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HMM Based Approach

  • Roberto Bonfigli
  • Stefano Squartini
Chapter
Part of the SpringerBriefs in Energy book series (BRIEFSENERGY)

Abstract

Approaches based on hidden Markov models (HMMs) have been devoted particular attention in the last years. AFAMAP (Additive Factorial Approximate Maximum a Posteriori) has been introduced in Kolter and Jaakkola to reduce the computational burden of FHMM. The algorithm bases its operation on additive and difference FHMM, and it constrains the posterior probability to require only one HMM change state at any given time.

Keywords

Hidden Markov Model Working state Power consumption Active power Factorial Hidden Markov Model Rest-of-the-world model Constrained optimization Reactive power Finite State Machine Footprint 

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Copyright information

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Roberto Bonfigli
    • 1
  • Stefano Squartini
    • 1
  1. 1.Marche Polytechnic UniversityVia Brecce Bianche 12Italy

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