  We conduct the first comprehensive meta-analysis of deterministic solar
forecasting based on skill score, screening 1,447 papers from Google Scholar
and reviewing the full texts of 320 papers for data extraction. A database of
4,687 points was built and analyzed with multivariate adaptive regression
spline modelling, partial dependence plots, and linear regression. The marginal
impacts on skill score of ten factors were quantified. The analysis shows the
non-linearity and complex interaction between variables in the database.
Forecast horizon has a central impact and dominates other factors' impacts.
Therefore, the analysis of solar forecasts should be done separately for each
horizon. Climate zone variables have statistically significant correlation with
skill score. Regarding inputs, historical data and spatial temporal information
are highly helpful. For intra-day, sky and satellite images show the most
importance. For day-ahead, numerical weather predictions and locally measured
meteorological data are very efficient. All forecast models were compared.
Ensemble-hybrid models achieve the most accurate forecasts for all horizons.
Hybrid models show superiority for intra-hour while image-based methods are the
most efficient for intra-day forecasts. More training data can enhance skill
score. However, over-fitting is observed when there is too much training data
(longer than 2000 days). There has been a substantial improvement in solar
forecast accuracy, especially in recent years. More improvement is observed for
intra-hour and intra-day than day-ahead forecasts. By controlling for the key
differences between forecasts, including location variables, our findings can
be applied globally.
