Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7935687 | Solar Energy | 2018 | 13 Pages |
Abstract
The skill of the various models were evaluated using several metrics and statistical tests. We found that WRF-solar combined with our proposed statistical learning method outperformed smart persistence, a climatological forecast and GFS for day-ahead forecasts of irradiance. In particular, our model was shown to have a Root Mean Square Error (RMSE) 23% lower than smart persistence.
Related Topics
Physical Sciences and Engineering
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Renewable Energy, Sustainability and the Environment
Authors
Hadrien Verbois, Robert Huva, Andrivo Rusydi, Wilfred Walsh,