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Design and Development of an Intelligent Ontology-Based Solution for Energy Management in the Home

  • Djamel Saba
  • Fatima Zohra Laallam
  • Houssem Eddine Degha
  • Brahim Berbaoui
  • Rachid Maouedj
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 801)

Abstract

The rapid development of the people life mode and the electrical devices use, including the search for the man to live in luxury and comfort, led to an uncontrollable consumption of electricity which leads to the allocation of funds to pay the bills for electricity consumed. This current problem requires severe treatment. To this end, we propose this solution to ensure energy saving without neglecting the well-being of individuals. The habitat is a complex, open and distributed systems, it includes a diversity of electrical equipment, which is characterized by a volume important information, which requires a good tool for the representation and treatment of knowledge. Thanks to this work, we try to offer a solution to all the problems mentioned above. This solution is based primarily on the Ontology and the Semantic Web to represent in a formal manner and explicit information that characterize the residential system and its environment. Finally, we chose OWL (Web Ontology Language) as a tool of knowledge representation, the rules SWRL (Semantic Web Rules Language) who have involved in the intelligent aspect of the solution and the “Protégé” software for the edition and the update of the data of the habitat and its environment. To evaluate the results of our solution, we applied our solution to the city of Adrar in Algeria.

Keywords

Energy saving Open and distributed system Ontology Semantic web OWL (ontology web language) SWRL (semantic web rules language) Protégé Smart home Decision-making 

