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Artificial Neural Networks and Machine Learning – ICANN 2013

Volume 8131 of the series Lecture Notes in Computer Science pp 451-458

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

  • Pablo RomeuAffiliated withEmbedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera
  • , Francisco Zamora-MartínezAffiliated withEmbedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera
  • , Paloma Botella-RocamoraAffiliated withEmbedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera
  • , Juan PardoAffiliated withEmbedded Systems and Artificial Intelligence Group, Escuela Superior de Enseñanzas Técnicas, Universidad CEU Cardenal Herrera

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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