9783031603389 - statistical learning tools for electricity load forecasting di antoniadis, anestis; cugliari, jairo; fasiolo, matteo; goude, yannig; poggi, jean-michel (10 risultati)
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Hardcover. Condizione: new. Hardcover. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Statistical Learning Tools for Electricity Load Forecasting (Statistics for Industry, Technology, and Engineering)
Antoniadis, Anestis; Cugliari, Jairo; Fasiolo, Matteo; Goude, Yannig; Poggi, Jean-Michel
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Condizione: New. 2024th edition NO-PA16APR2015-KAP.
Statistical Learning Tools for Electricity Load Forecasting
Antoniadis, Anestis|Cugliari, Jairo|Fasiolo, Matteo|Goude, Yannig|Poggi, Jean-Michel
Lingua: Inglese
Editore: Springer, Berlin|Springer International Publishing|Birkhäuser, 2024
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Condizione: New.
Statistical Learning Tools for Electricity Load Forecasting
Antoniadis, Anestis/ Cugliari, Jairo/ Fasiolo, Matteo/ Goude, Yannig/ Poggi, Jean-michel
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Hardcover. Condizione: Brand New. 240 pages. 9.25x6.10x9.49 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2024
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Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 elect…ricity 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.
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Condizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing Aug 2024, 2024
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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 f…orecasting 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. 244 pp. Englisch.
Statistical Learning Tools for Electricity Load Forecasting (Statistics for Industry, Technology, and Engineering)
Antoniadis, Anestis; Cugliari, Jairo; Fasiolo, Matteo; Goude, Yannig; Poggi, Jean-Michel
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Condizione: New. Print on Demand.
Statistical Learning Tools for Electricity Load Forecasting (Statistics for Industry, Technology, and Engineering)
Antoniadis, Anestis; Cugliari, Jairo; Fasiolo, Matteo; Goude, Yannig; Poggi, Jean-Michel
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Condizione: New. PRINT ON DEMAND.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 forec…asting 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.Springer Nature c/o IBS, Benzstrasse 21, 48619 Heek 244 pp. Englisch.
