Transformative Opportunities from Data Science and Big Data Analytics: Applied to Photovoltaics

Distributed computing, data science, and machine learning are producing transformative changes across diverse research areas. Our research focuses on increasing the lifetime performance of photovoltaic (PV) module, and is essential to increasing PV energy generation on the electrical grid. Tradition...

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Vydáno v:The Electrochemical Society interface Ročník 28; číslo 1; s. 57 - 61
Hlavní autor: Bruckman, Laura S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: The Electrochemical Society 01.03.2019
ISSN:1064-8208, 1944-8783
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Abstract Distributed computing, data science, and machine learning are producing transformative changes across diverse research areas. Our research focuses on increasing the lifetime performance of photovoltaic (PV) module, and is essential to increasing PV energy generation on the electrical grid. Traditional analysis of PV modules is insufficient to determine accurate lifetimes of modules with different architectures deployed in diverse climatic zones. To solve this complex problem, a data science approach is needed to handle the large scale data on materials, modules, commercial power plants, and the grid. This approach involves data ingestion with a non-relational data warehouse and data driven modeling based on the underlying physics and chemistry. It is critical to assemble data, develop and share codes and tools, and report research results to the whole PV value chain, as opposed to just the PV research community.
AbstractList Distributed computing, data science, and machine learning are producing transformative changes across diverse research areas. Our research focuses on increasing the lifetime performance of photovoltaic (PV) module, and is essential to increasing PV energy generation on the electrical grid. Traditional analysis of PV modules is insufficient to determine accurate lifetimes of modules with different architectures deployed in diverse climatic zones. To solve this complex problem, a data science approach is needed to handle the large scale data on materials, modules, commercial power plants, and the grid. This approach involves data ingestion with a non-relational data warehouse and data driven modeling based on the underlying physics and chemistry. It is critical to assemble data, develop and share codes and tools, and report research results to the whole PV value chain, as opposed to just the PV research community.
Author Bruckman, Laura S.
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