References

  1. 1.
    Mekhalfi, M.L., Melgani, F., Zeggada, A., et al.: Recovering the sight to blind people in indoor environments with smart technologies. Expert Syst. Appl. 46, 129–138 (2016).  https://doi.org/10.1016/j.eswa.2015.09.054CrossRefGoogle Scholar
  2. 2.
    Coccia, M.: Driving forces of technological change: the relation between population growth and technological innovation analysis of the optimal interaction across countries. Technol. Forecast. Soc. Change 82, 52–65 (2014).  https://doi.org/10.1016/j.techfore.2013.06.001CrossRefGoogle Scholar
  3. 3.
    Byrd-Bredbenner, C., Martin-Biggers, J., Povis, G.A., et al.: Promoting healthy home environments and lifestyles in families with preschool children: HomeStyles, a randomized controlled trial. Contemp. Clin. Trials 64, 139–151 (2018).  https://doi.org/10.1016/j.cct.2017.10.012CrossRefGoogle Scholar
  4. 4.
    Yang, T., Clements-Croome, D., Marson, M.: Building energy management systems. In: Encyclopedia of Sustainable Technologies, pp. 291–309 (2017)Google Scholar
  5. 5.
    Scholl, M.V., Rocha, C.R.: Embedded SCADA for Small Applications. IFAC-PapersOnLine, vol. 49, 246–253 (2016).  https://doi.org/10.1016/j.ifacol.2016.10.559
  6. 6.
    Fabi, V., Spigliantini, G., Corgnati, S.P.: Insights on smart home concept and occupants’ interaction with building controls. In: di Torino, P. (ed.) Energy Procedia. Science Direct, pp. 759–769. Torino, Italy (2017)Google Scholar
  7. 7.
    Kamsu-Foguem, B., Tiako, P.F., Fotso, L.P., Foguem, C.: Modeling for effective collaboration in telemedicine. Telemat. Inform. 32, 776–786 (2015).  https://doi.org/10.1016/j.tele.2015.03.009CrossRefGoogle Scholar
  8. 8.
    Toschi, G.M., Campos, L.B., Cugnasca, C.E.: Home automation networks: a survey. Comput. Stand. Interf. 50, 42–54 (2017).  https://doi.org/10.1016/j.csi.2016.08.008CrossRefGoogle Scholar
  9. 9.
    Nehrenheim, E., Goldstein, M.I., Nehrenheim, E.: Introduction to renewable energy. In: Encyclopedia Anthropology Elsevier, pp. 405–406 (2018)Google Scholar
  10. 10.
    Sonelgaz: Présentation du groupe. 1 (2017)Google Scholar
  11. 11.
    Devaux, M., Lamanna, M.: The rise and early history of the term ontology (1606-1730). Quaestio 9, 173–208 (2009).  https://doi.org/10.1484/J.QUAESTIO.1.100702CrossRefGoogle Scholar
  12. 12.
    Neches, R., Fikes, R.E., Finin, T., et al.: Enabling technology for knowledge sharing. AI Mag. 12, 36 (1991).  https://doi.org/10.1609/aimag.v12i3.902CrossRefGoogle Scholar
  13. 13.
    Gruber, T.R.: A translation approach to portable ontology specifications. Knowl. Acquis. 5, 199–220 (1993).  https://doi.org/10.1006/knac.1993.1008CrossRefGoogle Scholar
  14. 14.
    Borst, W.N.: Construction of engineering ontologies for knowledge sharing and reuse. University of Twente (1997)Google Scholar
  15. 15.
    Studer, R., Benjamins, V.R., Fensel, D.: Knowledge engineering: principles and methods. Data Knowl. Eng. 25, 161–197 (1998).  https://doi.org/10.1016/S0169-023X(97)00056-6CrossRefzbMATHGoogle Scholar
  16. 16.
    Guarino, N., Giaretta, P.: Ontologies and knowledge bases: towards a terminological clarification. Towar Very Large Knowl. Bases Knowl. Build Knowl. Shar. 1, 25–32 (1995).  https://doi.org/10.1006/ijhc.1995.1066CrossRefGoogle Scholar
  17. 17.
    Swartout, B., Patil, R., Kevin Knight, T.R.: Toward distributed use of large-scale ontologies. In: Gaines, B.R., Musen, M. (eds.) Proceedings Tenth Knowledge Acquisition for Knowledge-Based Systems, pp. 138–148. Banff, Alberta, Canada (1996). http://www.aaai.org
  18. 18.
    Gomez-Perez, A.: Survey on ontology development tools. OGAI J. (Oesterreichische Gesellschaft fuer Artif Intell) 22, 6–16 (2003)Google Scholar
  19. 19.
    W3C: Standards—W3C. Standards 1–5 (2017)Google Scholar
  20. 20.
    Hendler, J., Berners-Lee, T.: From the semantic web to social machines: a research challenge for AI on the world wide web. Artif. Intell. 174, 156–161 (2010).  https://doi.org/10.1016/j.artint.2009.11.010MathSciNetCrossRefGoogle Scholar
  21. 21.
    Chavarriaga, E., Jurado, F., Díez, F.: An approach to build XML-based domain specific languages solutions for client-side web applications. Comput Lang. Syst. Struct. 49, 133–151 (2017).  https://doi.org/10.1016/j.cl.2017.04.002CrossRefGoogle Scholar
  22. 22.
    Harrington, J.L.: XML Support, 4th. Relational Database Des Implement (2016).  https://doi.org/10.1016/b978-0-12-804399-8.00026-0
  23. 23.
    Horrocks, I., Patel-schneider, P.F., Boley, H., et al.: SWRL : A semantic web rule language combining OWL and RuleML. W3C 1 (2004)Google Scholar
  24. 24.
    Saba, D., Laallam, F.Z., Hadidi, A.E., Berbaoui, B.: Contribution to the management of energy in the systems multi renewable sources with energy by the application of the multi agents systems “MAS.” In: Energy Procedia. Proceeding International Conference Technology Materials and Renewable Energy, Environmental Sustainability, pp. 616–623. Elsevier, Beirut–Lebanon (2015)Google Scholar
  25. 25.
    Stanford: Ontolingua Home Page, vol. 1 (2017). http://www.ksl.stanford.edu
  26. 26.
    Swartout, B., Patil, R., Knight, K., Russ, T.: Ontosaurus: a tool for browsing and editing ontologies. USC/Information Sci Inst 1 (2017)Google Scholar
