REFERENCES
1. Jnr B, Abdul Majid M, Romli A. Emerging case oriented agents for sustaining educational institutions going green towards environmental responsibility. J Syst Inf Technol 2019;21:186-214.
2. Hrovatin N, Cagno E, Dolšak J, Zorić J. How important are perceived barriers and drivers versus other contextual factors for the adoption of energy efficiency measures: an empirical investigation in manufacturing SMEs. J Clean Prod 2021;323:129123.
3. European Commission, directorate-general for climate action. Going climate-neutral by 2050 : a strategic long-term vision for a prosperous, modern, competitive and climate-neutral EU economy. Available from: https://data.europa.eu/doi/10.2834/02074 [Last accessed on 26 Dec 2022].
4. Center for Climate and Energy Solutions. Getting to zero: A U.S. climate agenda. Available from: https://www.c2es.org/site/assets/uploads/2019/12/C2ES-Getting-to-Zero-summary-report.pdf [Last accessed on 26 Dec 2022].
5. Chen B, Fæste L, Jacobsen R, Teck Kong M, Dylan Lu D, Palme T. How China can achieve carbon neutrality by 2060. Available from: https://www.bcg.com/publications/2020/how-china-can-achieve-carbon-neutrality-by-2060. [Last accessed on 26 Dec 2022].
6. Mourtzis D, Angelopoulos J, Panopoulos N. Mourtzis D, Angelopoulos J, Panopoulos N. Digital manufacturing: the evolution of traditional manufacturing toward an automated and interoperable Smart Manufacturing Ecosystem. The Digital Supply Chain. Elsevier; 2022. pp. 27-45.
7. Leng J, Sha W, Wang B, et al. Industry 5.0: prospect and retrospect. J Manuf Syst 2022;65:279-95.
8. Huang S, Wang B, Li X, Zheng P, Mourtzis D, Wang L. Industry 5.0 and Society 5.0 - comparison, complementation and co-evolution. J Manuf Syst 2022;64:424-8.
9. Mourtzis D, Boli N, Alexopoulos K, Różycki D. A framework of energy services: from traditional contracts to product-service system (PSS). Procedia CIRP 2018;69:746-51.
10. IEA. World energy outlook 2020. Available from: https://www.iea.org/reports/world-energy-outlook-2020 [Last accessed on 26 Dec 2022].
11. European Commission. Gas and electricity market reports 2022. Available from: https://energy.ec.europa.eu/data-and-analysis/market-analysis_en [Last accessed on 26 Dec 2022].
12. Eck NJ,Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 2010;84:523-38.
13. Cin E, Carraro G, Volpato G, Lazzaretto A, Danieli P. A multi-criteria approach to optimize the design-operation of Energy Communities considering economic-environmental objectives and demand side management. Energy Convers Manag 2022;263:115677.
14. Sachs J, Kroll C, Lafortune G, Fuller G, Woelm F. Sustainable development report 2021. Cambridge University Press; 2021.
15. Mourtzis D. Towards the 5th industrial revolution: a literature review and a framework for process optimization based on big data analytics and semantics. J Mach Eng 2021;21:5-39.
16. IEA. Industry direct CO2 emissions in the Sustainable Development Scenario, 2000-2030. Available from: https://www.iea.org/data-and-statistics/charts/industry-direct-co2-emissions-in-the-sustainable-development-scenario-2000-2030 [Last accessed on 26 Dec 2022].
17. Nagasawa T, Pillay C, Beier G, et al. Accelerating clean energy through industry 4.0 manufacturing the next revolution. Available from: https://www.unido.org/sites/default/files/2017-08/REPORT_Accelerating_clean_energy_through_Industry_4.0.Final_0.pdf [Last accessed on 26 Dec 2022].
18. Singh R, Akram SV, Gehlot A, Buddhi D, Priyadarshi N, Twala B. Energy system 4.0: digitalization of the energy sector with inclination towards sustainability. Sensors 2022;22:6619.
19. Carayannis EG, Draper J, Bhaneja B. Towards fusion energy in the industry 5.0 and society 5.0 context: call for a global commission for urgent action on fusion energy. J Knowl Econ 2021;12:1891-904.
20. Mourtzis D, Vlachou E. A cloud-based cyber-physical system for adaptive shop-floor scheduling and condition-based maintenance. J Manuf Syst 2018;47:179-98.
