Parameter tuning of deep learning using evolutionary algorithm

  • Maxime Hendrix

Student thesis: Master typesMaster in Computer Science Professional focus in Software engineering

Abstract

The deep learning is an algorithm based on machine learning that allows to forecast the
consumption of electricity in Spain. The deep learning algorithm receives input data
that represent the past consumption of electricity. After different computations, the
desired forecast is obtained as an output. However, the deep learning algorithm has
some parameters that need to be conffigured in order to predict with accuracy. A Genetic algorithm is used to perform this parametrization and to find the optimal parameters.
The goal of this thesis is to optimize the parameters of deep learning algorithm in
order to forecast with more accuracy the consumption of electricity in Spain. The results
obtained by the deep learning algorithm with an optimization of the parameters are better than without. There are other methods that allow to forecast the electricity consumption. The comparison between our developed algorithm and the other techniques shows that our algorithm is more efficient to forecast the consumption of electricity.
Date of Award18 Jun 2018
Original languageEnglish
Awarding Institution
  • University of Namur
SupervisorWim VANHOOF (Supervisor)

Keywords

  • machine learning
  • genetic algorithm
  • deep learning algorithm
  • forecasting
  • time series
  • optimization parameters

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