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Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

Author

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  • Putri Nor Liyana Mohamad Radzi

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Muhammad Naveed Akhter

    (Department of Electrical Engineering, Rachna College of Engineering and Technology (A Constituent College of University of Engineering and Technology Lahore), Gujranwala 52250, Pakistan)

  • Saad Mekhilef

    (Power Electronics and Renewable Energy Research Laboratory (PEARL), Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia
    School of Software and Electrical Engineering, Swinburne University, Hawthorn, VIC 3122, Australia)

  • Noraisyah Mohamed Shah

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

Abstract

Advancements in renewable energy technology have significantly reduced the consumer dependence on conventional energy sources for power generation. Solar energy has proven to be a sustainable source of power generation compared to other renewable energy sources. The performance of a photovoltaic (PV) system is highly dependent on the amount of solar penetration to the solar cell, the type of climatic season, the temperature of the surroundings, and the environmental humidity. Unfortunately, every renewable’s technology has its limitation. Consequently, this prevents the system from operating to a maximum or optimally. Achieving a precise PV system output power is crucial to overcoming solar power output instability and intermittency performance. This paper discusses an intensive review of machine learning, followed by the types of neural network models under supervised machine learning implemented in photovoltaic power forecasting. The literature of past researchers is collected, mainly focusing on the duration of forecasts for very short-, short-, and long-term forecasts in a photovoltaic system. The performance of forecasting is also evaluated according to a different type of input parameter and time-step resolution. Lastly, the crucial aspects of a conventional and hybrid model of machine learning and neural networks are reviewed comprehensively.

Suggested Citation

  • Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:2942-:d:1059527
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    References listed on IDEAS

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    1. Kumari, Pratima & Toshniwal, Durga, 2021. "Long short term memory–convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Elsevier, vol. 295(C).
    2. Lauria, Davide & Mottola, Fabio & Proto, Daniela, 2022. "Caputo derivative applied to very short time photovoltaic power forecasting," Applied Energy, Elsevier, vol. 309(C).
    3. Yuehong Lu & Zafar A. Khan & Manuel S. Alvarez-Alvarado & Yang Zhang & Zhijia Huang & Muhammad Imran, 2020. "A Critical Review of Sustainable Energy Policies for the Promotion of Renewable Energy Sources," Sustainability, MDPI, vol. 12(12), pages 1-31, June.
    4. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Pang, Zhihong & Niu, Fuxin & O’Neill, Zheng, 2020. "Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons," Renewable Energy, Elsevier, vol. 156(C), pages 279-289.
    6. Vishnu Suresh & Przemyslaw Janik & Jacek Rezmer & Zbigniew Leonowicz, 2020. "Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm," Energies, MDPI, vol. 13(3), pages 1-15, February.
    7. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    8. Aghaei, M. & Fairbrother, A. & Gok, A. & Ahmad, S. & Kazim, S. & Lobato, K. & Oreski, G. & Reinders, A. & Schmitz, J. & Theelen, M. & Yilmaz, P. & Kettle, J., 2022. "Review of degradation and failure phenomena in photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    9. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    10. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    11. Pedregal, Diego J. & Trapero, Juan R., 2021. "Adjusted combination of moving averages: A forecasting system for medium-term solar irradiance," Applied Energy, Elsevier, vol. 298(C).
    12. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).
    13. Rodríguez, Fermín & Martín, Fernando & Fontán, Luis & Galarza, Ainhoa, 2021. "Ensemble of machine learning and spatiotemporal parameters to forecast very short-term solar irradiation to compute photovoltaic generators’ output power," Energy, Elsevier, vol. 229(C).
    14. Bouzgou, Hassen & Gueymard, Christian A., 2019. "Fast short-term global solar irradiance forecasting with wrapper mutual information," Renewable Energy, Elsevier, vol. 133(C), pages 1055-1065.
    15. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
    16. Graditi, G. & Ferlito, S. & Adinolfi, G., 2016. "Comparison of Photovoltaic plant power production prediction methods using a large measured dataset," Renewable Energy, Elsevier, vol. 90(C), pages 513-519.
    17. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    18. Gao, Mingming & Li, Jianjing & Hong, Feng & Long, Dongteng, 2019. "Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM," Energy, Elsevier, vol. 187(C).
    19. Gandoman, Foad H. & Abdel Aleem, Shady H.E. & Omar, Noshin & Ahmadi, Abdollah & Alenezi, Faisal Q., 2018. "Short-term solar power forecasting considering cloud coverage and ambient temperature variation effects," Renewable Energy, Elsevier, vol. 123(C), pages 793-805.
    20. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    21. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    22. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    23. Rodríguez, Fermín & Galarza, Ainhoa & Vasquez, Juan C. & Guerrero, Josep M., 2022. "Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control," Energy, Elsevier, vol. 239(PB).
    24. Haider, Syed Altan & Sajid, Muhammad & Sajid, Hassan & Uddin, Emad & Ayaz, Yasar, 2022. "Deep learning and statistical methods for short- and long-term solar irradiance forecasting for Islamabad," Renewable Energy, Elsevier, vol. 198(C), pages 51-60.
    25. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    26. Moreira, M.O. & Balestrassi, P.P. & Paiva, A.P. & Ribeiro, P.F. & Bonatto, B.D., 2021. "Design of experiments using artificial neural network ensemble for photovoltaic generation forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
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    Cited by:

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    2. Dae-Sung Lee & Sung-Yong Son, 2024. "Weighted Average Ensemble-Based PV Forecasting in a Limited Environment with Missing Data of PV Power," Sustainability, MDPI, vol. 16(10), pages 1-17, May.
    3. Chun-Che Huang & Wen-Yau Liang & Roger R. Gung & Pei-An Wang, 2023. "Rough-Set-Based Rule Induction with the Elimination of Outdated Big Data: Case of Renewable Energy Equipment Promotion," Sustainability, MDPI, vol. 15(20), pages 1-19, October.
    4. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.
    5. 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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