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Using the ensemble Kalman filter for electricity load forecasting and analysis

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  • Takeda, Hisashi
  • Tamura, Yoshiyasu
  • Sato, Seisho

Abstract

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.

Suggested Citation

  • Takeda, Hisashi & Tamura, Yoshiyasu & Sato, Seisho, 2016. "Using the ensemble Kalman filter for electricity load forecasting and analysis," Energy, Elsevier, vol. 104(C), pages 184-198.
  • Handle: RePEc:eee:energy:v:104:y:2016:i:c:p:184-198
    DOI: 10.1016/j.energy.2016.03.070
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    References listed on IDEAS

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    Cited by:

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    4. repec:eee:rensus:v:81:y:2018:i:p1:p:1484-1512 is not listed on IDEAS
    5. repec:eee:energy:v:148:y:2018:i:c:p:775-788 is not listed on IDEAS
    6. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    7. repec:gam:jeners:v:10:y:2017:i:8:p:1186-:d:107924 is not listed on IDEAS
    8. He, Yaoyao & Xu, Qifa & Wan, Jinhong & Yang, Shanlin, 2016. "Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function," Energy, Elsevier, vol. 114(C), pages 498-512.
    9. repec:gam:jeners:v:10:y:2017:i:11:p:1713-:d:116523 is not listed on IDEAS

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