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Recurrent neural networks for short-term load forecasting : an overview and comparative analysis

Author: Filippo Maria Bianchi; Enrico Maiorino; Michael C Kampffmeyer; Antonello Rizzi; Robert Jenssen
Publisher: Cham : Springer, 2017.
Series: SpringerBriefs in computer science.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Bianchi, Filippo Maria.
Recurrent Neural Networks for Short-Term Load Forecasting : An Overview and Comparative Analysis.
Cham : Springer International Publishing, ©2017
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Filippo Maria Bianchi; Enrico Maiorino; Michael C Kampffmeyer; Antonello Rizzi; Robert Jenssen
ISBN: 9783319703381 3319703382
OCLC Number: 1012345116
Description: 1 online resource (74 pages)
Contents: Preface --
Contents --
Acronyms --
1 Introduction --
References --
2 Properties and Training in Recurrent Neural Networks --
2.1 Backpropagation Through Time --
2.2 Gradient Descent and Loss Function --
2.3 Parameters Update Strategies --
2.4 Vanishing and Exploding Gradient --
References --
3 Recurrent Neural Network Architectures --
3.1 Elman Recurrent Neural Network --
3.2 Long Short-Term Memory --
3.3 Gated Recurrent Unit --
References --
4 Other Recurrent Neural Networks Models --
4.1 NARX Network --
4.2 Echo State Network --
References 5 Synthetic Time SeriesReferences --
6 Real-World Load Time Series --
6.1 Orange Dataset --
Telephonic Activity Load --
6.2 ACEA Dataset --
Electricity Load --
6.3 GEFCom2012 Dataset --
Electricity Load --
References --
7 Experiments --
7.1 Experimental Settings --
7.1.1 ERNN, LSTM, and GRU --
7.1.2 NARX --
7.1.3 ESN --
7.2 Results on Synthetic Dataset --
7.3 Results on Real-World Dataset --
7.3.1 Results on Orange Dataset --
7.3.2 Results on ACEA Dataset --
7.3.3 Results on GEFCom Dataset --
References --
8 Conclusions
Series Title: SpringerBriefs in computer science.
Responsibility: Filippo Maria Bianchi, Enrico Maiorino, Michael C. Kampffmeyer, Antonello Rizzi, Robert Jenssen.

Abstract:

The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series.  Read more...

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