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International Conference Europe Middle East & North Africa Information Systems and Technologies to Support Learning

EMENA-ISTL 2019: Innovation in Information Systems and Technologies to Support Learning Research pp 155–164Cite as

Energy Consumption Forecasting in Industrial Sector Using Machine Learning Approaches

Part of the Learning and Analytics in Intelligent Systems book series (LAIS,volume 7)

Abstract

This paper proposes an energy consumption forecasting Model by using the Machine Learning Methods (ML). On the one hand, the Linear Regression (LR), Support Vector Machine (SVM), Decision Tree (DT) and Artificial Neural Networks (ANN) are used as predictive tools. In the other hand, by using different attributes as inputs, the LR, SVM and ANN algorithms can predict energy consumption in the industry sector. In order to assess the performances of the proposed approaches, a simulation is carried out with Python software. The comparison between these methods proves the efficiency of LR approach.

Keywords

  • Electricity consumption
  • Prediction
  • Machine learning
  • LR
  • SVM
  • ANN

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Authors and Affiliations

  1. Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers (EMI), Mohammed V University, Rabat, Morocco

    Mouad Bahij, Mohamed Cherkaoui & Moussa Labbadi

Authors
  1. Mouad Bahij
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  2. Mohamed Cherkaoui
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  3. Moussa Labbadi
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Corresponding author

Correspondence to Mouad Bahij .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco

    Dr. Mohammed Serrhini

  2. Department of Computer Science, School of Management Sciences, Engineering and Technology, Atlantica University, Lisbon, Portugal

    Carla Silva

  3. Department of Computer Science, Taif University, College of Computers and Information Technology, Al Huwaya, Saudi Arabia

    Sultan Aljahdali

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Bahij, M., Cherkaoui, M., Labbadi, M. (2020). Energy Consumption Forecasting in Industrial Sector Using Machine Learning Approaches. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_17

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