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Machine Learning Based Prediction Mo...

Jaiswal, Rahul.

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Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization./
Author:	Jaiswal, Rahul.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	146 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
Subject:	Engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30315785
ISBN:	9798380606851

Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization.
Jaiswal, Rahul.

Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 146 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--The University of New Mexico, 2023.

This item must not be sold to any third party vendors.

Silicon heterojunction solar cell of Heterojunction with Thin Intrinsic Layer (HIT) structure is a commercially available technology, and its market share will significantly increase by the next decade. With such a significant market share, any minor improvement in the device's overall efficiency can be beneficial three folds - customer return on investment, industry revenue, and the overall carbon footprint (from manufacturing to recycling/ disposing of the device). Conventionally, device optimization for solar cells has been achieved using a hit & trial approach where multiple experiments are done to evaluate the best process conditions and device parameters. This approach has some inherent disadvantages, especially, because it is expensive in terms of resources, time, and manpower required. In the past couple of decades, simulation techniques are also being utilized in addition to the conventional approaches and very recently use of data science-based techniques has become popular in research and is gaining some traction in the photovoltaics industry.In this doctoral research, an innovative approach is presented, where device simulations for solar cells are designed and calibrated to match the performance of an industrially manufactured device using a minimal set of measurement data. Then a digital twin for the numerical simulations is developed by training Machine Learning (ML) models using simulation and measurement data. ML methods are also used for the calibration of simulation models in addition to being used forthe digital twin. The use of machine learning helps significantly reduce computational time to prototype any design changes in the device. In our work, apart from providing mean predictions, we are also providing a measure of uncertainty with every prediction, using Gaussian Process Regression (GPR).

ISBN: 9798380606851Subjects--Topical Terms:

586835
Engineering.
Subjects--Index Terms:

Calibration

Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization.
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245 1 0 $a Machine Learning Based Prediction Models for Silicon Heterojunction Solar Cell Optimization.
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300 $a 146 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
500 $a Advisor: Busani, Tito.
502 $a Thesis (Ph.D.)--The University of New Mexico, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Silicon heterojunction solar cell of Heterojunction with Thin Intrinsic Layer (HIT) structure is a commercially available technology, and its market share will significantly increase by the next decade. With such a significant market share, any minor improvement in the device's overall efficiency can be beneficial three folds - customer return on investment, industry revenue, and the overall carbon footprint (from manufacturing to recycling/ disposing of the device). Conventionally, device optimization for solar cells has been achieved using a hit & trial approach where multiple experiments are done to evaluate the best process conditions and device parameters. This approach has some inherent disadvantages, especially, because it is expensive in terms of resources, time, and manpower required. In the past couple of decades, simulation techniques are also being utilized in addition to the conventional approaches and very recently use of data science-based techniques has become popular in research and is gaining some traction in the photovoltaics industry.In this doctoral research, an innovative approach is presented, where device simulations for solar cells are designed and calibrated to match the performance of an industrially manufactured device using a minimal set of measurement data. Then a digital twin for the numerical simulations is developed by training Machine Learning (ML) models using simulation and measurement data. ML methods are also used for the calibration of simulation models in addition to being used forthe digital twin. The use of machine learning helps significantly reduce computational time to prototype any design changes in the device. In our work, apart from providing mean predictions, we are also providing a measure of uncertainty with every prediction, using Gaussian Process Regression (GPR).
590 $a School code: 0142.
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650 4 $a Alternative energy. $3 3436775
650 4 $a Materials science. $3 543314
653 $a Calibration
653 $a Digital twin
653 $a Machine learning
653 $a Silicon photovoltaics
653 $a Solar cells
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856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30315785