| 書目名稱 | Statistical Learning Tools for Electricity Load Forecasting | | 編輯 | Anestis Antoniadis,Jairo Cugliari,Jean-Michel Pogg | | 視頻video | http://file.papertrans.cn/877/876459/876459.mp4 | | 概述 | Introduces modern forecasting methods and tools for creating customized electricity forecasting models.Demonstrates implementation of modeling strategies using real-world data together with relevant R | | 叢書名稱 | Statistics for Industry, Technology, and Engineering | | 圖書封面 |  | | 描述 | .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 | | 出版日期 | Book 2024 | | 關(guān)鍵詞 | Electricity Load Forecasting; Machine learning tools for electricity load forecasting; Forecasting tim | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-60339-6 | | isbn_softcover | 978-3-031-60341-9 | | isbn_ebook | 978-3-031-60339-6Series ISSN 2662-5555 Series E-ISSN 2662-5563 | | issn_series | 2662-5555 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
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