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A Knowledge-Based Energy Management Model that Supports Smart Metering Networks for Korean Residential Energy Grids

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Abstract

The various energy management technologies that are required in order to deliver effective energy demand responses have resulted from the integrated use of digital technologies with energy grids. Therefore, the core technologies for smart metering infrastructure are regarded as a key issue in the design of future energy grids. The proposed knowledge-based model that supports advanced metering networks is capable of estimating energy consumption according to the characteristics of residential buildings. The energy consumption data is analyzed according to the residential building’s properties, which can significantly affect the energy consumption pattern. Therefore, appropriately designed models for energy consumption patterns with respect to identifying each energy consumption feature’s potential impact can be applied to create smart metering networks for future energy grid environments. This study introduces a knowledge-based model that considers both the energy and building management profiles. Then, case studies for the estimation of the energy consumption are presented. The proposed model could be effectively utilized in managing the energy demand response process with respect to market prices and residential energy shortages, and it provides a good reference for designing energy demand response strategies in Korean residential energy grid environments.

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References

  1. Suh, D., & Chang, S. (2012). An energy and water resource demand estimation model for multi-family housing complexes in Korea. Energies, 5(11), 4497–4516.

    Article  Google Scholar 

  2. Suh, D., et al. (2012). An electricity energy and water consumption model for Korean Style Apartment buildings. In 12th international conference on Control, automation and systems (ICCAS2012). IEEE.

  3. Brown, R. (2008). Impact of smart grid on distribution system design. In Power and energy society general meeting-conversion and delivery of electrical energy in the 21st century. IEEE.

  4. Fang, X., Misra, S., Xue, G., & Yang, D. (2012). Smart grid—The new and improved power grid: A survey. Communications Surveys & Tutorials, IEEE, 14(4), 944–980.

    Article  Google Scholar 

  5. Smart grid, smart grid. http://en.wikipedia.org/wiki/Smart_grid.

  6. KEPCO. www.kepco.co.kr.

  7. U.S. Energy Information Administration. http://www.eia.gov/countries/cab.cfm?fips=ks.

  8. Lee, C., & Suh, S. (2010). A study on the greenhouse gas intensity of energy-intensive of building groups and regional in Korea. In Proceedings of SAREK conference, SAREK (pp. 1506–1511).

  9. Hong, W., Bae, H., Kim, S., & Choi, M. (1998). A study on the energy consumption by the life style of resident in apartment houses. Korean Journal of Architectural Institute of Korea, 14, 193–200.

    Google Scholar 

  10. Yoon, S. H., Jang, H. K., & Kim, Y. T. (2009). Analysis on the characteristics of thermal load classified by the household location in apartment house. Journal of the Architectural Institute of Korea, 25, 289–296.

    Google Scholar 

  11. Kim, J.-G., & Lee, B.-H. (2005). A study on the investigation of the amount of city gas use for the location of a major apartment building and households. Korean Journal of Architectural Institute of Korea, 21, 217–226.

    Google Scholar 

  12. Choi, I. Y., Cho, S. H., & Kim, J. T. (2012). Energy consumption characteristics of high-rise apartment buildings according to building shape and mixed use development. Energy and Buildings, 46, 123–131.

    Article  Google Scholar 

  13. Hong, T., Koo, C., & Park, S. (2012). A decision support model for improving a multi-family housing complex based on CO2 emission from gas energy consumption. Building and Environment, 52, 142–151.

    Article  Google Scholar 

  14. Liu, Y. (2012). Wireless sensor network applications in smart grid: Recent trends and challenges. International Journal of Distributed Sensor Networks, 2012, Article ID 492819, 8 pp. doi:10.1155/2012/492819.

  15. Boonsong, W., & Ismail, W. (2014). Wireless monitoring of household electrical power meter using embedded RFID with wireless sensor network platform. International Journal of Distributed Sensor Networks, 2014, Article ID 876914, 10 pp. doi:10.1155/2014/876914.

  16. K-MEG. http://www.k-meg.org/.

  17. Smart meter. http://en.wikipedia.org/wiki/Smart_meter.

  18. Onnara Portal. http://www.onnara.go.kr/.

  19. Korea Land Information System. http://klis.seoul.go.kr/.

  20. Apartment Management Information System. http://www.k-apt.go.kr/.

  21. Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design (2nd ed.). Denver: University of Colorado. http://www.amazon.com/Neural-Network-Design-Martin-Hagan/dp/0971732116/ref=sr_1_1?s=books&ie=UTF8&qid=1444829473&sr=1-1&keywords=neural+network+design+hagan.

    Google Scholar 

  22. Duda, R. O., Hart, P. E., & Stork, D. G. (1995). In pattern classification and scene analysis (2nd ed.). New York: Wiley.

    Google Scholar 

  23. Association of City Gas. http://www.citygas.or.kr/.

  24. Korea Meteorological Administration. http://www.kma.go.kr.

  25. Yu, W., Li, B., Lei, Y., & Liu, M. (2011). Analysis of a residential building energy consumption demand model. Energies, 4, 475–487.

    Article  Google Scholar 

  26. Ben-Nakhi, A. E., & Mahmoud, M. A. (2004). Cooling load prediction for buildings using general regression neural networks. Energy Conversion and Management, 45, 2127–2141.

    Article  Google Scholar 

  27. ASHRAE Guideline 14-2002. (2002). Measurement of energy and demand savings. American Society of Heating, Ventilating, and Air Conditioning Engineers, Atlanta, Georgia.

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Acknowledgments

This work was financially supported by Chonnam National University 2013 and prof. Ilmin Kim was also financially supported by Hansung University.

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Correspondence to Jinsul Kim.

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Suh, D., Kim, I. & Kim, J. A Knowledge-Based Energy Management Model that Supports Smart Metering Networks for Korean Residential Energy Grids. Wireless Pers Commun 94, 431–444 (2017). https://doi.org/10.1007/s11277-015-3090-y

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  • DOI: https://doi.org/10.1007/s11277-015-3090-y

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