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Pregled bibliografske jedinice broj: 1027313

PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS


Majcen, Vedran; Krtalić, Andrija
PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS // 19th International Multidisciplinary Scientific GeoConference SGEM 2019 ; Conference Proceedings, Volume 19 ; Informatics, Geoinformatics and Remote Sensing, Issue: 2.2, Geodesy and Mine Surveying, Photogrammetry and Remote Sensing, Cartography and GIS
Sofia, Bugarska: STEF92 Technology, 2019. str. 593-600 doi:10.5593/sgem2019/2.2/s10.073 (ostalo, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)


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Naslov
PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS

Autori
Majcen, Vedran ; Krtalić, Andrija

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
19th International Multidisciplinary Scientific GeoConference SGEM 2019 ; Conference Proceedings, Volume 19 ; Informatics, Geoinformatics and Remote Sensing, Issue: 2.2, Geodesy and Mine Surveying, Photogrammetry and Remote Sensing, Cartography and GIS / - Sofia, Bugarska : STEF92 Technology, 2019, 593-600

ISBN
978-619-7408-80-5

Skup
19th International Multidisciplinary Scientific GeoConference SGEM 2019

Mjesto i datum
Sofia, Bugarska, 30.06.-06.07.2019

Vrsta sudjelovanja
Ostalo

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
solar potential ; aerial images ; Worldview2 ; remote sensing ; classification

Sažetak
More than 50% of the world’s populations live in urban areas, and they emit almost 80% of global carbon dioxide. They are also responsible for 75% of energy consumption in the world. That increases pressure on food, water, and especially energy. These facts imply that there is an urgent need to provide low carbon dioxide level in cities with efficient and renewable energy to foster the growth of the green economy. Therefore, attention should be paid to renewable energy sources such as the Sun and be aware of the potential of solar power of some areas. In favor of this assertion, recently published studies have suggested that solar power will generate 20% of global electricity by 2027. The above-mentioned facts and predictions were the motivation for conducting preliminary research on the possibility for predicting solar potential. The predictions of solar power can be provided by means of remote sensing methods. Digitalized analog VNIR aerial images (UMK camera) with very high spatial resolution and Worldview2 channels are used as input data. For the purpose of discriminating the roofs, the classification was made by controlling both sets of images. The average number of sun hours in one day and the average consumption of an ordinary household were calculated. Initial results obtained in this preliminary research are demonstrated in this paper for an urban neighborhood in Zagreb (Croatia).

Izvorni jezik
Engleski

Znanstvena područja
Geodezija, Interdisciplinarne tehničke znanosti



POVEZANOST RADA


Ustanove:
Geodetski fakultet, Zagreb

Profili:

Avatar Url Andrija Krtalić (autor)

doi

Citiraj ovu publikaciju

Majcen, Vedran; Krtalić, Andrija
PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS // 19th International Multidisciplinary Scientific GeoConference SGEM 2019 ; Conference Proceedings, Volume 19 ; Informatics, Geoinformatics and Remote Sensing, Issue: 2.2, Geodesy and Mine Surveying, Photogrammetry and Remote Sensing, Cartography and GIS
Sofia, Bugarska: STEF92 Technology, 2019. str. 593-600 doi:10.5593/sgem2019/2.2/s10.073 (ostalo, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
Majcen, V. & Krtalić, A. (2019) PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS. U: 19th International Multidisciplinary Scientific GeoConference SGEM 2019 ; Conference Proceedings, Volume 19 ; Informatics, Geoinformatics and Remote Sensing, Issue: 2.2, Geodesy and Mine Surveying, Photogrammetry and Remote Sensing, Cartography and GIS doi:10.5593/sgem2019/2.2/s10.073.
@article{article, year = {2019}, pages = {593-600}, DOI = {10.5593/sgem2019/2.2/s10.073}, keywords = {solar potential, aerial images, Worldview2, remote sensing, classification}, doi = {10.5593/sgem2019/2.2/s10.073}, isbn = {978-619-7408-80-5}, title = {PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS}, keyword = {solar potential, aerial images, Worldview2, remote sensing, classification}, publisher = {STEF92 Technology}, publisherplace = {Sofia, Bugarska} }
@article{article, year = {2019}, pages = {593-600}, DOI = {10.5593/sgem2019/2.2/s10.073}, keywords = {solar potential, aerial images, Worldview2, remote sensing, classification}, doi = {10.5593/sgem2019/2.2/s10.073}, isbn = {978-619-7408-80-5}, title = {PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS}, keyword = {solar potential, aerial images, Worldview2, remote sensing, classification}, publisher = {STEF92 Technology}, publisherplace = {Sofia, Bugarska} }

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  • Scopus


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