Importance of Feature Selection for Recurrent Neural Network Based Forecasting of Building Thermal Comfort
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00219453" target="_blank" >RIV/68407700:21230/14:00219453 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-11298-5_2#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-11298-5_2#page-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-11298-5_2" target="_blank" >10.1007/978-3-319-11298-5_2</a>
Alternative languages
Result language
angličtina
Original language name
Importance of Feature Selection for Recurrent Neural Network Based Forecasting of Building Thermal Comfort
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GP13-21696P" target="_blank" >GP13-21696P: Feature selection for temporal context aware models of multivariate time series</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Adaptive and Intelligent Systems - LNAI 8779
ISBN
978-3-319-11297-8
ISSN
0302-9743
e-ISSN
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Number of pages
9
Pages from-to
11-19
Publisher name
Springer
Place of publication
Heidelberg
Event location
Bournemouth
Event date
Sep 8, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000346932400002