Titlebook: Statistical Learning Tools for Electricity Load Forecasting; Anestis Antoniadis,Jairo Cugliari,Jean-Michel Pogg Book 2024 The Editor(s) (i

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Introduction,sis on their functional data analysis aspect. The book should be useful beyond the electrical context because it discusses methods and models that extend to other applications, such as forecasting of seasonal phenomena, possibly influenced by external factors (e.g., call centers activity, public hot water supply, airport passenger traffic, etc.).

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Aggregation of Expertsstic assumptions are made on the data generative process, and the theoretical developments are based on the theory of individual sequences where the goal is to derive aggregation strategies achieving good forecasting results on all sequences of observations.

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Forecasting Daily Peak Demand Using GAMse same time resolution as the demand (e.g., half-hourly temperatures) or less frequently (e.g., the day of the week). Here we consider the problem of forecasting the size and time of the daily peak demand which are, of course, observed on a daily basis, using covariates that are observed more frequently.

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Introduction,ial and scientific applications. We illustrate the basic theory and practical utility of several up-to-date statistical methods, with particular emphasis on their functional data analysis aspect. The book should be useful beyond the electrical context because it discusses methods and models that ext

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Additive Modelling of Electricity Demand with se distribution and several covariates is modelled nonparametrically, typically via spline bases expansions. Here we focus on standard GAMs, where only one parameter of the response distribution (typically controlling the mean or location) is modelled additively, while the remaining parameters do no

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