Ground-based classification method for direct normal and global horizontal irradiance

Kurzfassung

Evaluating surface solar irradiance (SSI) variability is important for irradiance models’ evaluation, resource assessment and forecasting applications in the solar energy field. In this work, SSI time series are classified in different sky conditions using one scheme based on direct normal irradiance (DNI) and another based on global horizontal irradiance (GHI) measurements, both at 1-minute resolution and for hourly classification. The classification follows the model proposed by Schroedter-Homscheidt et al. (2018) with four classes associated with clear sky and thin clouds and four classes related with thick clouds. The method is based on a visual interpretation of GHI and DNI measurement patterns for the Baseline Surface Radiation Network (BSRN) station of Carpentras during one year, which forms a reference database. The proposed scheme was reviewed for improvements in the reference database, a new normalization method for the variability indices, and the usefulness of the DNI-based variability indices are investigated for extension of the method to GHI-only data. Thirteen variability indices are applied in the classification including the clear sky index (kc); the average, maximum and standard deviation of the absolute values for the first derivative of SSI and kc; the variability indices proposed by Stein et al. (2012) and Coimbra et al. (2013); and variability indices based on the integrals of envelopes curves obtained according to the local maxima and minima time-series. The classification model is based on a statistical comparison between the median of the variability indices from the reference database and the median of the variability indices for the data being classified. The DNI-based classification results show an accuracy of up to 85% when applying the model in the reference database, which is one improvement compared with the previous method (accuracy of 77%). Preliminary results of the GHI-based classification show an accuracy of up to 60%. Improvements in the GHI classification method are expected, which includes an evaluation of the reference database classification for GHI time-series and additional variability indices, for example, in the case of cloud enhancement phenomenon. References: Schroedter-Homscheidt, M. et al., Classifying direct normal irradiance 1-minute temporal variability from spatial characteristics of geostationary satellite-based cloud observations. Meteorol. Z. 27, 2, 160–179, DOI:10.1127/metz/2018/0875, 2018. Coimbra, C.F.M. et al., Overview of Solar-Forecasting Methods and a Metric for Accuracy Evaluation. Kleissl, J. (Ed.): Solar Energy Forecasting and Resource Assessment. Oxford, 171–194, 2013. Stein, J. S. et al., The Variability Index: A New and Novel Metric for Quantifying Irradiance and Pv Output Variability. World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES) Annual Conference 4(May): 2764–70, 2012.