About: Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin

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About: Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin Goto Sponge NotDistinct Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://link.springer.com/chapter/10.1007%2F978-3-319-07995-0_26#page-1
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 the neural 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 related to data transfer. 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 the neural 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 related to data transfer. (en)
Title	Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin (en)
skos:prefLabel	Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin Sensitivity Based Feature Selection for Recurrent Neural Network Applied to Forecasting of Heating Gas Consumptin (en)
skos:notation	RIV/68407700:21230/14:00218399!RIV15-GA0-21230___
http://linked.open...avai/riv/aktivita	P
http://linked.open...avai/riv/aktivity	P(GP13-21696P)
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Macaš, Martin
http://linked.open.../riv/druhVysledku	D - Článek ve sborníku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	44458
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00218399
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Forecasting; Consumption; Gas; Heating; Neural Networks; Feature Selection (en)
http://linked.open.../riv/klicoveSlovo	Feature Selection Consumption Heating Forecasting Gas Neural Networks
http://linked.open...ontrolniKodProRIV	[0B0E94FE8E86]
http://linked.open...v/mistoKonaniAkce	Bilbao
http://linked.open...i/riv/mistoVydani	Heidelberg
http://linked.open...i/riv/nazevZdroje	Proceedings of the International Joint Conference SOCO’14-CISIS’14-ICEUTE’14
http://linked.open...in/vavai/riv/obor	JC
http://linked.open...ichTvurcuVysledku	1 (xsd:int)
http://linked.open...cetTvurcuVysledku	8 (xsd:int)
http://linked.open...vavai/riv/projekt	Selekce příznaků pro modely vícedimenzionálních časových řad uvažující časový kontext
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Moretti, F. Macaš, Martin Lauro, F. Pizzuti, S. Annuziato, M. Comodi, G. Fonti, A. Giantomassi, A.
http://linked.open...vavai/riv/typAkce	WRD - Světová
http://linked.open...ain/vavai/riv/wos	000343754200026
http://linked.open.../riv/zahajeniAkce	2014-06-25 (xsd:date)
issn	2194-5357
number of pages	10 (xsd:int)
http://bibframe.org/vocab/doi	10.1007/978-3-319-07995-0_26
http://purl.org/ne...btex#hasPublisher	Springer-Verlag
https://schema.org/isbn	978-3-319-07994-3
http://localhost/t...ganizacniJednotka	21230

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