04685nam a22005775i 4500 978-3-031-60339-6 DE-He213 20251030112855.0 cr nn 008mamaa 240815s2024 sz | s |||| 0|eng d 9783031603396 978-3-031-60339-6 10.1007/978-3-031-60339-6 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Antoniadis, Anestis. author. aut http://id.loc.gov/vocabulary/relators/aut 82392 Statistical Learning Tools for Electricity Load Forecasting [electronic resource] / by Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi. 1st ed. 2024. Cham : Springer International Publishing : Imprint: Birkhäuser, 2024. IX, 231 p. 128 illus., 48 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Statistics for Industry, Technology, and Engineering, 2662-5563 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. Statistics . 65982 Machine learning. 55059 Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 66911 Statistical Learning. 67991 Machine Learning. 55059 Cugliari, Jairo. author. aut http://id.loc.gov/vocabulary/relators/aut 82393 Fasiolo, Matteo. author. aut http://id.loc.gov/vocabulary/relators/aut 82394 Goude, Yannig. author. aut http://id.loc.gov/vocabulary/relators/aut 82395 Poggi, Jean-Michel. author. aut http://id.loc.gov/vocabulary/relators/aut 82396 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031603389 Printed edition: 9783031603402 Printed edition: 9783031603419 Statistics for Industry, Technology, and Engineering, 2662-5563 82397 https://doi.org/10.1007/978-3-031-60339-6 ZDB-2-SMA ZDB-2-SXMS 62490 62490