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Title: PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS
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PREDICTION OF SOLAR POTENTIAL IN URBAN AREAS USING REMOTE SENSING METHODS
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1314-2704
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English
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19
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2.2
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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).
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conference
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19th International Multidisciplinary Scientific GeoConference SGEM 2019
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19th International Multidisciplinary Scientific GeoConference SGEM 2019, 30 June - 6 July, 2019
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Proceedings Paper
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STEF92 Technology
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International Multidisciplinary Scientific GeoConference-SGEM
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Bulgarian Acad Sci; Acad Sci Czech Republ; Latvian Acad Sci; Polish Acad Sci; Russian Acad Sci; Serbian Acad Sci & Arts; Slovak Acad Sci; Natl Acad Sci Ukraine; Natl Acad Sci Armenia; Sci Council Japan; World Acad Sci; European Acad Sci, Arts & Letters; Ac
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593-600
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30 June - 6 July, 2019
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website
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cdrom
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5537
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solar potential; aerial images; Worldview2; remote sensing; classification
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