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| Link zu diesem Datensatz | https://d-nb.info/1339206269 |
| Titel | Statistical Learning Tools for Electricity Load Forecasting / by Anestis Antoniadis, Jairo Cugliari, Matteo Fasiolo, Yannig Goude, Jean-Michel Poggi |
| Person(en) |
Antoniadis, Anestis (Verfasser) Cugliari, Jairo (Verfasser) Fasiolo, Matteo (Verfasser) Goude, Yannig (Verfasser) Poggi, Jean-Michel (Verfasser) |
| Organisation(en) | SpringerLink (Online service) (Sonstige) |
| Ausgabe | 1st ed. 2024 |
| Verlag | Cham : Springer International Publishing, Imprint: Birkhäuser |
| Zeitliche Einordnung | Erscheinungsdatum: 2024 |
| Umfang/Format | Online-Ressource, IX, 231 p. 128 illus., 48 illus. in color. : online resource. |
| Andere Ausgabe(n) |
Printed edition:: ISBN: 978-3-031-60338-9 Printed edition:: ISBN: 978-3-031-60340-2 Printed edition:: ISBN: 978-3-031-60341-9 |
| Inhalt | 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 |
| Persistent Identifier |
URN: urn:nbn:de:101:1-2408161322307.061850222232 DOI: 10.1007/978-3-031-60339-6 |
| URL | https://doi.org/10.1007/978-3-031-60339-6 |
| ISBN/Einband/Preis | 978-3-031-60339-6 |
| Sprache(n) | Englisch (eng) |
| Beziehungen | Statistics for Industry, Technology, and Engineering |
| DDC-Notation | 621.3815 (maschinell ermittelte DDC-Kurznotation) |
| Sachgruppe(n) | 621.3 Elektrotechnik, Elektronik |
| Online-Zugriff | Archivobjekt öffnen |
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