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The aim of this paper is to compute one-day-ahead prediction regions fordaily curves of electricity demand and price. Three model-based procedures toconstruct general prediction regions are proposed, all of them using bootstrapalgorithms. The first proposed method considers any $L_p$ norm for functionaldata to measure the distance between curves, the second one is designed to takedifferent variabilities along the curve into account, and the third one takesadvantage of the notion of depth of a functional data. The regression modelwith functional response on which our proposed prediction regions are based israther general: it allows to include both endogenous and exogenous functionalvariables, as well as exogenous scalar variables; in addition, the effect ofsuch variables on the response one is modeled in a parametric, nonparametric orsemi-parametric way. A comparative study is carried out to analyse theperformance of these prediction regions for the electricity market of mainlandSpain, in year 2012. This work extends and complements the methods and resultsin Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018)(focused on prediction intervals), which use the same database as here.

Bootstrap prediction regions for daily curves of electricity demand and price using functional data