Probabilistic Forecasting of Photovoltaic Generation: An Efficient Statistical Approach

Can Wan, Jin Lin, Yonghua Song, Zhao Xu, Guangya Yang

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This letter proposes a novel efficient probabilistic forecasting approach to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV power generation is proposed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.
Original languageEnglish
JournalI E E E Transactions on Power Systems
Issue number3
Pages (from-to)2471 - 2472
Publication statusPublished - 2017


  • PV power
  • Forecasting
  • Prediction intervals
  • Extreme learning machine
  • Quantile regression

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