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Pregled bibliografske jedinice broj: 1109141

Independent component analysis as a dimensionality reduction method on public sector buildings energy data


Knežević, Marinela
Independent component analysis as a dimensionality reduction method on public sector buildings energy data // PROCEEDINGS of the ISCCRO - International Statistical Conference in Croatia / Žmuk, B. ; Čeh Časni, A. (ur.).
Zagreb: Croatian Statistical Association (CSA) / Hrvatsko statističko društvo (HSD), 2020. str. 20-20 (predavanje, međunarodna recenzija, sažetak, znanstveni)


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Naslov
Independent component analysis as a dimensionality reduction method on public sector buildings energy data

Autori
Knežević, Marinela

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
PROCEEDINGS of the ISCCRO - International Statistical Conference in Croatia / Žmuk, B. ; Čeh Časni, A. - Zagreb : Croatian Statistical Association (CSA) / Hrvatsko statističko društvo (HSD), 2020, 20-20

Skup
ISCCRO - International Statistical Conference in Croatia

Mjesto i datum
Zagreb, Hrvatska, 15.10.2020. - 16.10.2020

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
dimensionality reduction ; feature extraction ; independent component analysis ; large data sets

Sažetak
Excessive energy consumption affects large financial costs as well as the environment pollution, contributing to climate change and global warming. Croatia is among the highest ten energy intensity countries in European Union. Furthermore, since that 40% of all energy consumption in the European Union belongs to the building sector, this sector deserves attention. In that sense, the modelling of energy consumption is quite important topic. In this research, a real data set obtained from Croatian Energy Management Information System was used. It consisted of the constructional, geospatial, meteorological, occupational and energy data of public sector buildings in Croatia. It can be considered as a large data set, having a lot of observations and consisting of large number of variables. In this paper, the difficulties caused by the large number of variables in the model will be mentioned and the independent components analysis (ICA) as one method for dimensionality reduction will be discussed. ICA is a method for finding underlying factors or components from multivariate statistical data that looks for components that are both statistically independent and nongaussian. In this paper, the aforementioned method for reducing the dimensionality will be applied, as a step that will precede the modelling of the energy costs of public sector buildings depending on their characteristics.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Ekonomija, Informacijske i komunikacijske znanosti



POVEZANOST RADA


Projekti:
IP-2016-06-8350 - Metodološki okvir za učinkovito upravljanje energijom s pomoću inteligentne podatkovne analitike (MERIDA) (Zekić-Sušac, Marijana, HRZZ - 2016-06) ( CroRIS)

Ustanove:
Ekonomski fakultet, Osijek,
Fakultet organizacije i informatike, Varaždin

Profili:

Avatar Url Marinela Mokriš (autor)


Citiraj ovu publikaciju:

Knežević, Marinela
Independent component analysis as a dimensionality reduction method on public sector buildings energy data // PROCEEDINGS of the ISCCRO - International Statistical Conference in Croatia / Žmuk, B. ; Čeh Časni, A. (ur.).
Zagreb: Croatian Statistical Association (CSA) / Hrvatsko statističko društvo (HSD), 2020. str. 20-20 (predavanje, međunarodna recenzija, sažetak, znanstveni)
Knežević, M. (2020) Independent component analysis as a dimensionality reduction method on public sector buildings energy data. U: Žmuk, B. & Čeh Časni, A. (ur.)PROCEEDINGS of the ISCCRO - International Statistical Conference in Croatia.
@article{article, author = {Kne\v{z}evi\'{c}, Marinela}, year = {2020}, pages = {20-20}, keywords = {dimensionality reduction, feature extraction, independent component analysis, large data sets}, title = {Independent component analysis as a dimensionality reduction method on public sector buildings energy data}, keyword = {dimensionality reduction, feature extraction, independent component analysis, large data sets}, publisher = {Croatian Statistical Association (CSA) / Hrvatsko statisti\v{c}ko dru\v{s}tvo (HSD)}, publisherplace = {Zagreb, Hrvatska} }
@article{article, author = {Kne\v{z}evi\'{c}, Marinela}, year = {2020}, pages = {20-20}, keywords = {dimensionality reduction, feature extraction, independent component analysis, large data sets}, title = {Independent component analysis as a dimensionality reduction method on public sector buildings energy data}, keyword = {dimensionality reduction, feature extraction, independent component analysis, large data sets}, publisher = {Croatian Statistical Association (CSA) / Hrvatsko statisti\v{c}ko dru\v{s}tvo (HSD)}, publisherplace = {Zagreb, Hrvatska} }




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