Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database

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Application of Big Data and Machine Learning in Smart Grid, and Associated Security Concerns: A Review (2019, background): “Prediction models for both meteorological forecasts and system outputs were presented in [172]–[178].”
Proposed Metric for Evaluation of Solar Forecasting Models (2013, methodology): “The observation that leads to the relationship between V and U is that forecast model errors are typically higher during wet (cloudy) days than during dry (clear) days, as clearly demonstrated in a few recent studies [4,6].”
Intra-hour DNI forecasting based on cloud tracking image analysis (2013, methodology): “For the forecasting horizon of same-day to a few days ahead, NWP models have been evaluated in various locations (Perez et al., 2010; Lorenz et al., 2009; Marquez and Coimbra, 2011; Mathiesen and Kleissl, 2011; Changsong et al., 2011).”
Cloud-tracking methodology for intra-hour DNI forecasting (2014, background): “…use a coarse spatial resolution (several tens of km2) but recently higher resolution evaluations reaching 3 km2 (Benjamin et al., 2010; Lara-Fanego et al., 2012) and including Artificial Neural Network (ANN) model processing (Marquez and Coimbra, 2011; Chen et al., 2011) have been carried out.”