Energy Consumption Forecasting in Industrial Sector Using Machine Learning Approaches

  • Mouad BahijEmail author
  • Mohamed Cherkaoui
  • Moussa Labbadi
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


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.


Electricity consumption Prediction Machine learning LR SVM ANN 


  1. 1.
    Fazil, K., Cengiz, T., Ertugrul, C., Firat, H.: Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int. J. Electr. Power Energy Syst. 67, 431–438 (2015)CrossRefGoogle Scholar
  2. 2.
    Saravanan, S., Mahesh, K., Karunanithi, K.: Prediction of India’s electricity consumption in industrial sector using soft computing techniques. In: IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), Chennai, India, pp. 1990–1994 (2017)Google Scholar
  3. 3.
    Jaouhari, S., Jelaidi, M., El Hak, R.N.: Tendances de l’efficacité énergétique au MAROC. Technical report, MEDENER projet (2013)Google Scholar
  4. 4.
    Kavitha, S., Varuna S., Ramya, R.: A comparative analysis on linear regression and support vector regression. In: Online International Conference on Green Engineering and Technologies (IC-GET), Coimbatore, India, pp. 1–5 (2016)Google Scholar
  5. 5.
    Chakib, C., Mohammed, O.: Sensorless control of the PMSG in WECS using artificial neural network and sliding mode observer. In: International Symposium on Advanced Electrical and Communication Technologies (ISAECT), Rabat, Morocco, pp. 1–6 (2018)Google Scholar
  6. 6.
    Yixuan, W., Xingxing, Z., Yong, S., Liang, X., Song, P., Jinshun, W., Mengjie, H., Xiaoyun, Z.: A review of data-driven approaches for prediction and classification of building energy consumption. Renew. Sustain. Energy Rev. 82, 1027–1047 (2018)CrossRefGoogle Scholar
  7. 7.
    Ahmad, A.S., Hassan, M.Y., Abdullah, M.P., Rahman, H.A., Hussin, F., Abdullah, H., Saidur, R.: A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renew. Sustain. Energy Rev. 33, 102–109 (2014)CrossRefGoogle Scholar
  8. 8.
    Dandan, L., Qijun, C.: Prediction of building lighting energy consumption based on support vector regression. In: 9th Asian Control Conference (ASCC), Istanbul, Turkey, pp. 1–5 (2013)Google Scholar
  9. 9.
    Abdulwahed, S., Abdelaaziz, E.H.: Comparison of machine learning algorithms for the power consumption prediction case study of Tetouan city. In: 6th International Renewable and Sustainable Energy Conference (IRSEC), pp. 1–9 (2018)Google Scholar
  10. 10.
    Selvam, N., Karthikeyan, R.: Prediction of electricity consumption based on DT and RF: an application on USA country power consumption. In: International Conference on Electrical, Instrumentation and Communication Engineering (ICEICE), Karur, India, pp. 1–7 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mouad Bahij
    • 1
    Email author
  • Mohamed Cherkaoui
    • 1
  • Moussa Labbadi
    • 1
  1. 1.Engineering for Smart and Sustainable Systems Research Center, Mohammadia School of Engineers (EMI)Mohammed V UniversityRabatMorocco

Personalised recommendations