar]
  96. E. Negri, L. Fumagalli, and M. Macchi, “A Review of the Roles of Digital Twin in CPS-based Production Systems,” Procedia Manuf., vol. 11, pp. 939–948, Jan. (2017), doi: 10.1016/j.promfg.2017.07.198. [CrossRef] [Google Scholar]
  97. T.H.-J. Uhlemann, C. Lehmann, and R. Steinhilper, “The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0,” Procedia CIRP, vol. 61, pp. 335340, Jan. (2017), doi: 10.1016/j.procir.2016.11.152. [Google Scholar]
  98. T. Bauernhansl, WGP-Standpunkt Industrie 4.0. WGP, Wissenschaftliche Gesellschaft für Produktionstechnik, (2016). [Google Scholar]
  99. D. Ivanov, “Predicting the impacts of epidemic outbreaks on global supply chains: A simulation-based analysis on the coronavirus outbreak (COVID-19/SARS-CoV-2) case,” Transp. Res. Part E Logist. Transp. Rev., vol. 136, (2020), doi: 10.1016/j.tre.2020.101922. [CrossRef] [Google Scholar]
  100. A. Rezaei Aderiani, K. Wärmefjord, and R. Söderberg, “Evaluating different strategies to achieve the highest geometric quality in self-adjusting smart assembly lines,” Robot. Comput.-Integr. Manuf., vol. 71, (2021) doi: 10.1016/j.rcim.2021.102164. [CrossRef] [Google Scholar]
  101. G.B. Ozturk, “Digital Twin Research in the AECO-FM Industry,” J. Build. Eng., vol. 40, p. 102730, Aug. (2021) doi: 10.1016/j.jobe.2021.102730. [CrossRef] [Google Scholar]
  102. P. Bosch-Sijtsema, C. Claeson-Jonsson, M. Johansson, and M. Roupe, “The hype factor of digital technologies in AEC,” Constr. Innov., vol. ahead-of-print, no. ahead-of-print, Jan. (2021) doi: 10.1108/CI-01-2020-0002. [Google Scholar]
  103. Q. Lu, X. Xie, A.K. Parlikad, and J.M. Schooling, “Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance,” Autom. Constr., vol. 118, p. 103277, Oct. (2020), doi: 10.1016/j.autcon.2020.103277. [CrossRef] [Google Scholar]
  104. J. Zhang, L. Zi, Y. Hou, M. Wang, W. Jiang, and D. Deng, “A Deep Learning-Based Approach to Enable Action Recognition for Construction Equipment,” Adv. Civ. Eng., vol. 2020, p. e8812928, Nov. (2020), doi: 10.1155/2020/8812928. [Google Scholar]
  105. J. Zhang, L. Zhao, G. Ren, H. Li, and X. Li, “Special Issue ‘Digital Twin Technology in the AEC Industry,’” Adv. Civ. Eng., vol. 2020, p. e8842113, Nov. (2020), doi: 10.1155/2020/8842113. [Google Scholar]
  106. E. Shahat, C.T. Hyun, and C. Yeom, “City digital twin potentials: A review and research agenda,” Sustain. Switz., vol. 13, no. 6, (2021) doi: 10.3390/su13063386. [Google Scholar]
  107. A. Tezel, E. Papadonikolaki, I. Yitmen, and P. Hilletofth, “Preparing construction supply chains for blockchain technology: An investigation of its potential and future directions,” Front. Eng. Manag., vol. 7, no. 4, pp. 547–563, Dec. (2020), doi: 10.1007/s42524-020-0110-8. [CrossRef] [Google Scholar]
  108. H. Khajavi, N.H. Motlagh, A. Jaribion, L.C. Werner, and J. Holmstrom, “Digital Twin: Vision, benefits, boundaries, and creation for buildings,” IEEE Access, vol. 7, pp. 147406–147419, (2019), doi: 10.1109/ACCESS.2019.2946515. [CrossRef] [Google Scholar]
  109. E. VanDer Horn and S. Mahadevan, “Digital Twin: Generalization, characterization and implementation,” Decis. Support Syst., vol. 145, (2021) doi: 10.1016/j.dss.2021.113524. [Google Scholar]
  110. “IOP Conference Series: Materials Science and Engineering,” 2019, vol. 618, no. 1. [Google Scholar]
