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Cluster Computing
Published in: Cluster Computing 4/2019

14-02-2018

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

Authors: Wengen Gao, Qigong Chen, Yuan Ge, YiQing Huang

Published in: Cluster Computing | Special Issue 4/2019

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Abstract

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.
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Metadata
Title
The probabilistic model and forecasting of power load based on JMAP-ML and Gaussian process
Authors
Wengen Gao
Qigong Chen
Yuan Ge
YiQing Huang
Publication date
14-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 4/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-1927-3

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