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An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting

Author

Listed:
  • Mohamed Trabelsi

    (Electronics and Communications Engineering Department, Kuwait College of Science and Technology, Doha P.O. Box 27235, Kuwait)

  • Mohamed Massaoudi

    (Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha 23874, Qatar
    Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA)

  • Ines Chihi

    (Department of Engineering, Faculty of Science, Technology and Medicine, University of Luxembourg, L-135 Luxembourg, Luxembourg)

  • Lilia Sidhom

    (LAPER, Faculty of Sciences of Tunis, El Manar University, Tunis 1068, Tunisia
    National Engineering School of Bizerta, Carthage University, Tunis 7035, Tunisia)

  • Shady S. Refaat

    (Engineering, and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK)

  • Tingwen Huang

    (Arts and Sciences Department, Texas A&M University at Qatar, Doha 23874, Qatar)

  • Fakhreddine S. Oueslati

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

Abstract

The integration of Photovoltaic (PV) systems requires the implementation of potential PV power forecasting techniques to deal with the high intermittency of weather parameters. In the PV power prediction process, Genetic Programming (GP) based on the Symbolic Regression (SR) model has a widespread deployment since it provides an effective solution for nonlinear problems. However, during the training process, SR models might miss optimal solutions due to the large search space for the leaf generations. This paper proposes a novel hybrid model that combines SR and Deep Multi-Layer Perceptron (MLP) for one-month-ahead PV power forecasting. A case study analysis using a real Australian weather dataset was conducted, where the employed input features were the solar irradiation and the historical PV power data. The main contribution of the proposed hybrid SR-MLP algorithm are as follows: (1) The training speed was significantly improved by eliminating unimportant inputs during the feature selection process performed by the Extreme Boosting and Elastic Net techniques; (2) The hyperparameters were preserved throughout the training and testing phases; (3) The proposed hybrid model made use of a reduced number of layers and neurons while guaranteeing a high forecasting accuracy; (4) The number of iterations due to the use of SR was reduced. The presented simulation results demonstrate the higher forecasting accuracy (reductions of more than 20% for Root Mean Square Error (RMSE) and 30 % for Mean Absolute Error (MAE) in addition to an improvement in the R 2 evaluation metric) and robustness (preventing the SR from converging to local minima with the help of the ANN branch) of the proposed SR-MLP model as compared to individual SR and MLP models.

Suggested Citation

  • Mohamed Trabelsi & Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Shady S. Refaat & Tingwen Huang & Fakhreddine S. Oueslati, 2022. "An Effective Hybrid Symbolic Regression–Deep Multilayer Perceptron Technique for PV Power Forecasting," Energies, MDPI, vol. 15(23), pages 1-14, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9008-:d:986844
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    References listed on IDEAS

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    1. Muhammad Hussain & Hussain Al-Aqrabi & Richard Hill, 2022. "Statistical Analysis and Development of an Ensemble-Based Machine Learning Model for Photovoltaic Fault Detection," Energies, MDPI, vol. 15(15), pages 1-14, July.
    2. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    3. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
    4. Ismail Shah & Faheem Jan & Sajid Ali & Tahir Mehmood, 2022. "Functional Data Approach for Short-Term Electricity Demand Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-14, June.
    5. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    6. Wen-Chi Kuo & Chiun-Hsun Chen & Sih-Yu Chen & Chi-Chuan Wang, 2022. "Deep Learning Neural Networks for Short-Term PV Power Forecasting via Sky Image Method," Energies, MDPI, vol. 15(13), pages 1-17, June.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    9. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    10. Ogliari, Emanuele & Dolara, Alberto & Manzolini, Giampaolo & Leva, Sonia, 2017. "Physical and hybrid methods comparison for the day ahead PV output power forecast," Renewable Energy, Elsevier, vol. 113(C), pages 11-21.
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    1. Mateusz Sumorek & Adam Idzkowski, 2023. "Time Series Forecasting for Energy Production in Stand-Alone and Tracking Photovoltaic Systems Based on Historical Measurement Data," Energies, MDPI, vol. 16(17), pages 1-23, September.

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