RT Journal Article
T1 Hybridizing Deep Learning and Neuroevolution: Application to the Spanish Short-Term Electric Energy Consumption Forecasting
A1 Torres, José F.
A1 Divina, Federico
A1 García Torres, Miguel
A1 Martínez Alvarez, Francisco
A1 Troncoso, Alicia
A2 Universidad Americana (PY)
AB 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.
YR 2020
FD 2020
LK http://hdl.handle.net/20.500.14066/3783
UL http://hdl.handle.net/20.500.14066/3783
LA eng
NO CONACYT - Consejo Nacional de Ciencia y Tecnología
DS MINDS@UW
RD 07-jun-2024