| 书目名称 | Recurrent Neural Networks for Short-Term Load Forecasting | | 副标题 | An Overview and Comp | | 编辑 | Filippo Maria Bianchi,Enrico Maiorino,Robert Jenss | | 视频video | | | 概述 | Presents a comparative study on short-term load forecasting, using different classes of state-of-the-art recurrent neural networks.Describes tests of the models on both controlled synthetic tasks and | | 丛书名称 | SpringerBriefs in Computer Science | | 图书封面 |  | | 描述 | .The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system...Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures..Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first | | 出版日期 | Book 2017 | | 关键词 | Recurrent neural networks; Load forecasting; Time-series prediction; Echo state networks; NARX networks; | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-319-70338-1 | | isbn_softcover | 978-3-319-70337-4 | | isbn_ebook | 978-3-319-70338-1Series ISSN 2191-5768 Series E-ISSN 2191-5776 | | issn_series | 2191-5768 | | copyright | The Author(s) 2017 |
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发表于 2025-3-21 17:48:04
