| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1551024 | Solar Energy | 2011 | 13 Pages |
Three methods to estimate the daily global solar irradiation are compared: the Bristow–Campbell (BC), Artificial Neural Network (ANN) and Kernel Ridge Regression (KRR). BC is an empirical approach based on air maximum and minimum temperature. ANN and KRR are non-linear approaches that use temperature and precipitation data (which have been selected as the best combination of input data from a gamma test). The experimental dataset includes 4 years (2005–2008) of daily irradiation collected at 40 stations and temperature and precipitation data collected at 400 stations over Spain. Results show that the ANN method produces the best global solar irradiation estimates, with a mean absolute error 2.33 MJ m−2 day−1. Daily maps of solar irradiation over Spain at 1-km spatial resolution are produced by applying the ANN method to temperature and precipitation maps generated from ordinary kriging.
► Solar irradiation over Spain is estimated from air temperature and precipitation. ► Bristow–Campbell, Artificial Neural Network, and Kernel Ridge Regression are compared. ► Daily records of meteorological data during 4 years from ca. 600 stations are used. ► The best method has resulted to be the ANN. ► Solar irradiation maps are derived at 1-km spatial resolution applying the ANN method.
