Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting

Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting

The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based...

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Bibliographic Details
Published in:Applied sciences Vol. 10; no. 16; p. 5487
Main Authors: Divina, Federico, Torres Maldonado, José Francisco, García-Torres, Miguel, Martínez-Álvarez, Francisco, Troncoso, Alicia
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-08-2020
Subjects:
Accuracy
Algorithms
Back propagation
Big Data
Deep learning
Distributed processing
Economic forecasting
Electricity
Energy consumption
Energy efficiency
evolutionary computation
Forecasting
Genetic algorithms
Motivation
Neural networks
neuroevolution
Short term
time-series forecasting
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Summary:The electric energy production would be much more efficient if accurate estimations of the future demand were available, since these would allow allocating only the resources needed for the production of the right amount of energy required. With this motivation in mind, we propose a strategy, based on neuroevolution, that can be used to this aim. Our proposal uses a genetic algorithm in order to find a sub-optimal set of hyper-parameters for configuring a deep neural network, which can then be used for obtaining the forecasting. Such a strategy is justified by the observation that the performances achieved by deep neural networks are strongly dependent on the right setting of the hyper-parameters, and genetic algorithms have shown excellent search capabilities in huge search spaces. Moreover, we base our proposal on a distributed computing platform, which allows its use on a large time-series. In order to assess the performances of our approach, we have applied it to a large dataset, related to the electric energy consumption registered in Spain over almost 10 years. Experimental results confirm the validity of our proposal since it outperforms all other forecasting techniques to which it has been compared.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10165487