The probabilistic model and forecasting of power load based on JMAP-ML and Gaussian process

  • Wengen Gao
  • Qigong Chen
  • Yuan Ge
  • YiQing Huang


The power load is a significant and important factor to the power system which can provide secure power supply and reliable power system. In this paper, we focus on the approximation and forecasting of the power load profiles based on the Gaussian mixture model. In order to infer the parameters of each component of GMM, we employ the joint maximum a posterior and maximum likelihood algorithm (JMAP-ML). Given the approximation of the power load, the Gaussian process regression method are utilized to forecast the power load in the time sequence. The simulation results show that the proposed algorithms can closely approximate the power load profiles and forecast the power load with lower errors.


Power load Approximation and forecasting Gaussian mixture model (GMM) JMAP-ML Gaussian process regression 



The work is supported by the Natural Science Foundation of China (61572032), Key Natural Science Research Project of Anhui Province (KJ2017A107).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Wengen Gao
    • 1
    • 2
  • Qigong Chen
    • 2
  • Yuan Ge
    • 2
  • YiQing Huang
    • 2
  1. 1.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  2. 2.School of Electrical EngineeringAnhui Polytechnic UniversityWuhuChina

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