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020 _a9783031603396
_9978-3-031-60339-6
024 7 _a10.1007/978-3-031-60339-6
_2doi
050 4 _aQA276-280
072 7 _aPBT
_2bicssc
072 7 _aMAT029000
_2bisacsh
072 7 _aPBT
_2thema
100 1 _aAntoniadis, Anestis.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982392
245 1 0 _aStatistical Learning Tools for Electricity Load Forecasting
_h[electronic resource] /
_cby Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Birkhäuser,
_c2024.
300 _aIX, 231 p. 128 illus., 48 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStatistics for Industry, Technology, and Engineering,
_x2662-5563
505 0 _aIntroduction -- 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 _aThis 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 _aStatistics .
_965982
650 0 _aMachine learning.
_955059
650 1 4 _aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
_966911
650 2 4 _aStatistical Learning.
_967991
650 2 4 _aMachine Learning.
_955059
700 1 _aCugliari, Jairo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982393
700 1 _aFasiolo, Matteo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982394
700 1 _aGoude, Yannig.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982395
700 1 _aPoggi, Jean-Michel.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_982396
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031603389
776 0 8 _iPrinted edition:
_z9783031603402
776 0 8 _iPrinted edition:
_z9783031603419
830 0 _aStatistics for Industry, Technology, and Engineering,
_x2662-5563
_982397
856 4 0 _uhttps://doi.org/10.1007/978-3-031-60339-6
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c62490
_d62490