21. Strielkowski W, Rausser G, Kuzmin E. Digital revolution in the energy sector: effects of using digital twin technology. In: Kumar V, Leng J, Akberdina V, Kuzmin E, editors. Digital transformation in industry. Cham: Springer International Publishing; 2022. pp. 43-55.
22. Lang M. From industry 4.0 to energy 4.0. future business, models and legal relations. Available from: http://wise.co.th/wise/References/Creative_Economy/From_Industry_4_to_Energy_4.pdf [Last accessed on 26 Dec 2022].
23. Vineetha CP, Babu CA. Smart grid challenges, issues and solutions. In 2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG); 2014. pp. 1-4.
24. Kupzog F, King R, Stefan M. The role of IT in energy systems: the digital revolution as part of the problem or part of the solution. Elektrotech Inftech 2020;137:341-5.
25. Mourtzis D, Angelopoulos J, Panopoulos N. A literature review of the challenges and opportunities of the transition from industry 4.0 to society 5.0. Energies 2022;15:6276.
26. Moreno Escobar JJ, Morales Matamoros O, Tejeida Padilla R, et al. A comprehensive review on smart grids: challenges and opportunities. Sensors 2021;21:6978.
27. Mourtzis D, Angelopoulos J, Panopoulos N. A collaborative approach on energy-based offered services: energy 4.0 ecosystems. Procedia CIRP 2021;104:1638-43.
28. Mourtzis D, Angelopoulos J, Panopoulos N. Development of a PSS for smart grid energy distribution optimization based on digital twin. Procedia CIRP 2022;107:1138-43.
29. Jacobson MZ. 100% Clean, Renewable Energy and Storage for Everything. New York: Cambridge University Press; 2020. pp. 427. Available from: https://web.stanford.edu/group/efmh/jacobson/WWSBook/WWSBook.html [Last accessed on 26 Dec 2022].
30. Greer C, Wollman D A, Prochaska D, et al. NIST framework and roadmap for smart grid interoperability standards, release 3.0. 2014.
31. Heffner G. Smart grid-smart customer policy needs. Available from: https://www.ctc-n.org/sites/www.ctc-n.org/files/resources/sg_cust_pol.pdf [Last accessed on 26 Dec 2022].
32. Spelman M. How will the digital revolution transform the energy sector? Available from: https://www.weforum.org/agenda/2016/03/how-will-the-digital-revolution-transform-the-energy-sector/ [Last accessed on 26 Dec 2022].
33. Stem Inc. Reduce global adjustment charges with the world leader in energy storage. Available from: https://www.convergentep.com/canada-guarantee/ [Last accessed on 26 Dec 2022].
34. Angelopoulos J, Mourtzis D. An intelligent product service system for adaptive maintenance of engineered-to-order manufacturing equipment assisted by augmented reality. Appl Sci 2022;12:5349.
35. Song EY, FitzPatrick GJ, Lee KB, Griffor E. A methodology for modeling interoperability of smart sensors in smart grids. IEEE Trans Smart Grid 2022;13:555-63.
36. Alonso M, Amaris H, Alcala D, Florez R DM. Smart sensors for smart grid reliability. Sensors 2020;20:2187.
37. United Nations. Transforming our world: the 2030 agenda for sustainable development. Available from: https://sdgs.un.org/2030agenda [Last accessed on 26 Dec 2022].
38. United Nations. Sustainable development knowledge platform. Available from: https://sustainabledevelopment.un.org [Last accessed on 26 Dec 2022].
39. Grijpink F, Kutcher E, Ménard A, et al. Connected world: an evolution in connectivity beyond the 5G revolution. Available from: https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/connected-world-an-evolution-in-connectivity-beyond-the-5g-revolution [Last accessed on 26 Dec 2022].
40. Hui H, Ding Y, Shi Q, Li F, Song Y, Yan J. 5G network-based internet of things for demand response in smart grid: a survey on application potential. Appl Energy 2020;257:113972.
41. Thollander P, Paramonova S, Cornelis E, et al. International study on energy end-use data among industrial SMEs (small and medium-sized enterprises) and energy end-use efficiency improvement opportunities. J Clean Prod 2015;104:282-96.
