REFERENCES

[1] L. Dusonchet, S. Favuzza, F. Massaro, E. Telaretti, G. Zizzo, Technological and legislative status point of stationary energy storages in the EU, Renew. Sustain. Energy Rev. 101 (2019) 158–167. [2] F.P. Sioshansi, Consumer, Prosumer, Prosumager: How Service Innovations Will Disrupt the Utility Business Model, Academic Press, 2019. [3] M. dos Santos Silva, Study on “residential prosumers in the European energy union”, 2017. [4] A. Fernández-Izquierdo, A. Cimmino, C. Patsonakis, A.C. Tsolakis, R. García- Castro, D. Ioannidis, D. Tzovaras, OpenADR ontology: Semantic enrichment of demand response strategies in smart grids, in: Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies, Istanbul, Turkey, 7-9 September 2020, IEEE, 2020, pp. 1–6. [5] M.H. Yaghmaee, A. Leon-Garcia, M. Moghaddassian, On the performance of distributed and cloud-based demand response in smart grid, IEEE Trans. Smart Grid 9 (5) (2018) 5403–5417, http://dx.doi.org/10.1109/TSG.2017. 2688486. [6] C. Wang, N. Nasiriani, G. Kesidis, B. Urgaonkar, Q. Wang, L.Y. Chen, A. Gupta, R. Birke, Recouping energy costs from cloud tenants: Tenant demand response aware pricing design, in: Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems, Bangalore, India, July 14-17, 2015, ACM, 2015, pp. 141–150. [7] T. Deng, J. Yao, H. Guan, Maximizing profit of cloud service brokerage with economic demand response, in: Proceedings of the IEEE Conference on Computer Communications, Honolulu, HI, USA, April 16-19, 2018, IEEE, 2018, pp. 1907–1915, http://dx.doi.org/10.1109/INFOCOM.2018.8486412. [8] Y. Chen, J.M. Chang, Fair demand response with electric vehicles for the cloud based energy management service, IEEE Trans. Smart Grid 9 (1) (2018) 458–468, http://dx.doi.org/10.1109/TSG.2016.2609738. [9] K. Kaur, S. Garg, G. Kaddoum, S.H. Ahmed, F. Gagnon, M. Atiquzza- man, Demand-response management using a fleet of electric vehicles: An opportunistic-SDN-based edge-cloud framework for smart grids, IEEE Netw. 33 (5) (2019) 46–53, http://dx.doi.org/10.1109/MNET.001.1800496. [10] X. Zhang, D. Biagioni, M. Cai, P. Graf, S. Rahman, An edge-cloud integrated solution for buildings demand response using reinforcement learning, IEEE Trans. Smart Grid 12 (1) (2021) 420–431, http://dx.doi.org/10.1109/TSG. 2020.3014055. [11] M. Frincu, R. Draghici, Towards a scalable cloud enabled smart home automation architecture for demand response, in: Proceedings of the 2016 IEEE PES Innovative Smart Grid Technologies Conference Europe, Ljubljana, Slovenia, October 9-12, 2016, IEEE, 2016, pp. 1–6, http://dx.doi.org/10. 1109/ISGTEurope.2016.7856235. [12] N. Galkin, C.-W. Yang, L. Nordström, V. Vyatkin, Prototyping multi-protocol communication to enable semantic interoperability for demand response services, in: 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), IEEE, 2021, pp. 15–20. [13] J. Wu, F. Orlandi, T. AlSkaif, D. O’Sullivan, S. Dev, A semantic web approach to uplift decentralized household energy data, Sustain. Energy Grids Netw. 32 (2022) 100891, http://dx.doi.org/10.1016/j.segan.2022.100891, URL: https://www.sciencedirect.com/science/article/pii/S2352467722001497. [14] I. Esnaola-Gonzalez, F.J. Díez, L. Berbakov, N. Tomasevic, P. Štorek, M. Cruz, P. Kirketerp, Semantic interoperability for demand-response programs: Respond project’s use case, in: 2018 Global Internet of Things Summit (GIoTS), IEEE, 2018, pp. 1–6. [15] Q. Zhou, S. Natarajan, Y. Simmhan, V. Prasanna, Semantic information mod- eling for emerging applications in smart grid, in: 2012 Ninth International Conference on Information Technology-New Generations, IEEE, 2012, pp. 775–782. [16] Hongseok Kim, Y. Kim, K. Yang, M. Thottan, Cloud-based demand response for smart grid: Architecture and distributed