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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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  • DOI: 10.1007/978-3-030-36778-7_17
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Correspondence to Mouad Bahij .

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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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