Skip to main content
SpringerLink
Log in

Analysis of the Publications on Ontology-Based Smart Grid Applications: A Bird’s Eye View

  • Conference paper
  • First Online:
Applied Computing to Support Industry: Innovation and Technology (ACRIT 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1174))

  • 774 Accesses

Abstract

Smart grid ontology is a field of research recently viewed from the field of smart grid research as well as from the field of ontology research. The integration between smart grid and ontology is expected to enable the sharing of ontology among applications and stakeholders. By using the same language, the common problems that occur during applications interoperability will be prevented and solved. This brief paper intended to help researchers get an overview of the current smart grid ontology research in the world and also to know the connection between smart grid ontology paper publications with research attention leading to smart grid ontology. To do so, we conduct a comprehensive survey on three databases which are IEEE Xplore, Springer, and Elsevier ScienceDirect. From the survey, we obtained the number of smart grid ontology research studies, their citations, and the country. From the number of papers and their citations, it can be known which country has an interest in smart grid ontology research. From the results of this review, it was found that countries in continental Europe are the most widely issued publication about smart grid ontology. Similarly, the papers that cite largely dominated by countries in continental Europe.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dogdu, E., et al.: Ontology-centric data modelling and decision support in smart grid applications a distribution service operator perspective. In: 2014 IEEE International Conference on Intelligent Energy and Power Systems (IEPS), pp. 198–204. IEEE, June 2014

    Google Scholar 

  2. Schachinger, D., Kastner, W., Gaida, S.: Ontology-based abstraction layer for smart grid interaction in building energy management systems. In: 2016 IEEE International Energy Conference (ENERGYCON), pp. 1–6. IEEE, April 2016

    Google Scholar 

  3. Hippolyte, J.L., et al.: Ontology-based demand-side flexibility management in smart grids using a multi-agent system. In: 2016 IEEE International Smart Cities Conference (ISC2), pp. 1–7. IEEE, September 2016

    Google Scholar 

  4. Schumilin, A., Stucky, K.U., Sinn, F., Hagenmeyer, V.: Towards ontology-based network model management and data integration for smart grids. In: 2017 Workshop on Modeling and Simulation of Cyber-Physical Energy Systems (MSCPES), pp. 1–6. IEEE, April 2017

    Google Scholar 

  5. Santodomingo, R., Rohjans, S., Uslar, M., Rodriguez-Mondejar, J.A., Sanz-Bobi, M.A.: Ontology matching system for future energy smart grids. Eng. Appl. Artif. Intell. 32, 242–257 (2014)

    Article  Google Scholar 

  6. López, G., Custodio, V., Moreno, J.I., Sikora, M., Moura, P., Fernández, N.: Modeling smart grid neighborhoods with the ENERsip ontology. Comput. Ind. 70, 168–182 (2015)

    Article  Google Scholar 

  7. Daqing, X., Yinghua, H.: An adaptive data management model for smart grid. In: 2015 8th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 126–129. IEEE, June 2015

    Google Scholar 

  8. Barriquello, C.H., Garcia, V.J., Schmitz, M., Bernardon, D.P., Fonini, J.S.: A decision support system for planning and operation of maintenance and customer services in electric power distribution systems. In: System Reliability. InTech (2017)

    Google Scholar 

  9. Santos, G., Pinto, T., Vale, Z.: Ontologies for the interoperability of heterogeneous multi-agent systems in the scope of power and energy systems. In: De la Prieta, F., et al. (eds.) PAAMS 2017. AISC, vol. 619, pp. 300–301. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-61578-3_42

    Chapter  Google Scholar 

  10. Gao, W., Farahani, M.R.: Generalization bounds and uniform bounds for multi-dividing ontology algorithms with convex ontology loss function. Comput. J. 60(9), 1289–1299 (2017)

    MathSciNet  Google Scholar 

  11. Gao, W., Zhu, L.: Gradient learning algorithms for ontology computing. Comput. Intell. Neurosci. 2014, 24 (2014)

    Article  Google Scholar 

  12. Gao, Y., Gao, W.: Ontology sparse vector learning based on accelerated first-order method. Open Cybern. Syst. J. 9, 657–662 (2015)

    Article  Google Scholar 

  13. Gao, W., Zhu, L., Wang, K.: Ranking based ontology scheming using eigenpair computation. J. Intell. Fuzzy Syst. 31(4), 2411–2419 (2016)

