Importance of feature selection for recurrent neural network based forecasting of building thermal comfort

Martin Macas, Fabio Moretti, Fiorella Lauro, Stefano Pizzuti, Mauro Annunziato, Alessandro Fonti, Gabriele Comodi, Andrea Giantomassi

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of thermal comfort for office building heated by gas. Although the accuracy of the forecasting is similar for both the feed-forward and the recurrent network, the removal of features leads to accuracy reduction much earlier for the feed-forward network. The recurrent network can perform well even with less than 50% of features. This brings significant benefits in scenarios, where the neural network is used as a blackbox model of thermal comfort, which is called by an optimizer that minimizes the deviance from a target value. The reduction of input dimensionality can lead to reduction of costs related to measurement equipment, data transfer and also computational demands of optimization. © 2014 Springer International Publishing Switzerland.
Original languageEnglish
DOIs
Publication statusPublished - 2014
Externally publishedYes
Event3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014 - , United Kingdom
Duration: 1 Jan 2014 → …

Conference

Conference3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014
CountryUnited Kingdom
Period1/1/14 → …

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Macas, M., Moretti, F., Lauro, F., Pizzuti, S., Annunziato, M., Fonti, A., Comodi, G., & Giantomassi, A. (2014). Importance of feature selection for recurrent neural network based forecasting of building thermal comfort. Paper presented at 3rd International Conference on Adaptive and Intelligent Systems, ICAIS 2014, United Kingdom. https://doi.org/10.1007/978-3-319-11298-5_2