國立虎尾科技大學 |

Statistical learning tools for electricity load forecasting

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Statistical learning tools for electricity load forecasting/ by Anestis Antoniadis ...[et al.].
other author:	Antoniadis, Anestis.
Published:	Cham :Springer International Publishing : : 2024.,
Description:	ix, 231 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
Subject:	Electric power systems - Statistical methods. -
Online resource:	https://doi.org/10.1007/978-3-031-60339-6
ISBN:	9783031603396

Statistical learning tools for electricity load forecasting
Statistical learning tools for electricity load forecasting[electronic resource] /by Anestis Antoniadis ...[et al.]. - Cham :Springer International Publishing :2024. - ix, 231 p. :ill. (some col.), digital ;24 cm. - Statistics for industry, technology, and engineering,2662-5563. - Statistics for industry, technology, and engineering..

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.

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.

ISBN: 9783031603396

Standard No.: 10.1007/978-3-031-60339-6doiSubjects--Topical Terms:

1456852
Electric power systems
--Statistical methods.

LC Class. No.: TK1005

Dewey Class. No.: 621.310727

Statistical learning tools for electricity load forecasting
LDR:03316nam a22003495a 4500 001 1135203
003 DE-He213
005 20240815130225.0
006 m d
007 cr nn 008maaau
008 241213s2024 sz s 0 eng d
020 $a 9783031603396 $q (electronic bk.)
020 $a 9783031603389 $q (paper)
024 7 $a 10.1007/978-3-031-60339-6 $2 doi
035 $a 978-3-031-60339-6
040 $a GP $c GP
041 0 $a eng
050 4 $a TK1005
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 621.310727 $2 23
090 $a TK1005 $b .S797 2024
245 0 0 $a Statistical learning tools for electricity load forecasting $h [electronic resource] / $c by Anestis Antoniadis ...[et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Birkhäuser, $c 2024.
300 $a ix, 231 p. : $b ill. (some col.), digital ; $c 24 cm.
347 $a text file $b PDF $2 rda
490 1 $a Statistics for industry, technology, and engineering, $x 2662-5563
505 0 $a 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.
520 $a 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.
650 0 $a Electric power systems $x Statistical methods. $3 1456852
650 0 $a Electric power systems $x Mathematical models. $3 632539
650 1 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 1366002
650 2 4 $a Statistical Learning. $3 1396099
650 2 4 $a Machine Learning. $3 1137723
700 1 $a Antoniadis, Anestis. $e editor. $3 1253512
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
830 0 $a Statistics for industry, technology, and engineering. $3 1415383
856 4 0 $u https://doi.org/10.1007/978-3-031-60339-6
950 $a Mathematics and Statistics (SpringerNature-11649)