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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. 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.
    2. 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.
    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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