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

Federico Divina,Francisco Martínez-Álvarez,Miguel García-Torres,José Francisco Torres Maldonado,Alicia Troncoso

doi:10.3390/app10165487

Federico Divina, Francisco Martínez-Álvarez + Show 3 more

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https://doi.org/10.3390/app10165487

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Abstract

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.

Highlights

The electric energy needs are constantly growing
From [9], where a grid search strategy was used for setting the values of the deep neural network parameters, in this work, we propose to use a genetic algorithm (GA) in order to determine a sub-optimal set of hyper-parameters for building the deep neural network that will be used for obtaining the predictions
We proposed a strategy based on neuroevolution in order to predict the short-term electric energy demand

Summary

Introduction

The electric energy needs are constantly growing. It is estimated that such demand will increment from 549 quadrillion British thermal unit (Btu), registered in 2012, to 629 quadrillion Btu in 2020.A further increment of 48% is estimated by 2040 [1].The accurate estimation of the short-term electric energy demand provides several benefits.The economic benefits are evident because this would allow us to allocate only the right amount of resources that are needed in order to produce the amount of energy needed to face the actual demand [2,3]. It is estimated that such demand will increment from 549 quadrillion British thermal unit (Btu), registered in 2012, to 629 quadrillion Btu in 2020. The accurate estimation of the short-term electric energy demand provides several benefits. The economic benefits are evident because this would allow us to allocate only the right amount of resources that are needed in order to produce the amount of energy needed to face the actual demand [2,3]. Energy efficiency is another relevant goal pursued with these kinds of approaches since the accurate forecasting of electricity demand in public buildings or in industrial plants usually leads to energy savings [4,5,6]

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