Time-Series Forecasting of Indoor Temperature Using Pre-trained Deep Neural Networks

  • Pablo Romeu
  • Francisco Zamora-Martínez
  • Paloma Botella-Rocamora
  • Juan Pardo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8131)

Abstract

Artificial neural networks have proved to be good at time-series forecasting problems, being widely studied at literature. Traditionally, shallow architectures were used due to convergence problems when dealing with deep models. Recent research findings enable deep architectures training, opening a new interesting research area called deep learning. This paper presents a study of deep learning techniques applied to time-series forecasting in a real indoor temperature forecasting task, studying performance due to different hyper-parameter configurations. When using deep models, better generalization performance at test set and an over-fitting reduction has been observed.

Keywords

Artificial neural networks deep learning time series auto-encoders temperature forecasting energy efficiency 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Pablo Romeu
    • 1
  • Francisco Zamora-Martínez
    • 1
  • Paloma Botella-Rocamora
    • 1
  • Juan Pardo
    • 1
  1. 1.Embedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas TécnicasUniversidad CEU Cardenal HerreraValenciaSpain

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