Forecasting Photovoltaic Power Generation with a Stacking Ensemble Model

Abdallah Abdellatif, Hamza Mubarak, Shameem Ahmad, Tofael Ahmed (), G. M. Shafiullah (), Ahmad Hammoudeh, Hamdan Abdellatef, M. M. Rahman and Hassan Muwafaq Gheni
Additional contact information
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
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
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

Sustainability, 2022, vol. 14, issue 17, 1-21

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.

Keywords: photovoltaic systems; power output forecasting; one day ahead; machine learning; stacking ensemble model; extra trees regressor (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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