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Modeling of Consumption Data for Forecasting in Automated Metering Infrastructure (AMI) Systems

  • A. Jayanth BalajiEmail author
  • D. S. Harish Ram
  • Binoy B. Nair
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 466)

Abstract

The Smart Grid is a new paradigm that aims at improving the efficiency, reliability and economy of the power grid by integrating ICT infrastructure into the legacy grid networks at the generation, transmission and distribution levels. Automatic Metering Infrastructure (AMI) systems comprise the entire gamut of resources from smart meters to heterogeneous communication networks that facilitate two-way dissemination of energy consumption information and commands between the utilities and consumers. AMI is integral to the implementation of smart grid distribution services such as Demand Response (DR) and Distribution Automation (DA). The reliability of these services is heavily dependent on the integrity of the AMI data. This paper investigates the modeling of AMI data using machine learning approaches with the objective of load forecasting of individual consumers. The model can also be extended for detection of anomalies in consumption patterns introduced by false data injection attacks, electrical events and unauthorized load additions or usage modes.

Keywords

Automated metering infrastructure Smart grid Load forecasting Distribution side management Soft computing Artificial intelligence 

References

  1. 1.
    Meters, S.: Smart meter systems: a metering industry perspective. An Edison Electric Institute-Association of Edison Illuminating Companies-Utilities Telecom Council White Paper, A Joint Project of the EEI and AEIC Meter Committees, Edison Electric Institute (2011)Google Scholar
  2. 2.
    Siano, P.: Demand response and smart grids. A survey. Renew. Sustain. Energy Rev. 30, 461–478 (2014)CrossRefGoogle Scholar
  3. 3.
    Balakrishna, P. et al.: Analysis on AMI system requirements for effective convergence of distribution automation and AMI systems. In: 2014 6th IEEE Power India International Conference (PIICON) (2014)Google Scholar
  4. 4.
    Deng, P., Yang, L.: A secure and privacy-preserving communication scheme for advanced metering infrastructure. In: 2012 IEEE PES Innovative Smart Grid Technologies (ISGT) (2012)Google Scholar
  5. 5.
    Chen, J. et al.: A key management scheme for secure communications of advanced metering infrastructure. In: Communications in Computer and Information Science Applied Informatics and Communication, pp. 430–438 (2011)Google Scholar
  6. 6.
    Wang, W., Lu, Z.: Cyber security in the Smart Grid: Survey and challenges. Comput. Netw. 57(5), 1344–1371 (2013)CrossRefGoogle Scholar
  7. 7.
    Tasdighi, M. et al.: Residential microgrid scheduling based on smart meters data and temperature dependent thermal load modeling. IEEE Trans. Smart Grid. 5(1), 349–357 (2014)CrossRefGoogle Scholar
  8. 8.
    Rahman, M.A. et al.: A noninvasive threat analyzer for advanced metering infrastructure in smart grid. IEEE Trans. Smart Grid 4(1), 273–287 (2013)CrossRefGoogle Scholar
  9. 9.
    Guruprasad, S. et al.: A learning approach for identification of refrigerator load from aggregate load signal. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2014)Google Scholar
  10. 10.
    Chan, S. et al.: Load/price forecasting and managing demand response for smart grids: methodologies and challenges. IEEE Signal Process. Mag. 29(5), 68–85 (2012)CrossRefGoogle Scholar
  11. 11.
    Hong, T. et al.: Long term probabilistic load forecasting and normalization with hourly information. IEEE Trans. Smart Grid 5(1), 456–462 (2014)CrossRefGoogle Scholar
  12. 12.
    Kwac, J. et al.: Household energy consumption segmentation using hourly data. IEEE Trans. Smart Grid 5(1), 420–430 (2014)CrossRefGoogle Scholar
  13. 13.
    Krishna, V.B. et al.: PCA-based method for detecting integrity attacks on advanced metering infrastructure. In: Quantitative Evaluation of Systems Lecture Notes in Computer Science, pp. 70–85 (2015)Google Scholar
  14. 14.
    Hodrick, R., Prescott, E.: Postwar U.S. Business Cycles. In: Real Business Cycles A Reader, pp. 593–608 (1998)CrossRefGoogle Scholar
  15. 15.
    Nair, B.B., Mohandas, V.: An intelligent recommender system for stock trading. Intell. Decis. Technol. 9(3), 243–269 (2015)CrossRefGoogle Scholar
  16. 16.
    Nair, B.B., Mohandas, V.: Artificial intelligence applications in financial forecastinga survey and some empirical results. Intell. Decis. Technol. 9(2), 99–140 (2015)CrossRefGoogle Scholar
  17. 17.
    Nair, B.B., et al.: A stock trading recommender system based on temporal association rule mining. SAGE Open 5, 2 (2015)CrossRefGoogle Scholar
  18. 18.
    Gooijer, J.G.D., Hyndman, R.J.: 25 years of time series forecasting. Int. J. Forecast. 22(3), 443–473 (2006)Google Scholar

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© Springer International Publishing Switzerland 2016

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Authors and Affiliations

  • A. Jayanth Balaji
    • 1
    Email author
  • D. S. Harish Ram
    • 1
  • Binoy B. Nair
    • 1
  1. 1.Computing, Hardware Systems and Architectures Group, Department of Electronics and Communication Engineering, Amrita School of EngineeringAmrita Vishwa Vidyapeetham UniversityCoimbatoreIndia

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