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文库首页人工智能深度学习Recurrent Neural Networks for Short-Term Load Forecasting

Recurrent Neural Networks for Short-Term Load Forecasting

Recurrent

Neural

Networks

Forecasting

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循环神经网络非常适合处理序列化数据，本书比较短小，但内容比较精炼，是2017年11出版的新书，值得一看。

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123

SPRINGER BRIEFS IN COMPUTER SCIENCE

FilippoMariaBianchi

EnricoMaiorino

MichaelC.Kampffmeyer

AntonelloRizzi

RobertJenssen

Recurrent Neural

Networks for

Short-Term Load

Forecasting

An Overview and

Comparative Analysis

SpringerBriefs in Computer Science

Series editors

Stan Zdonik, Brown University, Providence, Rhode Isla nd, USA

Shashi Shekhar, University of Minnesota, Minneapolis, Minnesota, USA

Xindong Wu, University of Vermont, Burlington, Vermont, USA

Lakhmi C. Jain, University of South Australia, Adelaide, South Australia, Australia

David Padua, University of Illinois Urbana-Champaign, Urbana, Illinois, USA

Xuemin (Sherman) Shen, University of Waterloo, Waterloo, Ontario, Canada

Borko Furht, Florida Atlantic University, Boca Raton, Florida, USA

V.S. Subrahmanian, University of Maryland, College Park, Maryland, USA

Martial Hebert, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA

Katsushi Ikeuchi, University of Tokyo, Tokyo, Japan

Bruno Siciliano, Università di Napoli Federico II, Napoli, Italy

Sushil Jajodia, George Mason University, Fairfax, Virginia, USA

Newton Lee, Newton Lee Laboratories, LLC, Tujunga, California, USA

More information about this series at http://www.springer.com/series/10028

Filippo Maria Bianchi

•

Enrico Maiorino

Michael C. Kampffmeyer

Antonello Rizzi

•

Robert Jenssen

Recurrent Neural Networks

for Short-Term Load

Forecasting

An Overview and Comparative Analysis

123

Filippo Maria Bianchi

UiT The Arctic University of Norway

Tromsø

Norway

Enrico Maiorino

Harvard Medical School

Boston, MA

USA

Michael C. Kampffmeyer

UiT The Arctic University of Norway

Tromsø

Norway

Antonello Rizzi

Sapienza University of Rome

Rome

Italy

Robert Jenssen

UiT The Arctic University of Norway

Tromsø

Norway

ISSN 2191-5768 ISSN 2191-5776 (electronic)

SpringerBriefs in Computer Science

ISBN 978-3-319-70337-4 ISBN 978-3-319-70338-1 (eBook)

https://doi.org/10.1007/978-3-319-70338-1

Library of Congress Control Number: 2017957698

This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part

of the material is concerned, speciﬁcally the rights of translation, reprinting, reuse of illustrations,

recitation, broadcasting, reproduction on microﬁlms or in any other physical way, and transmission

or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar

methodology now known or hereafter developed.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this

publication does not imply, even in the absence of a speciﬁc statement, that such names are exempt from

the relevant protective laws and regulations and therefore free for general use.

The publisher, the authors and the editors are safe to assume that the advice and information in this

book are believed to be true and accurate at the date of publication. Neither the publisher nor the

authors or the editors give a warranty, express or implied, with respect to the material contained herein or

for any errors or omissions that may have been made. The publisher remains neutral with regard to

jurisdictional claims in published maps and institutional afﬁliations.

Printed on acid-free paper

This Springer imprint is published by Springer Nature

The registered company is Springer International Publishing AG

The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

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Deep Learning: Recurrent Neural Networks in Python

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