42. Smartgrid.gov. US department of energy’s office of electricity delivery and energy reliability. Available from: https://www.smartgrid.gov/ [Last accessed on 26 Dec 2022].
45. Karanjkar N, Joglekar A, Mohanty S, Prabhu V, Raghunath D, Sundaresan R. Digital twin for energy optimization in an SMT-PCB assembly line. In 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS); 2018. pp. 85-89.
46. Zhou M, Yan J, Feng D. Digital twin and its application to power grid online analysis. CSEE J Power Energy Syst 2019;5:391-8.
47. Stavropoulos P, Mourtzis D. Digital twins in industry 4.0. design and operation of production networks for mass personalization in the era of cloud technology. Elsevier; 2022. pp. 277-316.
48. Francisco A, Mohammadi N, Taylor JE. Smart city digital twin-enabled energy management: toward real-time urban building energy benchmarking. J Manage Eng 2020:36.
49. Srinivasan RS, Manohar B, Issa RRA. Urban building energy CPS (UBE-CPS): real-time demand response using digital twin. In: Anumba CJ, Roofigari-esfahan N, editors. Cyber-Physical Systems in the Built Environment. Cham: Springer International Publishing; 2020. pp. 309-22.
50. Yu W, Patros P, Young B, Klinac E, Walmsley TG. Energy digital twin technology for industrial energy management: classification, challenges and future. Renew Sust Energ Rev 2022;161:112407.
51. Mourtzis D, Milas N, Athinaios N. Towards machine shop 4.0: a general machine model for CNC machine-tools through OPC-UA. Procedia CIRP 2018;78:301-6.
52. Mourtzis D, Angelopoulos J, Panopoulos N. Design and development of an IoT enabled platform for remote monitoring and predictive maintenance of industrial equipment. Procedia Manuf 2021;54:166-71.
53. Onile AE, Machlev R, Petlenkov E, Levron Y, Belikov J. Uses of the digital twins concept for energy services, intelligent recommendation systems, and demand side management: a review. Energy Rep 2021;7:997-1015.
54. Hamwi M, Lizarralde I. A review of business models towards service-oriented electricity systems. Procedia CIRP 2017;64:109-14.
55. Meier H, Völker O, Funke B. Industrial product-service systems (IPS2): paradigm shift by mutually determined products and services. Int J Adv Manuf Technol 2011;52:1175-91.
56. George G, Bock J. A.The business model book: design, build and adapt business ideas that drive business growth. 1st ed. United Kingdom: Pearson; 2017.
57. Annarelli A, Battistella C, Nonino F. Product service system: a conceptual framework from a systematic review. J Clean Prod 2016;139:1011-32.
58. Catulli M, Cook M, Potter S. Consuming use orientated product service systems: a consumer culture theory perspective. J Clean Prod 2017;141:1186-93.
59. Zhang H, Zhao F, Sutherland JW. Energy-efficient scheduling of multiple manufacturing factories under real-time electricity pricing. CIRP Ann 2015;64:41-4.
60. Mourtzis D, Angelopoulos J, Panopoulos N. A collaborative approach on energy-based offered services: energy 4.0 ecosystems. Procedia CIRP 2021;104:1638-43.
61. Unterberger E, Eisenreich F, Reinhart G. Design principles for energy flexible production systems. Procedia CIRP 2018;67:98-103.
62. Tsaousoglou G, Efthymiopoulos N, Makris P, Varvarigos E. Personalized real time pricing for efficient and fair demand response in energy cooperatives and highly competitive flexibility markets. J Mod Power Syst Clean Energy 2019;7:151-62.
63. Mourtzis D, Vlachou E, Milas N, Dimitrakopoulos G. Energy consumption estimation for machining processes based on real-time shop floor monitoring via wireless sensor networks. Procedia CIRP 2016;57:637-42.
64. Keller F, Reinhart G. Systematic approach for energy-supply-orientated production planning. Int J Ind Manuf Eng 2015;9:2417-22.
65. Biel K, Glock CH. Systematic literature review of decision support models for energy-efficient production planning. Comput Ind Eng 2016;101:243-59.
66. Zhang Q, Grossmann IE. Planning and scheduling for industrial demand side management: advances and challenges. In: Martín M, editor. Alternative energy sources and technologies. Springer, Cham; 2016.
