A Deep Learning Based Approach for Rooftop Solar Potential Estimation of a City: A Case Study of Indian Metropolis

Prakash, Prithvi; Aithal, Bharath H.

doi:10.1109/igarss47720.2021.9553088

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…They concluded that both methods are effective. Prakash and Vyas [145] use drone images and ML to calculate rooftop solar energy potential. Through auto-mated ML approaches, they identify suitable rooftops for solar panel installation in a village setting.…”

Section: • Solar Panelmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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Unmanned Aerial Vehicles (UAVs) play an important role in many applications, including health, transport, telecommunications and safe and rescue operations. Their adoption can improve the speed and precision of applications when compared to traditional solutions based on handwork. The use of UAVs brings scientific and technological challenges. In this context, Machine Learning (ML) techniques provide solutions to several problems concerning the use of UAVs in civil and military applications. An increasing number of scientific papers on the use of ML in UAVs context have been published in academic journals. In this work, we present a literature review on the use of ML techniques in UAVs, outlining the most recurrent areas and the most commonly used ML techniques in UAV applications. The results reveal that applications in the areas of environment, communication and security are among the main research topics.INDEX TERMS unmanned aerial vehicle, machine learning, literature review, UAV applications, neural networks.

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Section: • Solar Panelmentioning

confidence: 99%

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Teixeira,

Miguel,

Silva

et al. 2023

IEEE Access

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“…In [7], the authors use collected data about the solar radiation and the rooftop areas in Beirut city to estimate solar rooftop potential, without taking into consideration each rooftop morphology. A similar approach was conducted in [17] In [8], authors use high precision photographic sensors mounted on a UAV to scan and create a digital surface model (DSM) for a single building. Further statistical analysis of the output DSM model, in- cluding shading, solar irradiance, and panel placement, was conducted to estimate a single roof's solar potential.…”

Section: Solar Rooftop Potential Estimationmentioning

confidence: 99%

Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images

Nasrallah

Samhat

Shi

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Estimating solar rooftop potential at a national level is a fundamental building block for every country to utilize solar power efficiently. Solar rooftop potential assessment relies on several features such as building geometry, location, and surrounding facilities. Hence, national-level approximations that do not take these factors into deep consideration are often inaccurate. This paper introduces Lebanon's first comprehensive footprint and solar rooftop potential maps using deep learning-based instance segmentation to extract buildings' footprints from satellite images. A photovoltaic panels placement algorithm that considers the morphology of each roof is proposed. We show that the average rooftop's solar potential can fulfill the yearly electric needs of a single-family residence while using only 5% of the roof surface. The usage of 50% of a residential apartment rooftop area would achieve energy security for up to 8 households. We also compute the average and total solar rooftop potential per district to localize regions corresponding to the highest and lowest solar rooftop potential yield. Factors such as size, ground coverage ratio and P V out are carefully investigated for each district. Baalbeck district yielded the highest total solar rooftop potential despite its low built-up area. While, Beirut capital city has the highest average solar rooftop potential due to its extremely populated urban nature. Reported results and analysis reveal solar rooftop potential urban patterns and provides policymakers and key stakeholders with tangible insights. Lebanon's total solar rooftop potential is about 28.1 T W h/year, two times larger than the national energy consumption in 2019.

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A Deep Learning Based Approach for Rooftop Solar Potential Estimation of a City: A Case Study of Indian Metropolis

Cited by 2 publications

References 15 publications

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

A Survey on Applications of Unmanned Aerial Vehicles Using Machine Learning

Lebanon Solar Rooftop Potential Assessment Using Buildings Segmentation From Aerial Images

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