DOI: 10.17586/2226-1494-2015-15-2-285-292


M. A. Kolchin, A. A. Fensel, D. I. Mouromtsev, S. O. Popov, D. S. Pavlov, N. V. Klimov, A. A. Andreev, D. S. Garayzuev

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For citation: Kolchin M.A., Fensel A., Mouromtsev D.I., Popov S.O., Pavlov D.S., Klimov N.V., Andreev A.A., Garayzuev D.S. Energy consumption monitoring of Smart grid based on semantic stream data analysis. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2015, vol.15, no. 2, pp. 285–292.


Problem statement. Currently, the task of improving energy efficiency are addressed mainly through the creation of more efficient devices and appliances, the use of alternative energy sources, application of special additional equipment for power consumption control and other technological methods. All these solutions are quite expensive and often economically difficult to payback. At the same time, the issues of automated integrated analysis of existing data measuring equipment have been poorly known. But just these data contain all the necessary information for finding bottlenecks and failures in the equipment, leading to increased energy consumption.
Methods. Methods of web services creation are considered for current state monitoring of electrical networks using CQELS for static and streaming data integration of smart meters. RDF data model is used as the main way of data representation.
Results. The architecture of the energy monitoring system (Smart grid) based on semantic analysis of the streaming data is proposed. Ontology has been worked out, aimed at information domain model creation, which describes the measurement data and the possible situations for tracking by the system using semantic queries. An example of system operation is shown, and description of the visualization interfaces for streaming data and log of messages is given.
Practical relevance. Industrial application of the proposed approach will give the possibility to achieve significant energy efficiency through integrated analysis of smart meters data based on existing infrastructure of test and measurement equipment. An additional effect lies in the ability to create flexible Smart grid monitoring system and visualization of their states by an ontological approach to the domain modeling.

Keywords: ontologies, RDF, stream data semantic analysis, smart meter, data integration, visualization, Smart grid.

Acknowledgements. This work was partially financially supported by the Government of the Russian Federation (Grant 074-U01).

1. Brown R.E. Impact of smart grid on distribution system design. Proc. IEEE Power and Energy Society General Meeting: Conversion and Delivery of Electrical Energy in 21th Century. Pittsburgh, USA, 2008, art. 4596843. doi: 10.1109/PES.2008.4596843
2. Dautov R., Paraskakis I., Stannett M. Towards a framework for monitoring cloud application platforms as sensor networks. Cluster Computing, 2014, vol. 17, pp. 1203–1213. doi: 10.1007/s10586-014-0389-5
3. Barbieri D., Braga D., Ceri S., Della Valle E., Huang Y., Tresp V., Rettinger A., Wermser H. Deductive and inductive stream reasoning for semantic social media analytics. IEEE Intelligent Systems, 2010, vol. 25, no. 6, pp. 32–41. doi: 10.1109/MIS.2010.142
4. Celino I., Dell`Agilo D., Della Valle E., Huang Y., Lee T., Kim S., Tresp V. Towards BOTTARI: using stream reasoning to make sense of location-based micro-posts. Lecture Notes in Computer Science, 2012, vol. 7117, pp. 80–87. doi: 10.1007/978-3-642-25953-1_7
5. Wetz P., Trinh T.D., Do B.L., Anjomshoaa A., Kiesling E., Tjoa A.M. Towards an environmental information system for semantic stream data. Proc. 28th Conf. on Environmental Informatics – Informatics for Environmental Protection, Sustainable Development and Risk Management. Oldenburg, Germany, 2014, pp. 637–644.
6. Taylor K., Leidinger L. Ontology-driven complex event processing in heterogeneous sensor networks. Lecture Notes in Computer Science, 2011, vol. 6643, pp. 285–299. doi: 10.1007/978-3-642-21064-8_20
7. Gray A., Sadler J., Kit O., Kyzirakos K., Karpathiotakis M., Calbimonte J.-P., Page K. et. al. A semantic sensor web for environmental decision support applications. Sensors, 2011, vol. 11, no. 9, pp. 8855–8887. doi: 10.3390/s110908855
8. Heintz F., Kvarnstrom J., Doherty P. Bridging the sense-reasoning gap: DyKnow – Stream-based middleware for knowledge processing. Advanced Engineering Informatics, 2010, vol. 24, no. 1, pp. 14–26. doi: 10.1016/j.aei.2009.08.007
9. Banerjee S., Mukherjee D., Misra P. ‘What affects me?’ A smart public alert system based on stream reasoning. Proc. 7th Int. Conf. on Ubiquitous Information Management and Communication, ICUIMC 2013. Kota Kinabalu, Malaysia, 2013, art. 22. doi: 10.1145/2448556.2448578
10. Ruta M., Scioscia F., Di Sciascio E., Rotondi D., Piccione S. Semantic-based knowledge dissemination and extraction in smart environments. Proc. 27th Int. Conf. on Advanced Information Networking and Applications Workshops, WAINA 2013. Barcelona, Spain, 2013, pp. 1289–1294. doi: 10.1109/WAINA.2013.249
11. Tallevi-Diotallevi S., Kotoulas S., Foschini L., Lecue F., Corradi A. Real-time urban monitoring in Dublin using semantic and stream technologies. Lecture Notes in Computer Science, 2013, vol. 8219, pp. 178–194. doi: 10.1007/978-3-642-41338-4_12
12. Balduini M., Della Valle E., Dell`Aglio D., Tsytsarau M., Palpanas T., Confalonieri C. Social listening of city scale events using the streaming linked data framework. Lecture Notes in Computer Science, 2013, vol. 8219, pp. 1–16. doi: 10.1007/978-3-642-41338-4_1
13. Calbimonte J., Corcho O., Gray A. Enabling ontology-based access to streaming data sources. Lecture Notes in Computer Science, 2010, vol. 6496, pp. 96–111. doi: 10.1007/978-3-642-17746-0_7
14. Le-Phuoc D., Dao-Tran M., Xavier Parreira J., Hauswirth M. A native and adaptive approach for unified processing of linked streams and linked data. Lecture Notes in Computer Science, 2011, vol. 7031, pp. 370– 388.
15. Kolchin M., Mouromtsev D., Arustamov S. Demonstration: web-based visualisation and monitoring of smart meters using CQELS. Proc. 7th Int. Workshop on Semantic Sensor Network. Trentino, Italy, 2014, pp. 1–4.
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