Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00218399" target="_blank" >RIV/68407700:21230/14:00218399 - isvavai.cz</a>
Result on the web
<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-07995-0_26#page-1" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-07995-0_26#page-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-07995-0_26" target="_blank" >10.1007/978-3-319-07995-0_26</a>
Alternative languages
Result language
angličtina
Original language name
Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin
Original language description
The paper demonstrates the importance of feature selection for recurrent neural network applied to problem of one hour ahead forecasting of gas consumption for office building heating. 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 50% of features. This brings significant benefits in scenarios, where theneural network is used as a blackbox model of building consumption, which is called by an optimizer that minimizes the consumption. The reduction of input dimensionality leads to reduction of costs related to measurement equipment, but also costs relatedto data transfer.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
JC - Computer hardware and software
OECD FORD branch
—
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
Proceedings of the International Joint Conference SOCO?14-CISIS?14-ICEUTE?14
ISBN
978-3-319-07994-3
ISSN
2194-5357
e-ISSN
—
Number of pages
10
Pages from-to
259-268
Publisher name
Springer
Place of publication
Heidelberg
Event location
Bilbao
Event date
Jun 25, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000343754200026