  27. 27.
    Standford University: Protégé, pp. 1–10 (2017)Google Scholar
  28. 28.
    Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11, 93–136 (1996). 10.1.1.111.5903Google Scholar
  29. 29.
    Corcho, O., Fernández-López, M., Gómez-Pérez, A.: Methodologies, tools and languages for building ontologies. Where is their meeting point? Data Knowl. Eng. 46, 41–64 (2003).  https://doi.org/10.1016/S0169-023X(02)00195-7CrossRefGoogle Scholar
  30. 30.
    Noy, N.F., McGuinness, D.L.: Ontology development 101: a guide to creating your first ontology. Stanford Knowl. Syst. Lab 1 (2001)Google Scholar
  31. 31.
    Bilgin, G., Dikmen, I., Birgonul, M.T.: Ontology evaluation: an example of delay analysis. Proc. Eng. 85, 61–68 (2014).  https://doi.org/10.1016/j.proeng.2014.10.529CrossRefGoogle Scholar
  32. 32.
    Miksa, T., Rauber, A.: Using ontologies for verification and validation of workflow-based experiments. Web Semant. Sci. Serv. Agents World Wide Web 43, 25–45 (2017).  https://doi.org/10.1016/j.websem.2017.01.002CrossRefGoogle Scholar
  33. 33.
    Wu, D., Håkansson, A.: A method of identifying ontology domain. Proc. Comput. Sci. 35, 504–513 (2014).  https://doi.org/10.1016/j.procs.2014.08.131CrossRefGoogle Scholar
  34. 34.
    Suárez-Figueroa, M.C., Gómez-Pérez, A., Fernández-López, M.: Scheduling ontology development projects. Data Knowl. Eng. 102, 1–21 (2016).  https://doi.org/10.1016/j.datak.2015.11.004CrossRefGoogle Scholar
  35. 35.
    Saba, D., Zohra Laallam, F., Belmili, H., et al.: Development of an ontology-based generic optimisation tool for the design of hybrid energy systems development of an ontology-based generic optimisation tool for the design of hybrid energy systems. Int. J. Comput. Appl. Technol. (2017).  https://doi.org/10.1504/ijcat.2017.084773
  36. 36.
    Saba, D., Laallam, F.Z., Hadidi, A.E., Berbaoui, B.: Optimization of a multi-source system with renewable energy based on ontology. In: Energy Procedia. International Conference on Environment and Renewable Energy, Environmental Sustainability, pp. 608–615. Elsevier, Beirut–Lebanon (2015)Google Scholar
  37. 37.
    Han, J., Jeong, Y.-K., Lee, I.: A rule-based ontology reasoning system for context-aware building energy management. In: 2015 IEEE International Conference Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 2134–2142. IEEE (2015)Google Scholar
  38. 38.
    Chahuara, P., Portet, F., Vacher, M.: Context-aware decision making under uncertainty for voice-based control of smart home. Expert Syst. Appl. 75, 63–79 (2017).  https://doi.org/10.1016/j.eswa.2017.01.014CrossRefGoogle Scholar
  39. 39.
    Bonino, D., Corno, F.: DogOnt—ontology modeling for intelligent domotic environments. In: Sheth, A.P., Staab, S., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) International Semantic Web Conference, ISWC 2008 Semantic Web—ISWC 2008. Pp. 790–803. Springer, Berlin, Heidelberg, Karlsruhe, Germany (2008)Google Scholar
  40. 40.
    Homes, E.S., Cheong, Y., et al.: An ontology-based reasoning approach towards. In: IEEE. 2011 IEEE Consumer Communications and Networking Conference, pp. 850–854. IEEE (2011)Google Scholar
  41. 41.
    Gruber, T.R.: Technical report KSL 92-71 Revised April 1993. A translation approach to portable ontology specifications by a translation approach to portable ontology specifications. Knowl. Creat. Diffus. Util. 5, 199–220 (1993).  https://doi.org/10.1006/knac.1993.1008CrossRefGoogle Scholar
  42. 42.
    Angsuchotmetee, C., Chbeir, R., Cardinale, Y.: MSSN-Onto: an ontology-based approach for flexible event processing in multimedia sensor networks. Future Gen. Comput. Syst. (2018).  https://doi.org/10.1016/j.future.2018.01.044
  43. 43.
    Andrzej, U., Jeffrey, M.B., Renia, J.: IHMC ontology and policy management: KAoS core ontology. In: Lecture Notes in Computer Science. Trust Management. Second International Conference, iTrust 2004, pp 16–26. Oxford, UK, DBLP, iTrust 2004, Oxford (2004)Google Scholar
  44. 44.
    Abanda, F.H., Tah, J.H.M., Duce, D.: PV-TONS: a photovoltaic technology ontology system for the design of PV-systems. Eng. Appl. Artif. Intell. 26, 1399–1412 (2013).  https://doi.org/10.1016/j.engappai.2012.10.010CrossRefGoogle Scholar
  45. 45.
    Bermudez-Edo, M., Elsaleh, T., Barnaghi, P., Taylor, K.: IoT-Lite: a lightweight semantic model for the internet of things. In: 2016 IEEE Conference Ubiquitous Intelligent Computing, July 2016, pp. 1–8. University surrey (2016)Google Scholar
  46. 46.
    URERMS-EPST-CDER: Division de Conversion Photovoltaïque (DCPV)—URER.MS. URERMS-EPST-CDER 1 (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Djamel Saba
    • 1
    • 2
  • Fatima Zohra Laallam
    • 2
  • Houssem Eddine Degha
    • 2
  • Brahim Berbaoui
    • 1
  • Rachid Maouedj
    • 1
  1. 1.Unité de Recherche en Energies Renouvelables en Milieu Saharien, URER-MS, Centre de Développement des Energies Renouvelables, CDERAdrarAlgeria
  2. 2.Lab.Laboratoire de l’Intelligence Artificielle et les Technologies de l’Information, Fac.Faculté des Nouvelles Technologies de l’Information et de la CommunicationUniversité Kasdi Merbah OuarglaOuarglaAlgeria

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