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Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Author

Listed:
  • Mohamed Massaoudi

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 3263, Qatar
    Laboratoire Matériaux Molécules et Applications (LMMA) à l’IPEST, Carthage University, Tunis 1054, Tunisia)

  • Ines Chihi

    (Département Ingénierie, Faculté des Sciences, des Technologies et de Médecine, Campus Kirchberg, Université du Luxembourg, 1359 Luxembourg, Luxembourg
    Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, Tunisia
    National Engineering School of Bizerta, Carthage University, Tunis 7080, Tunisia)

  • Lilia Sidhom

    (Laboratory of Energy Applications and Renewable Energy Efficiency (LAPER), El Manar University, Tunis 1068, Tunisia
    National Engineering School of Bizerta, Carthage University, Tunis 7080, Tunisia)

  • Mohamed Trabelsi

    (Department of Electronic and Communications Engineering, Kuwait College of Science and Technology, Doha District, Block 4, Doha P.O. Box 27235, Kuwait)

  • Shady S. Refaat

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 3263, Qatar)

  • Fakhreddine S. Oueslati

    (Laboratoire Matériaux Molécules et Applications (LMMA) à l’IPEST, Carthage University, Tunis 1054, Tunisia)

Abstract

Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

Suggested Citation

  • Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3992-:d:587816
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    References listed on IDEAS

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    Cited by:

    1. Adam Krechowicz & Maria Krechowicz & Katarzyna Poczeta, 2022. "Machine Learning Approaches to Predict Electricity Production from Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-41, December.

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