Tracking Time-Varying Coefficient-Functions

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Spatio‐temporal analysis and modeling of short‐term wind power forecast errors (2011, methodology): “Methods included in this forecasting system are described in [12, 13] (and references therein), while application results may be found in e.”
On the market impact of wind energy forecasts (2010, methodology): “One could locally solve a weighted least squares problem as described in detail for the general case in (Cleveland and Devlin, 1988; Nielsen et al., 2000; Madsen and Holst, 2000).”
Forecasting Electricity Spot Prices Accounting for Wind Power Predictions (2013, methodology): “Abruptly changing parameter estimates are avoided by following [26] and defining the effective forgetting factor as”
Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information (2014, background): “The estimation of can be performed in an adaptive recursive manner as presented in [37], to which the reader is referred to for more details.”
Very‐short‐term probabilistic forecasting of wind power with generalized logit–normal distributions (2012, methodology): “It follows the definition that was given by Nielsen et al. (2000), which links λeff.j/,t and c.j/,t so that λeff.j/,t =1− .1−λ/c.j/,t .20/ where λ is the classical user-defined forgetting factor, λ∈ .0, 1/.”