Deterministic and Stochastic Approaches for Day-Ahead Solar Power Forecasting
2016
Photovoltaic (PV) power forecasting has the potential to mitigate some of effects of
resource variability caused by high solar power penetration into the electricity grid. Two
main methods are currently used for PV power generation forecast: (i) a deterministic
approach that uses physics-based models requiring detailed PV plant information and
(ii) a data-driven approach based on statistical or stochastic machine learning techniques
needing historical power measurements. The main goal of this work is to analyze
the accuracy of these different approaches. Deterministic and stochastic models for dayahead
PV generation forecast were developed, and a detailed error analysis was performed.
Four years of site measurements were used to train and test the models. Numerical
weather prediction (NWP) data generated by the weather research and forecasting
(WRF) model were used as input. Additionally, a new parameter, the clear sky performance
index, is defined. This index is equivalent to the clear sky index for PV power generation
forecast, and it is here used in conjunction to the stochastic and persistence models.
The stochastic model not only was able to correct NWP bias errors but it also provided a
better irradiance transposition on the PV plane. The deterministic and stochastic models
yield day-ahead forecast skills with respect to persistence of 35% and 39%, respectively.
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