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SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions

Author

Listed:
  • Utpal Kumar Das

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Kok Soon Tey

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Mehdi Seyedmahmoudian

    (School of Engineering, Deakin University, Melbourne 3216, Australia)

  • Mohd Yamani Idna Idris

    (Department of Computer System and Technology, Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Saad Mekhilef

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

  • Ben Horan

    (School of Engineering, Deakin University, Melbourne 3216, Australia)

  • Alex Stojcevski

    (School of Software and Electrical Engineering, Swinburne University of Technology, Melbourne, Victoria 3122, Australia)

Abstract

Inaccurate forecasting of photovoltaic (PV) power generation is a great concern in the planning and operation of stable and reliable electric grid systems as well as in promoting large-scale PV deployment. The paper proposes a generalized PV power forecasting model based on support vector regression, historical PV power output, and corresponding meteorological data. Weather conditions are broadly classified into two categories, namely, normal condition (clear sky) and abnormal condition (rainy or cloudy day). A generalized day-ahead forecasting model is developed to forecast PV power generation at any weather condition in a particular region. The proposed model is applied and experimentally validated by three different types of PV stations in the same location at different weather conditions. Furthermore, a conventional artificial neural network (ANN)-based forecasting model is utilized, using the same experimental data-sets of the proposed model. The analytical results showed that the proposed model achieved better forecasting accuracy with less computational complexity when compared with other models, including the conventional ANN model. The proposed model is also effective and practical in forecasting existing grid-connected PV power generation.

Suggested Citation

  • Utpal Kumar Das & Kok Soon Tey & Mehdi Seyedmahmoudian & Mohd Yamani Idna Idris & Saad Mekhilef & Ben Horan & Alex Stojcevski, 2017. "SVR-Based Model to Forecast PV Power Generation under Different Weather Conditions," Energies, MDPI, vol. 10(7), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:876-:d:103113
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    References listed on IDEAS

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    1. Strzalka, Aneta & Alam, Nazmul & Duminil, Eric & Coors, Volker & Eicker, Ursula, 2012. "Large scale integration of photovoltaics in cities," Applied Energy, Elsevier, vol. 93(C), pages 413-421.
    2. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
    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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