Screen LibraryRecurrent Neural Networks for Short-Term Load Forecasting : An Overview and Comparative Analysis /
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 thu...
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| Group Author: | ; ; ; |
| Published: |
Springer International Publishing : Imprint: Springer,
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| Publisher Address: | Cham : |
| Publication Dates: | 2017. |
| Literature type: | eBook |
| Language: | English |
| Series: |
SpringerBriefs in Computer Science,
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| Subjects: | |
| Online Access: |
http://dx.doi.org/10.1007/978-3-319-70338-1 |
| 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 class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of th |
| Carrier Form: | 1 online resource (IX, 72 pages): illustrations. |
| ISBN: | 9783319703381 |
| Index Number: | Q334 |
| CLC: | TP18-532 |
| Contents: | Introduction -- Properties and Training in Recurrent Neural Networks -- Recurrent Neural Networks Architectures -- Other Recurrent Neural Networks Models -- Synthetic Time Series -- Real-World Load Time Series -- Experiments -- Conclusions. . |