    Article  Google Scholar 

  14. Butzin, B., Golatowski, F., Timmermann, D.: A survey on information modeling and ontologies in building automation. In: IECON 2017-43rd Annual Conference of the IEEE Industrial Electronics Society,  pp. 8615–8621. IEEE, October 2017

    Google Scholar 

  15. Zanabria, C., Tayyebi, A., Pröstl Andrén, F., Kathan, J., Strasser, T.: Engineering support for handling controller conflicts in energy storage systems applications. Energies 10(10), 1595 (2017)

    Article  Google Scholar 

  16. Hernández, O., Guinea, D., Santos, M.: Semantic sensors: a proposal from smart building to smart city model. In: Proceedings of the Mexican International Conference on Computer Science, 2nd. Workshop on Semantic Web and Linked Open Data, Oaxaca, Mexico, vol. 35, November 2014

    Google Scholar 

  17. Santodomingo, R., Uslar, M., Rodríguez-Mondéjar, J.A., Sanz-Bobi, M.A.: Rule-based data transformations in electricity smart grids. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds.) RuleML 2015. LNCS, vol. 9202, pp. 447–455. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21542-6_29

    Chapter  Google Scholar 

  18. Essayeh, A., Abed, M.: Towards ontology matching based system through terminological, structural and semantic level. Proc. Comput. Sci. 60, 403–412 (2015)

    Article  Google Scholar 

  19. Gong, S., Gao, W.: Ontology Learning Algorithm via WMW Optimization Model. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp. 431–434. IEEE, December 2016

    Google Scholar 

  20. Hamdaqa, M., Tahvildari, L.: Prison break: a generic schema matching solution to the cloud vendor lock-in problem. In: 2014 IEEE 8th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA), pp. 37–46. IEEE, September 2014

    Google Scholar 

  21. Lan, M.H., Xu, J., Gao, W.: Ontology feature extraction via vector learning algorithm and applied to similarity measuring and ontology mapping. IAENG Int. J. Comput. Sci. 43(1), 10–19 (2016)

    Google Scholar 

  22. Lan, M., Xu, J., Gao, W.: Ontology similarity computation and ontology mapping using distance matrix learning approach. IAENG Int. J. Comput. Sci. 45(1), 164–176 (2018)

    Google Scholar 

  23. Rosinger, M.: Visualisierung als Projektcockpit im Smart Grid Projekt DISCERN (2016). https://www.discern.eu/datas/Messen_Bewerten_und_Vergleichen_Visualisierung_als_Projektcockpit_im_Smart_Grid_Projekt_DISCERN_OTTI_2016.pdf

  24. Wei, G.A.O., Jianzhang, W.U., Linli, Z.H.U.: Ontology similarity measuring and ontology mapping algorithms based on proximal technologies. Int. J. Simul.-Syst. Sci. Technol. 17(43), 1–9 (2016)

    Google Scholar 

  25. Yan, L., Li, Y.J., Yang, X., Gao, W.: Gradient descent technology for sparse vector learning in ontology algorithms. J. Disc. Math. Sci. Crypt. 19(3), 753–775 (2016)

    Google Scholar 

  26. Ravikumar, G., Khaparde, S.A.: A common information model oriented graph database framework for power systems. IEEE Trans. Power Syst. 32(4), 2560–2569 (2017)

    Article  Google Scholar 

  27. Zhu, L., Pan, Y., Farahani, M.R., Gao, W.: Magnitude preserving based ontology regularization algorithm. J. Intell. Fuzzy Syst. 33(5), 3113–3122 (2017)

    Article  Google Scholar 

  28. Küçük, D., Küçük, D.: OntoWind: An Improved and Extended Wind Energy Ontology (2018). arXiv preprint arXiv:1803.02808

  29. Balabanov, M.S., Baboshkina, S.V., Hamitov, R.N.: Ecological aspects in energy saving policy at the stage of creation in Russia of intelligent power systems with an actively adaptive network. In: Proceedings of the Tomsk Polytechnic University, vol. 326 (2015)

    Google Scholar 

  30. Marsal-Llacuna, M.L.: The standards evolution: a pioneering meta-standard framework architecture as a novel self-conformity assessment and learning tool. Comput. Stan. Interfaces 55, 106–115 (2018)

    Article  Google Scholar 

  31. Mountasser, I., Ouhbi, B., Frikh, B.: Hybrid large-scale ontology matching strategy on big data environment. In: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services, pp. 282–287. ACM, November 2016

    Google Scholar 

  32. Teixeira, B., Pinto, T., Silva, F., Santos, G., Praça, I., Vale, Z.: Multi-agent decision support tool to enable interoperability among heterogeneous energy systems. Appl. Sci. 8(3), 328 (2018)

