Wireless Personal Communications

, Volume 102, Issue 4, pp 3041–3053 | Cite as

The Probabilistic Model and Forecasting of Power Load Based on Variational Bayesian Expectation Maximization and Relevance Vector Machine

  • Wegen GaoEmail author
  • Qigong Chen
  • Yuan Ge
  • YiQing Huang


As the surging demands of secure power supply and reliable power system, the power load approximation and forecasting are becoming more significant and more important. Different from the current research work, we integrate power load approximation and forecasting based on the Gaussian mixture model and relevance vector machine. In order to estimate the parameters of GMM, the variational bayesian expectation maximization algorithm are employed. Based on the estimation results, the relevance vector machine and bayesian regression model are built to forecast the power load and its labels. 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 Gaussian mixture model (GMM) Variational bayesian expectation maximization (VBEM) Relevance vector machine (RVM) Bayesian regression model 



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

  • Wegen Gao
    • 1
    • 2
    Email author
  • Qigong Chen
    • 2
  • Yuan Ge
    • 2
  • YiQing Huang
    • 2
  1. 1.School of Internet of Things EngineeringJiangnan UniversityWuxiChina
  2. 2.AnHui Polytechnic UniversityWuHuChina

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