  Due to the stochastic nature of photovoltaic (PV) power generation, there is
high demand for forecasting PV output to better integrate PV generation into
power grids. Systematic knowledge regarding the factors influencing forecast
accuracy is crucially important, but still mostly unknown. In this paper, we
review 180 papers on PV forecasts and extract a database of forecast errors for
statistical analysis. We show that among the forecast models, hybrid models
consistently outperform the others and will most likely be the future of PV
output forecasting. The use of data processing techniques is positively
correlated with the forecast quality, while the lengths of the forecast horizon
and out-of-sample test set have negative effects on the forecast accuracy. We
also found that the inclusion of numerical weather prediction variables, data
normalization, and data resampling are the most effective data processing
techniques. Furthermore, we found some evidence for cherry picking in reporting
errors and recommend that the test sets be at least one year to better assess
model performance. The paper also takes the first step towards establishing a
benchmark for assessing PV output forecasts.
