Uncertainty for solar products assessment and benchmarking
Uncertainty for solar products assessment and benchmarking J. Polo, L. Ramírez, L.F.Zarzalejo, L. Martín, A. Navarro CIEMAT (Energy department – Solar Platform of Almería)4th Meeting IEA SHC Task 36 Hamburg 23-25 Oct 2007
Uncertainty parametersParameters based on deviation of data values (careful with notation) n ( yi − g i ) MRE = ∑ × 100 Mean Relative Error (MRE) n gi ∑ ( gi − yi ) / n i =1 i =1 Mean Bias Error (MBE) MBE = n × 100 ∑ gi / n n i =1 ∑ ( gi − yi )2 / n RMSE RMSE = i =1 n × 100 ∑ gi / n i =1Parameters based on deviation of distribution functions KSI and OVER (Integral of KS test complete and over critical value) KSE KSE = ( KSI × w1 + OVER × w2) / 2 RIO = ( RMSE + KSE ) / 2 RIO 4th Meeting IEA SHC Task 36 Hamburg 23-25 Oct 2007
Towards standardization: open issues for discussion Solar Radiation Product uncertainty: users require one number (Radiation ± U) . Candidates: RMSE, MBE, relative error… Problems with normalization. Model assessment: we look for more information than uncertainty. strengths and shortcomings of models is also required. Candidates: K-S Test in addition to uncertainty measures MBE, RMSE, deviations at different solar elevation angles, … are useful for this purpose. Benchmarking of models: We should know a priori the capabilities of different models and we want to compare their response under the same conditions. Candidates: RIO parameter compiles KS test and RMSE information in one single parameter. 4th Meeting IEA SHC Task 36 Hamburg 23-25 Oct 2007
Future activityBenchmarking exercise on one selected pixel forone year of hourly global irradiation?Elaboration of a guide for uncertainty (MESoR)?4th Meeting IEA SHC Task 36 Hamburg 23-25 Oct 2007
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