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Recurrent Neural Networks for Short-Term Load Forecasting

Part of the SpringerBriefs in Computer Science Book Series

DOI: 10.1007/978-3-319-70338-1

ISBN: 978-3-319-70337-4

Publisher: Springer International Publishing - Springer Journals

Nov 10, 2017

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Introduction

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 1 –7

[A short-term load forecast is the prediction of the consumption of resources in a distribution network in the near future. The supplied resource can be of any kind, such as electricity in power grids or telephone service in telecommunication networks. An accurate forecast of the demand is of...
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Properties and Training in Recurrent Neural Networks

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 9 –21

[In this chapter, we describe the basic concepts behind the functioning of recurrent neural networks and explain the general properties that are common to several existing architectures. We introduce the basis of their training procedure, the backpropagation through time, as a general way to...
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Recurrent Neural Network Architectures

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 23 –29

[In this chapter, we present three different recurrent neural network architectures that we employ for the prediction of real-valued time series. All the models reviewed in this chapter can be trained through the previously discussed backpropagation through time procedure. First, we present the...
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Other Recurrent Neural Networks Models

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 31 –39

[In this chapter we review two additional types of Recurrent Neural Network, which present important differences with respect to the architectures described so far. More specifically, we introduce the nonlinear auto-regressive with eXogenous inputs (NARX) neural network and the Echo State...
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Synthetic Time Series

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 41 –43

[In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple...
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Real-World Load Time Series

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 45 –55

[In this chapter, we consider three different real-world datasets, which contain real-valued time series of measurements of electricity and telephonic activity load. For each dataset, we set up a short-term load forecast problem of 24 hours ahead prediction. Two of the datasets under analysis...
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Experiments

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 57 –69

[In this section, we compare the prediction performance achieved by the recurrent neural network architectures presented in the previous sections on both the synthetic tasks and the real-world datasets. For each architecture, we report the optimal configuration of its hyperparameters for the task...
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Conclusions

Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert

Pages: 71 –72

[In this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines...

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