    Article  Google Scholar 

  33. Ferrante, P., La Gennusa, M., Peri, G., Porretto, V., Sanseverino, E.R., Vaccaro, V.: On the architectural and energy classification of existing buildings: a case study of a district in the city of Palermo. In: 2016 IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), pp. 1–6. IEEE, June 2016

    Google Scholar 

  34. Guarino, F., Tumminia, G., Longo, S., Mistretta, M., Bilotta, R., Cellura, M.: Energy planning methodology of net-zero energy solar neighborhoods in the mediterranean basin. Sci. Technol. Built Environ. 22(7), 928–938 (2016)

    Article  Google Scholar 

  35. Karakosta, C., Flamos, A.: Managing climate policy information facilitating knowledge transfer to policy makers. Energies 9(6), 454 (2016)

    Article  Google Scholar 

  36. Tuballa, M.L., Abundo, M.L.: A review of the development of smart grid technologies. Renew. and Sustain. Energy Rev. 59, 710–725 (2016)

    Article  Google Scholar 

  37. Billanes, J.D., Ma, Z., Jørgensen, B.N.: Energy flexibility in the power system: challenges and opportunites in Philippines. J. Energy Power Eng. 11, 597–604 (2017)

    Google Scholar 

  38. Cuenca, J., Larrinaga, F., Curry, E.: A unified semantic ontology for energy management applications. In: Joint Proceedings of the Web Stream Processing workshop (WSP 2017) and the 2nd International Workshop on Ontology Modularity, Contextuality, and Evolution (WOMoCoE 2017), pp. 86–97 (2017)

    Google Scholar 

  39. Reynolds, J., Rezgui, Y., Hippolyte, J.L.: Upscaling energy control from building to districts: Current limitations and future perspectives. Sustain. Cities Soc. 35, 816–829 (2017)

    Article  Google Scholar 

  40. Joint Research Center Smart Electricity Systems and Interoperability. http://ses.jrc.ec.europa.eu/. Accessed May 2018

  41. Schachinger, D., Kastner, W.: Ontology-based generation of optimization problems for building energy management. In: 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), pp. 1–8. IEEE, September 2017

    Google Scholar 

  42. Albalushi, A., Khan, R., McLaughlin, K., Sezer, S.: Ontology-based approach for malicious behaviour detection in synchrophasor networks. In: Power & Energy Society General Meeting, 2017 IEEE, pp. 1–5. IEEE, July 2017

    Google Scholar 

Download references

Acknowledgments

This work is sponsored by Tenaga Nasional Berhad (TNB) under TNB R&D Seeding Fund Scheme No. TC-RD-18-19. We also gratefully appreciate Universiti Tenaga Nasional & Uniten R&D for securing and managing the fund.

Author information

Authors and Affiliations

  1. Insitute of Informatics and Computing Energy, Universiti Tenaga Nasional, Kajang, Malaysia

    Moamin A. Mahmoud, Andino Maseleno & Alicia Y. C. Tang

  2. College of Computing and Informatics, Universiti Tenaga Nasional, Kajang, Malaysia

    Fung-Cheng Lim & Hairoladenan Bin Kasim

  3. Tenaga Nasional Berhad, Kuala Lumpur, Malaysia

    Christine Yong

Authors
  1. Moamin A. Mahmoud
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andino Maseleno
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alicia Y. C. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fung-Cheng Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hairoladenan Bin Kasim
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christine Yong
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Moamin A. Mahmoud , Andino Maseleno or Alicia Y. C. Tang .

Editor information

Editors and Affiliations

  1. Al Maaref University College, Ramadi, Iraq

    Mohammed I. Khalaf

  2. Liverpool John Moores University, Liverpool, UK

    Dhiya Al-Jumeily

  3. University of Liverpool, Liverpool, UK

    Alexei Lisitsa

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mahmoud, M.A., Maseleno, A., Tang, A.Y.C., Lim, FC., Kasim, H.B., Yong, C. (2020). Analysis of the Publications on Ontology-Based Smart Grid Applications: A Bird’s Eye View. In: Khalaf, M., Al-Jumeily, D., Lisitsa, A. (eds) Applied Computing to Support Industry: Innovation and Technology. ACRIT 2019. Communications in Computer and Information Science, vol 1174. Springer, Cham. https://doi.org/10.1007/978-3-030-38752-5_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-38752-5_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-38751-8

  • Online ISBN: 978-3-030-38752-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation