Short-term forecasts have recently gained an increasing attention because of the rise of competitive electricity markets. In fact, short-terms forecast of possible future loads turn out to be fundamental to build efficient energy management strategies as well as to avoid energy wastage. Such type of challenges are difficult to tackle both from a theoretical and applied point of view. Latter tasks require sophisticated methods to manage multidimensional time series related to stochastic phenomena which are often highly interconnected. In the present work we first review novel approaches to energy load forecasting based on recurrent neural network, focusing our attention on long/short term memory architectures (LSTMs). Such type of artificial neural networks have been widely applied to problems dealing with sequential data such it happens, e.g., in socio-economics settings, for text recognition purposes, concerning video signals, etc., always showing their effectiveness to model complex temporal data. Moreover, we consider different novel variations of basic LSTMs, such as sequence-to-sequence approach and bidirectional LSTMs, aiming at providing effective models for energy load data. Last but not least, we test all the described algorithms on real energy load data showing not only that deep recurrent networks can be successfully applied to energy load forecasting, but also that this approach can be extended to other problems based on time series prediction.
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28 November 2017
PROCEEDINGS OF THE INTERNATIONAL CONFERENCE OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2017 (ICCMSE-2017)
21–25 April 2017
Thessaloniki, Greece
Research Article|
November 28 2017
Analysis of recurrent neural networks for short-term energy load forecasting
Luca Di Persio;
Luca Di Persio
a)
a,b)
Dept. of Computer Science, University of Verona and HPA-High Performance Analytics
, Italy
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Oleksandr Honchar
Oleksandr Honchar
b)
a,b)
Dept. of Computer Science, University of Verona and HPA-High Performance Analytics
, Italy
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AIP Conf. Proc. 1906, 190006 (2017)
Citation
Luca Di Persio, Oleksandr Honchar; Analysis of recurrent neural networks for short-term energy load forecasting. AIP Conf. Proc. 28 November 2017; 1906 (1): 190006. https://doi.org/10.1063/1.5012469
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