algorithms, in: Proceedings of the 2011 IEEE International Conference on Smart Grid Communications, Brussels, Belgium, October 17-20, 2011, IEEE, pp. 398–403, http://dx.doi. org/10.1109/SmartGridComm.2011.6102355. [17] F. Bellifemine, A. Poggi, G. Rimassa, JADE–a FIPA-compliant agent framework, in: Proceedings of PAAM, London, 1999, p. 33. [18] H. Wicaksono, T. Boroukhian, A. Bashyal, A demand-response system for sustainable manufacturing using linked data and machine learning, in: Dynamics in Logistics, Springer, Cham, 2021, pp. 155–181. [19] A.T. Schreiber, Y. Raimond, RDF 1.1 primer, 2014. [20] P. Hitzler, M. Krötzsch, B. Parsia, P.F. Patel-Schneider, S. Rudolph, OWL 2 web ontology language primer, W3C Recomm. (2009). [21] A. Fernández-Izquierdo, A. Cimmino, R. García-Castro, Supporting demand- response strategies with the DELTA ontology, in: 2021 IEEE/ACS 18th International Conference on Computer Systems and Applications (AICCSA), IEEE, 2021, pp. 1–8. [22] H. Knublauch, D. Kontokostas, Shapes constraint language (SHACL), W3C Recomm. (2017) URL: https://www.w3.org/TR/shacl/. [23] A. Cimmino, A. Fernández-Izquierdo, M. Poveda-Villalón, R. García-Castro, Ontologies for IoT semantic interoperability, in: C. Zivkovic, Y. Guan, C. Grimm (Eds.), IoT Platforms, Use Cases, Privacy, and Business Models: With Hands-on Examples Based on the VICINITY Platform, Springer International Publishing, Cham, 2021, pp. 99–123. [24] Energy Market Information Exchange (EMIX) Version 1.0, OASIS Consor- tium, 2012. [25] An Introduction to the Universal Smart Energy Framework, Smart Energy, 2019. [26] OpenADR 2.0 Profile Specification, B Profile, OpenADR Alliance, 2015. [27] EN 50090-1 Home and Building Electronic Systems (HBES), CENELEC, 2011. [28] IEC 61970-301:2020 Energy Management System Application Program Interface (EMS-API) - Part 301: Common Information Model (CIM) base, International Electrotechnical Commission, 2020. [29] IEC 62056 Electricity Metering Data Exchange - The DLMS/COSEM Suite, International Electrotechnical Commission, 2016. [30] IEC 62746-10-1:2018 Systems Interface Between Customer Energy Man- agement System and the Power Management System, International Electrotechnical Commission, 2018. [31] C. Neureiter, Introduction to the SGAM toolbox, in: Josef Ressel Center for User-Centric Smart Grid Privacy, Security and Control, Salzburg University of Applied Sciences, Tech. Rep, 2013. [32] T. Linnenberg., A.W. Mueller., L. Christiansen., C. Seitz., A. Fay., OntoENERGY – a lightweight ontology for supporting energy-efficiency tasks - en- abling generic evaluation of energy efficiency in the engineering phase of automated manufacturing plants, in: Proceedings of the International Con- ference on Knowledge Engineering and Ontology Development, Algarve, Portugal, September 19-22, 2013, Vol. 1, 2013, pp. 337–344. [33] J.L. Hippolyte, S. Howell, B. Yuce, M. Mourshed, H.A. Sleiman, M. Vinyals, L. Vanhee, Ontology-based demand-side flexibility management in smart grids using a multi-agent system, in: Proceedings of the 2016 IEEE Interna- tional Smart Cities Conference, Trento, Italy, September 12-15, 2016, 2016, pp. 1–7. [34] M.J. Kofler, C. Reinisch, W. Kastner, A semantic representation of energy- related information in future smart homes, Energy Build. 47 (2012) 169–179. [35] J. Verhoosel, D. Rothengatter, F. Rumph, M. Konsman, An ontology for modeling flexibility in smart grid energy management, in: Proceedings of the EWork and EBusiness in Architecture, Engineering and Construction - European Conference on Product and Process Modelling, Reykjavik, Iceland, 25-27 July 2012, CRC Press, 2012, pp. 931–938. [36] T.G. Stavropoulos, D. Vrakas, D. Vlachava, N. Bassiliades, Bonsai: a smart building ontology for ambient intelligence, in: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, Craiova, Romania, June 13-15, 2012, ACM, 2012, pp. 1–12. [37] J.