Rooftop Detection for Planning of Solar PV Deployment: A Case Study in Abu Dhabi

Bikash Joshi, Baluyan Hayk, Amer Al-Hinai, Wei Lee Woon*

*المؤلف المقابل لهذا العمل

نتاج البحث: Chapter

10 اقتباسات (Scopus)

ملخص

Photovoltaic (PV) technology is one of two modes of energy generation which utilize solar energy as its source. The rooftops of buildings can be utilized for solar power generation using this technology, and are considered to be highly promising sites for urban PV installations due to land space limitations. However, to properly plan such installations decision makers would need to have detailed information about the amount of rooftop area that is available, as well as the distribution of individual rooftop sizes. In this paper a machine learning based approach for detecting rooftops is proposed and its utility for planning rooftop PV installations is demonstrated via a simple pilot study on two different residential areas in Abu Dhabi, UAE. The proposed method uses a twostage classification model to estimate the rooftop area that is available for solar panel installation. Next, a comparative study of three different types of PV technologies is conducted in terms of energy generation and economic viability. The results obtained from these experiments suggest that thin-film panels may have a distinct advantage over other PV technologies. Even though the cost of PV panels is still quite high, this could be balanced by the potential benefits to the environment. If reasonable subsidies and feed-in tariffs are implemented, PV technology can become a cost effective option for the UAE.

اللغة الأصليةEnglish
عنوان منشور المضيفData Analytics For Renewable Energy Integration
الصفحات137-149
عدد الصفحات13
مستوى الصوت8817
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2014

سلسلة المنشورات

الاسمLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ناشرSpringer Verlag
رقم المعيار الدولي للدوريات (المطبوع)0302-9743

ASJC Scopus subject areas

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