Designing a System for Data-driven Risk Assessment of Solar Projects

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

1 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Title of host publicationIECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society
PublisherIEEE
ISBN (Electronic)978-1-6654-3554-3
ISBN (Print)978-1-6654-0256-9
Publication statusPublished - 2021

Publication series

NameIECON Proceedings (Industrial Electronics Conference)
ISSN (Print)1553-572X
ISSN (Electronic)2577-1647

Conference

Title47th Annual Conference of the IEEE Industrial Electronics Society, IECON 2021
LocationVirtual
PlaceCanada
CityToronto
Period13 - 16 October 2021

Abstract

Solar energy is the fastest growing source of renewable energy worldwide, and is set to grow at an unprecedented pace for the coming years. Large scale solar energy projects now compete with conventional energy production and offer attractive returns to investors. Solar energy projects have a projected lifetime of over 25 years and while the returns are attractive, investors rarely oversee the risks that impact their Return on Investment (ROI) over the long term. In the wake of increasingly fiercer competition among PV module manufacturers, quality often takes a backseat. In this project, we propose a prognostic solution contrary to existing reactive approaches. We develop a data-driven decision support system (DSS) for technical derisking of utility scale solar energy projects. This system can provide project stakeholders insight into risks associated to different manufacturers. The system is based on data gathered by Sinovoltaics Group, a leading solar quality assurance company with 10+ years of experience. Using information extraction algorithms, useful data is extracted from a large number of quality assurance reports from Sinovoltaics Group, compiled in a database and analyzed for risk assessment leading to a DSS.

Research Area(s)

  • data analytics, decision support system, PV project, quality assurance, risk assessment, solar energy project

Citation Format(s)

Designing a System for Data-driven Risk Assessment of Solar Projects. / Umair, Zuneera; Zwetsloot, Inez M.; Luk, Kin Ming Marco; Shim, Jiwoo; Kostromin, Daniil.

IECON 2021 - 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review