Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques

By A. Sfetsos, A. Coonick. 2000

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Centralized Control for Optimizing Microgrids Operation (2008, methodology): “For PV forecasting, efforts like the ones presented in [7] and [8] can be used.”
Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models (2007, neutral): “In addition, they are particularly suitable to solve the problem of identification in the presence of noisy data [20,21], and some promising results have been reported [22–27].”
Photovoltaic and solar power forecasting for smart grid energy management (2015, methodology): “AI approaches utilize advanced AI techniques, such as artificial neural networks (ANNs), to construct solar forecasters, which can be also classified into the category of the statistical approach [15].”
Intra-hour DNI forecasting based on cloud tracking image analysis (2013, methodology): “The time-series forecasting methodologies have been applied successfully for 5–15 min (Reikard, 2009), 1-h (Cao and Lin, 2008; Sfetsos and Coonick, 2000; Martin et al., 2010), and 24-h ahead (Mellit et al., 2010).”
Online short-term solar power forecasting (2009, background): “The need for efficient forecasting methods is evident and the research activities within the topic is increasing, see for example [1], [2], [3], and [4].”