The aim of this paper is to compute one-day-ahead prediction regions for daily curves of electricity demand and price. Three model-based procedures to construct general prediction regions are proposed, all of them using bootstrap algorithms. The first proposed method considers any $L_p$ norm for functional data to measure the distance between curves, the second one is designed to take different variabilities along the curve into account, and the third one takes advantage of the notion of depth of a functional data. The regression model with functional response on which our proposed prediction regions are based is rather general: it allows to include both endogenous and exogenous functional variables, as well as exogenous scalar variables; in addition, the effect of such variables on the response one is modeled in a parametric, nonparametric or semi-parametric way. A comparative study is carried out to analyse the performance of these prediction regions for the electricity market of mainland Spain, in year 2012. This work extends and complements the methods and results in Aneiros et al. (2016) (focused on curve prediction) and Vilar et al. (2018) (focused on prediction intervals), which use the same database as here.

翻译：本文旨在计算电力需求和价格日曲线的提前一天预测区域。提出了三种基于模型的方法来构建一般预测区域，均采用自助法算法。第一种方法考虑函数型数据的任意$L_p$范数来度量曲线间的距离；第二种方法旨在考虑曲线不同位置的变化性；第三种方法利用了函数型数据深度的概念。本文提出的预测区域所基于的函数响应回归模型具有相当的一般性：它允许包含内生和外生函数型变量，以及外生标量变量；此外，这些变量对响应变量的影响可以通过参数、非参数或半参数方式建模。我们以2012年西班牙本土电力市场为对象开展比较研究，分析这些预测区域的性能。本文扩展并补充了使用相同数据库的Aneiros等人(2016)（侧重于曲线预测）和Vilar等人(2018)（侧重于预测区间）的方法与结果。