Titlebook: Statistical Learning Tools for Electricity Load Forecasting; Anestis Antoniadis,Jairo Cugliari,Jean-Michel Pogg Book 2024 The Editor(s) (i

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2662-5555 ion 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 978-3-031-60341-9978-3-031-60339-6Series ISSN 2662-5555 Series E-ISSN 2662-5563

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978-3-031-60341-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Functional State Space ModelsThe aim of this case study is to present an adaptation of the functional time series (FTS) framework described before, in order to obtain a setting well suited to handle state space models. We recall that the FTS allows us to capture in a natural way the underlying continuous nature of the electrical load curve.

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Anestis Antoniadis,Jairo Cugliari,Jean-Michel PoggIntroduces modern forecasting methods and tools for creating customized electricity forecasting models.Demonstrates implementation of modeling strategies using real-world data together with relevant R

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Statistics for Industry, Technology, and Engineeringhttp://image.papertrans.cn/s/image/876459.jpg

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