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Using the ensemble Kalman filter for electricity load forecasting and analysis

Listed author(s):
  • Takeda, Hisashi
  • Tamura, Yoshiyasu
  • Sato, Seisho
Registered author(s):

    This paper proposes a novel framework for modeling electricity loads; it can be used for both forecasting and analysis. The framework combines the EnKF (ensemble Kalman filter) technique with shrinkage/multiple regression methods. First, SSMs (state-space models) are used to model the load structure, and then the EnKF is used for the estimation. Next, shrinkage/multiple linear regression methods are used to further enhance accuracy. The EnKF allows for the modeling of nonlinear systems in the SSMs, and this gives it great flexibility and detailed analytical information, such as the temperature response rate. We show that the forecasting accuracy of the proposed models is significantly better than that of the current state-of-the-art models, and this method also provides detailed analytical information.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216303140
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    Article provided by Elsevier in its journal Energy.

    Volume (Year): 104 (2016)
    Issue (Month): C ()
    Pages: 184-198

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    Handle: RePEc:eee:energy:v:104:y:2016:i:c:p:184-198
    DOI: 10.1016/j.energy.2016.03.070
    Contact details of provider: Web page: http://www.journals.elsevier.com/energy

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    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    3. Ghofrani, M. & Ghayekhloo, M. & Arabali, A. & Ghayekhloo, A., 2015. "A hybrid short-term load forecasting with a new input selection framework," Energy, Elsevier, vol. 81(C), pages 777-786.
    4. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    5. Ko, Chia-Nan & Lee, Cheng-Ming, 2013. "Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter," Energy, Elsevier, vol. 49(C), pages 413-422.
    6. Dordonnat, V. & Koopman, S.J. & Ooms, M. & Dessertaine, A. & Collet, J., 2008. "An hourly periodic state space model for modelling French national electricity load," International Journal of Forecasting, Elsevier, vol. 24(4), pages 566-587.
    7. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    8. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
    9. Bahrami, Saadat & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2014. "Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm," Energy, Elsevier, vol. 72(C), pages 434-442.
    10. Charlton, Nathaniel & Singleton, Colin, 2014. "A refined parametric model for short term load forecasting," International Journal of Forecasting, Elsevier, vol. 30(2), pages 364-368.
    11. Pielow, Amy & Sioshansi, Ramteen & Roberts, Matthew C., 2012. "Modeling short-run electricity demand with long-term growth rates and consumer price elasticity in commercial and industrial sectors," Energy, Elsevier, vol. 46(1), pages 533-540.
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