Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape

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On the importance of the long-term seasonal component in day-ahead electricity price forecasting (2016, background): “…proposed by Misiorek et al. (2006) and later used in a number of EPF studies (Gaillard et al., 2016; Kristiansen, 2012; Maciejowska et al., 2016; Nowotarski and Weron, 2016; Nowotarski et al., 2014; Serinaldi, 2011; Uniejewski et al., 2016; Weron, 2006; Weron and Misiorek, 2008; Ziel, 2016).”
Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, methodology): “Serinaldi [13] introduces the class of Generalized Additive Models for Location, Scale and Shape (GAMLSS) and computes the PIs (called ‘confidence intervals’) as the time-varying quantiles of the density forecasts.”