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A Data-Driven Approach for Localization and Power Generation Estimation of Invisible Solar Resources.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	A Data-Driven Approach for Localization and Power Generation Estimation of Invisible Solar Resources./
Author:	Sahin, Esma.
Description:	1 online resource (99 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
Subject:	Energy. -
Online resource:	click for full text (PQDT)
ISBN:	9798381108477

A Data-Driven Approach for Localization and Power Generation Estimation of Invisible Solar Resources.
Sahin, Esma.

A Data-Driven Approach for Localization and Power Generation Estimation of Invisible Solar Resources. - 1 online resource (99 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--The George Washington University, 2024.

Includes bibliographical references

In recent years, due to the rapid increase in the number of solar panels in recent years, a portion of the daily power demand is being met by the energy generated by these solar panels. This study presents a data-driven approach to estimate how much of the demand can be met by off-grid solar panels. The research consists of two phases. In the first phase, the localization of rooftop solar panels from aerial photographs is worked on using U-net image segmentation. The use of U-net for solar panel detection is evaluated with various efficiency parameters. In the second phase, the goal is to predict the power generation of these solar panels using machine learning algorithms and climate data. Power prediction is performed using five different machine learning algorithms, and the performance of these algorithms is numerically evaluated and compared.In conclusion, this study takes an important step in evaluating the role of solar panels in energy production and understanding the future potential of solar energy usage. Such technological advancements can enhance sustainability in the energy sector and reduce the challenging environmental impacts in our modern society.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381108477Subjects--Topical Terms:

784773
Energy.
Subjects--Index Terms:

EstimationIndex Terms--Genre/Form:

554714
Electronic books.

A Data-Driven Approach for Localization and Power Generation Estimation of Invisible Solar Resources.
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504 $a Includes bibliographical references
520 $a In recent years, due to the rapid increase in the number of solar panels in recent years, a portion of the daily power demand is being met by the energy generated by these solar panels. This study presents a data-driven approach to estimate how much of the demand can be met by off-grid solar panels. The research consists of two phases. In the first phase, the localization of rooftop solar panels from aerial photographs is worked on using U-net image segmentation. The use of U-net for solar panel detection is evaluated with various efficiency parameters. In the second phase, the goal is to predict the power generation of these solar panels using machine learning algorithms and climate data. Power prediction is performed using five different machine learning algorithms, and the performance of these algorithms is numerically evaluated and compared.In conclusion, this study takes an important step in evaluating the role of solar panels in energy production and understanding the future potential of solar energy usage. Such technological advancements can enhance sustainability in the energy sector and reduce the challenging environmental impacts in our modern society.
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538 $a Mode of access: World Wide Web
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650 4 $a Electrical engineering. $3 596380
653 $a Estimation
653 $a Invisible
653 $a Localization
653 $a Off-grid solar panels
653 $a Power prediction
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