  111. Z. Cheng and V.E. Kuzmichev, “Digital twin and men’s underwear design,” (2018), vol. 459, no. 1. doi: 10.1088/1757-899X/459/1/012075. [Google Scholar]
  112. P.P. Oliveira, “Digital twin development for airport management,” J. Airpt. Manag., vol. 14, no. 3, pp. 246–259, (2020). [Google Scholar]
  113. E.G. Kaigom and J. Rosmann, “Value-Driven Robotic Digital Twins in CyberPhysical Applications,” IEEE Trans. Ind. Inform., vol. 17, no. 5, pp. 3609–3619, (2021) doi: 10.1109/TII.2020.3011062. [CrossRef] [Google Scholar]
  114. M. Matulis and C. Harvey, “A robot arm digital twin utilising reinforcement learning,” Comput. Graph. Pergamon, vol. 95, pp. 106–114, (2021) doi: 10.1016/j.cag.2021.01.011. [CrossRef] [Google Scholar]
  115. Nativi, P. Mazzetti, and M. Craglia, “Digital ecosystems for developing digital twins of the earth: The destination earth case,” Remote Sens., vol. 13, no. 11, (2021) doi: 10.3390/rs13112119. [CrossRef] [Google Scholar]
  116. “Ural Environmental Science Forum ‘Sustainable Development of Industrial Region’, UESF 2021,” (2021) vol. 258. [Google Scholar]
  117. R. Snijders, P. Pileggi, J. Broekhuijsen, J. Verriet, M. Wiering, and K. Kok, “Machine learning for digital twins to predict responsiveness of cyber-physical energy systems,” (2020). doi: 10.1109/MSCPES49613.2020.9133695. [Google Scholar]
  118. Z. Hameed, Y.S. Hong, Y.M. Cho, S.H. Ahn, and C.K. Song, “Condition monitoring and fault detection of wind turbines and related algorithms: A review,” Renew. Sustain. Energy Rev., vol. 13, no. 1, pp. 1–39, (2009). [CrossRef] [Google Scholar]
  119. P.A. Fokaides et al., “Research challenges and advancements in the field of sustainable energy technologies in the built environment,” Sustain. Switz., vol. 12, no. 20, pp. 1–20, (2020), doi: 10.3390/su12208417. [Google Scholar]
  120. C.J. Valant, J.D. Wheaton, M.G. Thurston, S.P. McConky, and N.G. Nenadic, “Evaluation of 1D CNN Autoencoders for Lithium-ion Battery Condition Assessment Using Synthetic Data,” Annu. Conf. PHMSoc., vol. 11, no. 1, Art. no. 1, Sep. (2019), doi: 10.36001/phmconf.2019.v11i1.876. [Google Scholar]
  121. A. Kummerow, C. Monsalve, D. Rösch, K. Schäfer, and S. Nicolai, “Cyber-physical data stream assessment incorporating Digital Twins in future power systems,” in 2020 International Conference on Smart Energy Systems and Technologies (SEST), Sep. (2020), pp. 1–6. doi: 10.1109/SEST48500.2020.9203270. [Google Scholar]
  122. Y. Yang, X. Li, Z. Yang, Q. Wei, N. Wang, and L. Wang, “The application of cyber physical system for thermal power plants: Data-driven modeling,” Energies, vol. 11, no. 4, (2018), doi: 10.3390/en11040690. [Google Scholar]
  123. Y. Cui, F. Xiao, W. Wang, X. He, C. Zhang, and Y. Zhang, “Digital Twin for Power System Steady-state Modelling, Simulation, and Analysis,” in 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2), Nov. (2020), pp. 1233–1238. doi: 10.1109/EI250167.2020.9346850. [CrossRef] [Google Scholar]
  124. H. Pan, Z. Dou, Y. Cai, W. Li, X. Lei, and D. Han, “Digital Twin and Its Application in Power System,” in 2020 5th International Conference on Power and Renewable Energy (ICPRE), Sep. (2020), pp. 21–26. doi: 10.1109/ICPRE51194.2020.9233278. [CrossRef] [Google Scholar]
  125. M. Atalay and P. Angin, “A Digital Twins Approach to Smart Grid Security Testing and Standardization,” in 2020 IEEE International Workshop on Metrology for Industry 4.0IoT, Jun. (2020), pp. 435–440. doi: 10.1109/MetroInd4.0IoT48571.2020.9138264. [CrossRef] [Google Scholar]
