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Building rooftops extraction from satellite images using machine learning algorithms for solar photovoltaic potential estimation / Eslam Mustafa Mahmoud Muhammed ; Supervised Adelelshazly , Salem Morsy

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Eslam Mustafa Mahmoud Muhammed , 2021Description: 87 P . : charts , facsmilies , maps ; 30cmOther title:
  • استخلاص أسطح المبانى من صور الأقمار الصناعية باستخدام خوارزميات التعلم الآلى لتقدير الطاقة الشمسية الكهروضوئية المحتملة [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering Summary: This research involves the extraction of buildings from satellite images to be used for solar PV potential estimation. Pre-processing algorithms were used to enhance the image of a city in Cairo, Egypt called Madinaty. Then, two machine learning techniques were used to extract the buildings{u2019} rooftops. SVM exceeded Naïve Bayes in terms of the detection accuracy with an F1 score of 94.7%. The detected gross areas of the rooftops were used in the second phase of this thesis, which is the PV potential estimation of PV panels mounted over the detected rooftops. Three methods were used for the solar modeling of the study area which are PVWatts calculator, PVGIS, and ArcGIS. The estimated PV potentials were calculated to be 21, 24.9, and 22.3 GWh/year for the three methods respectively. CO2 was reduced by an average of 62% after using solar panels instead of depending on traditional energy sources
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Item type Current library Home library Call number Copy number Status Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2021.Es.B (Browse shelf(Opens below)) Not for loan 01010110085474000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.13.05.M.Sc.2021.Es.B (Browse shelf(Opens below)) 85474.CD Not for loan 01020110085474000

Thesis (M.Sc.) - Cairo University - Faculty of Engineering - Department of Civil Engineering

This research involves the extraction of buildings from satellite images to be used for solar PV potential estimation. Pre-processing algorithms were used to enhance the image of a city in Cairo, Egypt called Madinaty. Then, two machine learning techniques were used to extract the buildings{u2019} rooftops. SVM exceeded Naïve Bayes in terms of the detection accuracy with an F1 score of 94.7%. The detected gross areas of the rooftops were used in the second phase of this thesis, which is the PV potential estimation of PV panels mounted over the detected rooftops. Three methods were used for the solar modeling of the study area which are PVWatts calculator, PVGIS, and ArcGIS. The estimated PV potentials were calculated to be 21, 24.9, and 22.3 GWh/year for the three methods respectively. CO2 was reduced by an average of 62% after using solar panels instead of depending on traditional energy sources

Issued also as CD

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