Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5482997 | Renewable and Sustainable Energy Reviews | 2017 | 16 Pages |
Abstract
Three different tiers of estimations were obtained being SARAH and OK the best performing methods overall. The SARAH dataset (MAE=1.10±0.13 MJ/m2, MBE=0.22±0.36 MJ/m2) generated estimates with the lowest spread, but led to a slight overestimation in low-altitude flat areas. The OK (MAE=1.10±0.25 MJ/m2, MBE=0.00±0.31 MJ/m2) outperformed SARAH in these flat areas (high density of stations), but at the expense of a higher variability. Alternatively, SARAH surpassed Ordinary Kriging (OK) when the distance to the closest station exceeded 20-30 km. The ERA-Interim reanalysis and the XGBoost were in the second tier of estimations, and the parametric model yielded the worst results overall. ERA-Interim exhibited a systematic overestimation. The locally trained Antonanzas and XGBoost struggled to model the atmospheric transmissivity, showing large positive errors in spring months and a small underestimation of clear-sky days. Finally, a summary with the strengths and weaknesses of the five methods provides a deeper understanding for the selection of the adequate estimation approach.
Keywords
ECMWFSSISISNOAALUTGOESGBMMFGMBDIDWMADCLARAMBEBSRNGeostationary meteorological satelliteJMASurface solar irradianceJapan Meteorological AgencyrRMSEXGBoostANNRMSENWPMSGRBFERA-InterimGMsrTMGenetic algorithmsMAEGlobal horizontal irradiationdirect normal irradianceleave-one-outExtreme gradient boostingLook-up tableRandom searchSodaMean bias errorGradient Boosting MachineDNIEstimation methodsLinear regressionroot mean squared errorSARAHNational Oceanic and Atmospheric Administrationnational aeronautics and space administrationMarsSIARArtificial Neural NetworkMagicGHIRadial basis functionLOOSVMSupport vector machineGeostationary Operational Environmental SatelliteRadiative transfer modelInverse distance weightingMean absolute deviationMean Absolute ErrorNASANumerical weather predictionKrigingUniversal krigingOrdinary kriging
Related Topics
Physical Sciences and Engineering
Energy
Renewable Energy, Sustainability and the Environment
Authors
R. Urraca, E. Martinez-de-Pison, A. Sanz-Garcia, J. Antonanzas, F. Antonanzas-Torres,