  126. A.E. Onile, R. Machlev, E. Petlenkov, Y. Levron, and J. Belikov, “Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: A review,” Energy Rep., vol. 7, pp. 997–1015, Nov. (2021) doi: 10.1016/j.egyr.2021.01.090. [CrossRef] [Google Scholar]
  127. N. Tzanis, N. Andriopoulos, A. Magklaras, E. Mylonas, M. Birbas, and A. Birbas, “A Hybrid Cyber Physical Digital Twin Approach for Smart Grid Fault Prediction,” in 2020 IEEE Conference on Industrial Cyberphysical Systems (ICPS), Jun. (2020), vol. 1, pp. 393–397. doi: 10.1109/ICPS48405.2020.9274723. [CrossRef] [Google Scholar]
  128. X. Xie, A.K. Parlikad, and R.S. Puri, “A Neural Ordinary Differential Equations Based Approach for Demand Forecasting within Power Grid Digital Twins,” in 2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Oct. (2019), pp. 1–6. doi: 10.1109/SmartGridComm.2019.8909789. [Google Scholar]
  129. X. He, Q. Ai, R.C. Qiu, and D. Zhang, “Preliminary Exploration on Digital Twin for Power Systems: Challenges, Framework, and Applications,” ArXiv190906977 Eess Stat, Sep. (2019), Accessed: Mar. 11, 2021. Online. Available: http://arxiv.org/abs/1909.06977 [Google Scholar]
  130. J. Huang, L. Zhao, F. Wei, and B. Cao, “The Application of Digital Twin on Power Industry,” IOP Conf. Ser. Earth Environ. Sci., vol. 647, p. 012015, Jan. (2021) doi: 10.1088/1755-1315/647/1/012015. [CrossRef] [Google Scholar]
  131. P. Pileggi, J. Verriet, J. Broekhuijsen, C. van Leeuwen, W. Wijbrandi, and M. Konsman, “A Digital Twin for Cyber-Physical Energy Systems,” in 2019 7th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), Apr. (2019), pp. 1–6. doi: 10.1109/MSCPES.2019.8738792. [Google Scholar]
  132. T.I. Strasser et al., “An overview of trends and developments of internet of things applied to industrial systems,” in Proceedings: IECON - Annu. Conf. IEEE Ind. Electron. Soc., (2018), pp. 2853–2860. doi: 10.1109/IECON.2018.8591431. [Google Scholar]
  133. D. Tucker, P. Pezzini, and K.M. Bryden, “Cyber-physical systems: A new paradigm for energy technology development,” in ASME Power Div Publ POWER, (2018), vol. 1. doi: 10.1115/POWER2018-7315. [Google Scholar]
  134. L.V. Massel and A.G. Massel, “Development of Digital Twins and Digital Shadows of Energy Objects and Systems Using Scientific Tools for Energy Research,” in E3S Web Conf., (2020), vol. 209. doi: 10.1051/e3sconf/202020902019. [Google Scholar]
  135. A. Saad, S. Faddel, T. Youssef, and O.A. Mohammed, “On the Implementation of IoT-Based Digital Twin for Networked Microgrids Resiliency Against Cyber Attacks,” IEEE Trans. Smart Grid, vol. 11, no. 6, pp. 5138–5150, Nov. (2020), doi: 10.1109/TSG.2020.3000958. [CrossRef] [Google Scholar]
  136. A. Benigni, T. Strasser, G. De Carne, M. Liserre, M. Cupelli, and A. Monti, “RealTime Simulation-Based Testing of Modern Energy Systems: A Review and Discussion,” IEEE Ind. Electron. Mag., vol. 14, no. 2, pp. 28–39, (2020), doi: 10.1109/MIE.2019.2957996. [CrossRef] [Google Scholar]
  137. N. Conway and A. Hainoun, “Regional energy demand analysis portal (REDAP) digitalisation: Enabling better government decision-making in the building & transport sectors,” in IOP Conf. Ser. Earth Environ. Sci., vol. 588, no. 3. (2020), doi: 10.1088/1755-1315/588/3/032008. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.