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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 auth...

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Main Authors: Antoniadis, Anestis (Author), Cugliari, Jairo (Author), Fasiolo, Matteo (Author), Goude, Yannig (Author), Poggi, Jean-Michel (Author)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:inglés
Publicado: Cham : Springer International Publishing : Imprint: Birkhäuser, 2024.
Edición:1st ed. 2024.
Series:Statistics for Industry, Technology, and Engineering,
Subjects:
Statistics .
Machine learning.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Statistical Learning.
Machine Learning.
Acceso en liña:https://doi.org/10.1007/978-3-031-60339-6
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Table of Contents:
  • Introduction
  • Part I: A Toolbox of Models
  • Additive Modelling of Electricity Demand with mgcv
  • Probabilistic GAMs: Beyond Mean Modelling
  • Functional Time Series
  • Random Forests
  • Aggregation of Experts
  • Mixed Effects Models for Electricity Load Forecasting
  • Part II: Case Studies: Models in Action on Specific Applications
  • Disaggregated Forecasting of the Total Consumption
  • Aggregation of Multi-Scale Experts
  • Short-Term Load Forecasting using Fine-Grained Data
  • Functional State Space Models
  • Forecasting Daily Peak Demand using GAMs
  • Forecasting During the Lockdown Period.

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