Name: Statistical Learning Tools for Electricity Load Forecasting
Price: 91430.00 CLP
Availability: InStock
Author: Anestis Antoniadis;Yannig Goude;Matteo Fasiolo;Jean-Michel Poggi;Jairo Cugliari
ISBN: 9783031603419

portada Statistical Learning Tools for Electricity Load Forecasting

Statistical Learning Tools for Electricity Load Forecasting

Formato

Libro Físico

Editorial

Birkhauser Verlag AG

Autor

Anestis Antoniadis;Yannig Goude;Matteo Fasiolo;Jean-Michel Poggi;Jairo Cugliari

Colección

Statistics for Industry, Technology, and Engineering

Año

2025

N° páginas

231

Encuadernación

Tapa Blanda

Dimensiones

23.50 x 15.50 cm

ISBN13

9783031603419

Statistical Learning Tools for Electricity Load Forecasting

Anestis Antoniadis;Yannig Goude;Matteo Fasiolo;Jean-Michel Poggi;Jairo Cugliari (Autor) · Birkhauser Verlag AG · Tapa Blanda

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Reseña del libro "Statistical Learning Tools for Electricity Load Forecasting"

This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.