67. Curiale M. From smart grids to smart city. 2014 Saudi Arabia Smart Grid Conference (SASG); 2014, pp. 1-9.
68. Bellekom S, Arentsen M, van Gorkum K. Prosumption and the distribution and supply of electricity. Energ Sustain Soc 2016:6.
69. Anthony B, Petersen SA, Ahlers D, Krogstie J, Livik K. Big data-oriented energy prosumption service in smart community districts: a multi-case study perspective. Energy Inform 2019;2:36.
70. Hussain HM, Narayanan A, Nardelli PHJ, Yang Y. What is energy internet? IEEE Access 2020;8:183127-45.
71. Cao J, Yang M. Energy internet - towards smart grid 2.0. 2013 Fourth International Conference on Networking and Distributed Computing, 2013, pp. 105-110.
72. Shahinzadeh H, Moradi J, Gharehpetian GB, Nafisi H, Abedi M. Internet of energy (IoE) in smart power systems. 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI); 2019. pp. 627-36.
73. Kabalci E, Kabalci Y. Introduction to smart grid and internet of energy systems. From smart grid to internet of energy. Elsevier; 2019. pp. 1-62.
74. Gopstein A, Nguyen C, O'Fallon C, Hastings N, Wollman D. NIST framework and roadmap for smart grid interoperability standards, release 4.0. Department of commerce. National Institute of Standards and Technology; 2021.
75. Dehalwar V, Kolhe ML, Deoli S, Jhariya MK. Blockchain-based trust management and authentication of devices in smart grid. Clean Eng Technol 2022;8:100481.
76. Guo Y, Wan Z, Cheng X. When blockchain meets smart grids: a comprehensive survey. High-Confidence Comput 2022;2:100059.
77. Yapa C, de Alwis C, Liyanage M, Ekanayake J. Survey on blockchain for future smart grids: technical aspects, applications, integration challenges and future research. Energy Rep 2021;7:6530-64.
78. Serban AC, Lytras MD. Artificial intelligence for smart renewable energy sector in europe - smart energy infrastructures for next generation smart cities. IEEE Access 2020;8:77364-77.
79. Das UK, Tey KS, Seyedmahmoudian M, et al. Forecasting of photovoltaic power generation and model optimization: a review. Renew Sust Energ Rev 2018;81:912-28.
80. Ssekulima EB, Anwar MB, Al Hinai A, El Moursi MS. Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: a review. IET Renew Power Gener 2016;10:885-989.
81. Bhandari B, Lee K, Lee G, Cho Y, Ahn S. Optimization of hybrid renewable energy power systems: a review. Int J Precis Eng Manuf-Green Tech 2015;2:99-112.
82. Dawoud SM, Lin X, Okba MI. Hybrid renewable microgrid optimization techniques: a review. Renew Sust Energ Rev 2018;82:2039-52.
83. Javaid N, Hafeez G, Iqbal S, Alrajeh N, Alabed MS, Guizani M. Energy efficient integration of renewable energy sources in the smart grid for demand side management. IEEE Access 2018;6:77077-96.
85. Neves D, Pina A, Silva CA. Comparison of different demand response optimization goals on an isolated microgrid. Sustain Energy Technol Assess 2018;30:209-15.
87. Atef S, Eltawil A. A comparative study using deep learning and support vector regression for electricity price forecasting in smart grids. In Proceedings of the IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA); 2019, pp. 603-7.
88. Qdr Q. Benefits of demand response in electricity markets and recommendations for achieving them. Available from: http://www.madrionline.org/wp-content/uploads/2017/02/doe_2006_dr_benefitsrecommendations.pdf [Last accessed on 26 Dec 2022].
89. Ahmad T, Chen H. Utility companies strategy for short-term energy demand forecasting using machine learning based models. Sustain Cities Soc 2018;39:401-17.
90. Ahmad T, Chen H, Shah WA. Effective bulk energy consumption control and management for power utilities using artificial intelligence techniques under conventional and renewable energy resources. Int Journal Electr Power Energy Syst 2019;109:242-58.
91. Lu R, Hong SH, Yu M. Demand response for home energy management using reinforcement learning and artificial neural network. IEEE Trans Smart Grid 2019;10:6629-39.
92. Alazab M, Khan S, Krishnan SSR, Pham Q, Reddy MPK, Gadekallu TR. A multidirectional LSTM model for predicting the stability of a smart grid. IEEE Access 2020;8:85454-63.
