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IJMLC 2022 Vol.12(2): 68-72 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2022.12.2.1081

Prediction of Time Series Analysis of Power Usage Based on Rstudio

Cuihua Tian, Mugisha Theophile, Jianhong Qian, Yiping Zhang, and Zhigang Hu

Abstract—Use the historical data of household energy consumption to design a valuable information model for predicting future demand. The cluster analysis of the data set of the transmission power distribution system shows the proportion of different consumption behaviors and the level of power consumption in different periods, and effectively predicts the power consumption of users. It can help power companies and users to control the load during peak hours of power demand transfer to off-peak hours.

Index Terms—Big data, data analytics, RStudio language, predictive model.

Cuihua Tian is with the School of Computer and Information Engineering, Xiamen University of Technology, China (Corresponding author: Jianhong Qian; e-mail: qianjianhong2020@outlook.com, tiancuihua@xmut.edu.cn).

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Cite: Cuihua Tian, Mugisha Theophile, Jianhong Qian, Yiping Zhang, and Zhigang Hu, "Prediction of Time Series Analysis of Power Usage Based on Rstudio," International Journal of Machine Learning and Computing vol. 12, no. 2, pp. 68-72, 2022.

Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

General Information

  • E-ISSN: 2972-368X
  • Abbreviated Title: Int. J. Mach. Learn.
  • Frequency: Quaterly
  • DOI: 10.18178/IJML
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals LibraryCNKI.
  • E-mail: ijml@ejournal.net


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