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Machine learning for solar irradiance forecasting of photovoltaic system

Author

Listed:
  • Li, Jiaming
  • Ward, John K.
  • Tong, Jingnan
  • Collins, Lyle
  • Platt, Glenn

Abstract

Photovoltaic generation of electricity is an important renewable energy source, and large numbers of relatively small photovoltaic systems are proliferating around the world. Today it is widely acknowledged by power producers, utility companies and independent system operators that it is only through advanced forecasting, communications and control that these distributed resources can collectively provide a firm, dispatchable generation capacity to the electricity market. One of the challenges of realizing such a goal is the precise forecasting of the output of individual photovoltaic systems, which is affected by a lot of factors. This paper introduces our short-term solar irradiance forecasting algorithms based on machine learning methodologies, Hidden Markov Model and SVM regression. A series of experimental evaluations are presented to analyze the relative performance of the techniques in order to show the importance of these methodologies. The Matlab interface, the Weather Forecasting Platform, has been used for these evaluations. The experiments are performed using the dataset generated by Australian Bureau of Meteorology. The experimental results show that our machine learning based forecasting algorithms can precisely predict future 5–30 min solar irradiance under different weather conditions.

Suggested Citation

  • Li, Jiaming & Ward, John K. & Tong, Jingnan & Collins, Lyle & Platt, Glenn, 2016. "Machine learning for solar irradiance forecasting of photovoltaic system," Renewable Energy, Elsevier, vol. 90(C), pages 542-553.
    • Handle: RePEc:eee:renene:v:90:y:2016:i:c:p:542-553
    DOI: 10.1016/j.renene.2015.12.069
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    References listed on IDEAS

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    1. Li, Jiaming & Guo, Ying & Platt, Glenn & Ward, John K., 2013. "Renewable energy aggregation with intelligent battery controller," Renewable Energy, Elsevier, vol. 59(C), pages 220-228.
    2. Mellit, Adel & Kalogirou, Soteris A. & Drif, Mahmoud, 2010. "Application of neural networks and genetic algorithms for sizing of photovoltaic systems," Renewable Energy, Elsevier, vol. 35(12), pages 2881-2893.
    3. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    12. Jessica Wojtkiewicz & Matin Hosseini & Raju Gottumukkala & Terrence Lynn Chambers, 2019. "Hour-Ahead Solar Irradiance Forecasting Using Multivariate Gated Recurrent Units," Energies, MDPI, vol. 12(21), pages 1-13, October.
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    14. Amith Khandakar & Muhammad E. H. Chowdhury & Monzure- Khoda Kazi & Kamel Benhmed & Farid Touati & Mohammed Al-Hitmi & Antonio Jr S. P. Gonzales, 2019. "Machine Learning Based Photovoltaics (PV) Power Prediction Using Different Environmental Parameters of Qatar," Energies, MDPI, vol. 12(14), pages 1-19, July.
    15. Xu, Fang Yuan & Tang, Rui Xin & Xu, Si Bin & Fan, Yi Liang & Zhou, Ya & Zhang, Hao Tian, 2021. "Neural network-based photovoltaic generation capacity prediction system with benefit-oriented modification," Energy, Elsevier, vol. 223(C).
    16. Hanany Tolba & Nouha Dkhili & Julien Nou & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2020. "Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study," Energies, MDPI, vol. 13(16), pages 1-23, August.
    17. Sharma, Amandeep & Kakkar, Ajay, 2018. "Forecasting daily global solar irradiance generation using machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2254-2269.
    18. Kong, Weicong & Jia, Youwei & Dong, Zhao Yang & Meng, Ke & Chai, Songjian, 2020. "Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting," Applied Energy, Elsevier, vol. 280(C).
    19. Gabriel Mendonça de Paiva & Sergio Pires Pimentel & Bernardo Pinheiro Alvarenga & Enes Gonçalves Marra & Marco Mussetta & Sonia Leva, 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks," Energies, MDPI, vol. 13(11), pages 1-28, June.
    20. Nespoli, Alfredo & Niccolai, Alessandro & Ogliari, Emanuele & Perego, Giovanni & Collino, Elena & Ronzio, Dario, 2022. "Machine Learning techniques for solar irradiation nowcasting: Cloud type classification forecast through satellite data and imagery," Applied Energy, Elsevier, vol. 305(C).
    21. Arumugham, Dinesh Rajan & Rajendran, Parvathy, 2021. "Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data," Renewable Energy, Elsevier, vol. 180(C), pages 1114-1123.
    22. N. Yogambal Jayalakshmi & R. Shankar & Umashankar Subramaniam & I. Baranilingesan & Alagar Karthick & Balasubramaniam Stalin & Robbi Rahim & Aritra Ghosh, 2021. "Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting," Energies, MDPI, vol. 14(9), pages 1-23, April.
    23. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
    24. Mariaud, Arthur & Acha, Salvador & Ekins-Daukes, Ned & Shah, Nilay & Markides, Christos N., 2017. "Integrated optimisation of photovoltaic and battery storage systems for UK commercial buildings," Applied Energy, Elsevier, vol. 199(C), pages 466-478.

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