93. Al-Fuqaha A, Guizani M, Mohammadi M, Aledhari M, Ayyash M. Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surveys Tuts 2015;17:2347-76.
94. Mourtzis D, Angelopoulos J, Panopoulos N. Smart Manufacturing and tactile internet based on 5G in industry 4.0: challenges, applications and new trends. Electronics 2021;10:3175.
95. Wang K, Wang Y, Hu X, et al. Wireless big data computing in smart grid. IEEE Wirel Commun 2017;24:58-64.
96. Deepa N, Pham Q, Nguyen DC, et al. A survey on blockchain for big data: approaches, opportunities, and future directions. Future Gener Comput Syst 2022;131:209-26.
97. Mourtzis D. Simulation in the design and operation of manufacturing systems: state of the art and new trends. Int J Prod Res 2020;58:1927-49.
98. Bornstein J. Energy-as-a-service, the lights are on. Is anyone home? Deloitte UK 2019. Available from: https://www2.deloitte.com/content/dam/Deloitte/uk/Documents/energy-resources/deloitte-uk-energy-as-a-service-report-2019.pdf [Last accessed on 26 Dec 2022].
99. Mourtzis D, Angelopoulos J, Panopoulos N. A survey of digital B2B platforms and marketplaces for purchasing industrial product service systems: a conceptual framework. Procedia CIRP 2021;97:331-6.
100. Matsas M, Pintzos G, Kapnia A, Mourtzis D. An integrated collaborative platform for managing product-service across their life cycle. Procedia CIRP 2017;59:220-6.
101. Mourtzis D, Boli N, Xanthakis E, Alexopoulos K. Energy trade market effect on production scheduling: an industrial product-service system (IPSS) approach. Int J Comput Integr Manuf 2021;34:76-94.
102. Gartner Inc. Gartner says 6.4 billion connected ‘things’ will be in use in 2016, up 30 percent from 2015. Available from: https://www.gartner.com/en/newsroom/press-releases/2015-11-10-gartner-says-6-billion-connected-things-will-be-in-use-in-2016-up-30-percent-from-2015 [Last accessed on 26 Dec 2022].
103. Navigant Consulting Inc. Navigating the energy transformation: building a competitive advantage for energy cloud 2.0. Available from: https://guidehouse.com/-/media/www/site/events/2016/pdfs/engerati-webinar-gridedge-december-8-2016-final-up.pdf [Last accessed on 26 Dec 2022].
104. Kim B, Zhang Y, van der Schaar M, Lee J. Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans Smart Grid 2016;7:2187-98.
105. Mocanu E, Mocanu DC, Nguyen PH, et al. On-line building energy optimization using deep reinforcement learning. IEEE Trans Smart Grid 2019;10:3698-708.
106. Ferrag MA, Maglaras L. DeepCoin: A novel deep learning and blockchain-based energy exchange framework for smart grids. IEEE Trans Eng Manage 2020;67:1285-97.
107. Helbing G, Ritter M. Deep learning for fault detection in wind turbines. Renew Sust Energy Rev 2018;98:189-98.
108. Milas N, Mourtzis D, Tatakis E. A decision-making framework for the smart charging of electric vehicles considering the priorities of the driver. Energies 2020;13:6120.
109. Mocanu E, Nguyen PH, Gibescu M, Kling WL. Deep learning for estimating building energy consumption. Sustain Energy Grids Netw 2016;6:91-9.
110. Manoj P, Kumar YB, Gowtham M, Vishwas DB, Ajay AV. Internet of things for smart grid applications. In advances in smart grid power system. Academic Press; 2021. pp. 159-90.
111. . Stabilization, safety, and security of distributed systems. In: Guerraoui R, Petit F. editors. 11th International Symposium, SSS 2009, Lyon, France. Proceedings. Springer; 2009.
112. Winter T. C. The advantages and challenges of the blockchain for smart grids. Available from: https://repository.tudelft.nl/islandora/object/uuid:e4818a29-3344-4ae1-bd26-97b5a06403ae [Last accessed on 26 Dec 2022].
113. IEA. Smart grids. Available from: https://www.iea.org/reports/smart-grids [Last accessed on 26 Dec 2022].
