A structural time series approach to modeling dynamic trends in power system data

A. R. Messina, Vijay Vittal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Structural time series models provide a natural framework for modeling time-varying trends in measured data. In this paper, a statistical framework for analyzing and estimating time-varying trends in measured data is developed. In this model, temporal patterns in measured data are modeled within a stochastic state space setting. Estimates of the time-varying parameters are then obtained using an optimal estimation method based on Kalman filters and associated smoothers. Both, synthetic and observational data are used to assess the predictive capability of the model. Results are compared to other detrending techniques in order to assess the potential of the methodology.

Original languageEnglish (US)
Title of host publication2012 IEEE Power and Energy Society General Meeting, PES 2012
DOIs
StatePublished - Dec 11 2012
Event2012 IEEE Power and Energy Society General Meeting, PES 2012 - San Diego, CA, United States
Duration: Jul 22 2012Jul 26 2012

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Other

Other2012 IEEE Power and Energy Society General Meeting, PES 2012
CountryUnited States
CitySan Diego, CA
Period7/22/127/26/12

Keywords

  • Hilbert-Huang
  • Kalman filter
  • Prony analysis
  • Trend identification
  • empirical mode decomposition

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Nuclear Energy and Engineering
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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