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A benchmark of statistical regression methods for short-term forecasting of photovoltaic electricity production, part I: Deterministic forecast of hourly production

Solar Energy
DOI: 10.1016/j.solener.2013.12.006
  • Photovoltaic Power Forecasting
  • Benchmark
  • Statistical Methods
  • Numerical Weather Prediction
  • Quantile Regression
  • Ensemble Numerical Weather Prediction


Abstract This pair of articles presents the results of a study about forecasting photovoltaic (PV) electricity production for some power plants in mainland France. Forecasts are built with statistical methods exploiting outputs from numerical weather prediction (NWP) models. Contrary to most other studies, forecasts are built without using technical information on the power plants. In each article, several statistical methods are used to build forecast models and their performance is compared by means of adequate scores. When a best forecast emerges, its characteristics are then further assessed in order to get a deeper insight of its merits and flaws. The robustness of the results are evaluated with an intense use of cross-validation. The companion article Zamo et al. (2013) will deal with probabilistic forecasts of daily production 2days ahead. By “probabilistic” we mean that our forecast models yield some quantiles of the expected production’s probability distribution. This article deals with forecasting hourly PV production for the next day in a deterministic way, which means the mean expectable hourly PV production is forecast for each day-time hour. In this part of our study, predictors comes from ARPEGE, Météo France’s deterministic NWP model. Our best model is very reliable and performs well, even compared to best expectable performances computed while using observations as predictors. It also points at the interest of using predictors based on human forecasters’ experience.

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