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Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

Author

Listed:
  • Abdallah Abdellatif

    (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Hamza Mubarak

    (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia)

  • Shameem Ahmad

    (Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia
    Department of Electrical and Electronic Engineering, Faculty of Engineering, American International University-Bangladesh (AIUB), Dhaka 1229, Bangladesh)

  • Tofael Ahmed

    (Department of Electrical and Electronic Engineering, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh)

  • G. M. Shafiullah

    (Discipline of Engineering and Energy, Murdoch University, Perth 6150, Australia)

  • Ahmad Hammoudeh

    (ISIA Lab, Faculty of Engineering University of Mons, 7000 Mons, Belgium
    MAIA Lab, Faculty of Science, University of Mons, 7000 Mons, Belgium
    TRAIL Institute, Wallonia-Brussels Federation, 7000 Mons, Belgium)

  • Hamdan Abdellatef

    (School of Engineering-Electrical & Computer Engineering Department, Lebanese American University, Beirut 1102, Lebanon)

  • M. M. Rahman

    (Department of Electronics and Communications Engineering, East West University, Aftabnagar, Dhaka 1212, Bangladesh)

  • Hassan Muwafaq Gheni

    (Computer Techniques Engineering Department, Al-Mustaqbal University College, Hillah 51001, Iraq)

Abstract

Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.

Suggested Citation

  • Abdallah Abdellatif & Hamza Mubarak & Shameem Ahmad & Tofael Ahmed & G. M. Shafiullah & Ahmad Hammoudeh & Hamdan Abdellatef & M. M. Rahman & Hassan Muwafaq Gheni, 2022. "Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model," Sustainability, MDPI, vol. 14(17), pages 1-21, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11083-:d:907204
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    References listed on IDEAS

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    Cited by:

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    2. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
    3. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    4. 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.
    5. Jose Cruz & Christian Romero & Oscar Vera & Saul Huaquipaco & Norman Beltran & Wilson Mamani, 2023. "Multiparameter Regression of a Photovoltaic System by Applying Hybrid Methods with Variable Selection and Stacking Ensembles under Extreme Conditions of Altitudes Higher than 3800 Meters above Sea Lev," Energies, MDPI, vol. 16(12), pages 1-21, June.
    6. Edna S. Solano & Carolina M. Affonso, 2023. "Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms," Sustainability, MDPI, vol. 15(10), pages 1-19, May.

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