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Bad Data Detection

  • Kianoosh G. Boroojeni
  • M. Hadi Amini
  • S. S. Iyengar
Chapter

Abstract

One of the major roles that Smart Grid has promised to play is to provide a power to satisfy power demand with environmentally-friendly source of energy while maintaining an acceptable level of adequacy and security that traditional systems promise. As a result, there have been many efforts to develop estimation algorithms of the power system states which are the core of the time-sensitive grid management. In this chapter, we address auto-regressive load forecasting methods which play pivotal role in creating an accurate state estimator for the power grid management.

Keywords

Time Series Smart Grid Load Data Stationary Time Series Load Demand 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    K.G. Boroojeni, M.H. Amini, S. Bahrami, S.S. Iyengar, A.I. Sarwat, O. Karabasoglu, A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electr. Power Syst. Res. 142, 58–73 (2017)CrossRefGoogle Scholar
  2. 2.
    Theresa Hoang Diem NGO, Warner Bros. Entertainment Group, The Box–Jenkins methodology for time series models, SAS Global Forum, 2013Google Scholar
  3. 3.
    G.E.P. Box, G.M. Jenkins, Time-Series Analysis: Forecasting and Control (Holden-Day, CA, 1976)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Kianoosh G. Boroojeni
    • 1
  • M. Hadi Amini
    • 2
    • 3
  • S. S. Iyengar
    • 1
  1. 1.School of Computing and Information SciencesFlorida International UniversityMiamiUSA
  2. 2.SYSU-CMU Joint Institute of Engineering School of Electronics and Information TechnologySun Yat-sen UniversityGuangzhouChina
  3. 3.Department of Electrical and Computer EngineeringCarnegie Mellon UniversityPittsburghUSA

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