-L. Hippolyte, S. Howell, B. Yuce, M. Mourshed, H.A. Sleiman, M. Vinyals, L. Vanhée, Ontology-based demand-side flexibility management in smart grids using a multi-agent system, in: 2016 IEEE International Smart Cities Conference (ISC2), IEEE, 2016, pp. 1–7. [38] I. Esnaola-Gonzalez, J. Bermúdez, I. Fernandez, A. Arnaiz, EEPSA as a core ontology for energy efficiency and thermal comfort in buildings, Appl. Ontol. 16 (2) (2021) 193–228. [39] M. Haghgoo, I. Sychev, A. Monti, F.H. Fitzek, SARGON–smart energy domain ontology, IET Smart Cities 2 (4) (2020) 191–198. [40] M.-F. Robbe, M. Vinyals, S. Lodeweyckx, J.M. Espeche, P.-E. Brun, S.V. Costa, M. Mourshed, A. Kavgić, T. Loureiro, Putting residential flexibility management into action with pilot sites in europe: From mas2tering to drive projects, Multidiscip. Digit. Publ. Inst. Proc. 2 (15) (2018) 1130. [41] A.M. Ouksel, A. Sheth, Semantic interoperability in global information systems, ACM SIGMOD Rec. 28 (1) (1999) 5–12. [42] I. Esnaola-Gonzalez, F.J. Diez, Integrating building and iot data in demand response solutions, in: Proceedings of the 7th Linked Data in Architecture and Construction Workshop (LDAC 2019), 2389, CEUR, 2019, pp. 92–105. [43] A.E. Youssef, Exploring cloud computing services and applications, J. Emerg. Trends Comput. Inform. Sci. 3 (6) (2012) 838–847. [44] A. Hornsby, R. Walsh, From instant messaging to cloud computing, an XMPP review, in: Proceedings of the IEEE International Symposium on Consumer Electronics , Braunschweig, Germany, June 7-10, 2010, IEEE, 2010, pp. 1–6. [45] M. Kirsche, R. Klauck, Unify to bridge gaps: Bringing XMPP into the internet of things, in: Proceedings of the 2012 IEEE International Conference on Per- vasive Computing and Communications Workshops, Lugano, Switzerland, March 19-23, 2012, IEEE, 2012, pp. 455–458. [46] A. Poggi, D. Lembo, D. Calvanese, G. De Giacomo, M. Lenzerini, R. Rosati, Linking data to ontologies, in: Journal on Data Semantics X, Springer, 2008, pp. 133–173. [47] C.R. Rivero, A. Schultz, C. Bizer, D. Ruiz Cortés, Benchmarking the perfor- mance of linked data translation systems, in: Proceedings of the Workshop on Linked Data on the Web, Lyon, France, 16 April, 2012, CEUR-WS, 2012. [48] O. Ethelbert, F.F. Moghaddam, P. Wieder, R. Yahyapour, A JSON token-based authentication and access management schema for cloud saas applications, in: Proceedings of the 5th IEEE International Conference on Future Internet of Things and Cloud, Prague, Czech Republic, August 21-23, 2017, IEEE, 2017, pp. 47–53. [49] A. Cimmino, N. Andreadou, A. Fernández-Izquierdo, C. Patsonakis, A.C. Tsolakis, A. Lucas, D. Ioannidis, E. Kotsakis, D. Tzovaras, R. García-Castro, Semantic interoperability for DR schemes employing the SGAM framework, in: Proceedings of the 2020 International Conference on Smart Energy Systems and Technologies, Istanbul, Turkey, 7-9 September 2020, IEEE, 2020, pp. 1–6. [50]A. Cimmino a,J. Cano-Benito a, A. Fernández-Izquierdo a, C. Patsonakis b, A. C. Tsolakis b, R. García-Castro a, D. Ioannidis b, D. Tzovaras b : A scalable, secure, and semantically interoperable client for cloud-enabled Demand Response, Future Generation Computer Systems 141 (2023) 54–66 . [51] L. Daniele, F. den Hartog, J. Roes, Created in close interaction with the industry: the smart appliances reference (SAREF) ontology, in: Proceedings of the 7th International Workshop Formal Ontologies Meet Industries, Berlin, Germany, August 5, 2015, Springer, 2015, pp. 100–112. [52] D.G. Pereira, A. Afonso, F.M. Medeiros, Overview of Friedman’s test and post-hoc analysis, Comm. Statist. Simulation Comput. 44 (10) (2015) 2636–2653.