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

Umair, Zuneera, Zwetsloot, Inez M., Marco, Luk Kin Ming, Shim, Jiwoo, and Kostromin, Daniil (2021) Designing a System for Data-driven Risk Assessment of Solar Projects. In: Proceedings of the Annual Conference of the IEEE Industrial Electronics Conference. From: IECON 2021: 47th Annual Conference of the IEEE Industrial Electronics Conference, 13-16 October 2021, Toronto, ON, Canada.

Full text not available from this repository
View at Publisher Website: https://doi.org/10.1109/IECON48115.2021....
2


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.

Item ID: 93784
Item Type: Conference Item (Research - E1)
ISBN: 9781665435543
Keywords: data analytics, decision support system, PV project, quality assurance, risk assessment, solar energy project
Copyright Information: © IEEE 2021.
Date Deposited: 16 Sep 2026 02:54
FoR Codes: 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460501 Data engineering and data science @ 100%
SEO Codes: 22 INFORMATION AND COMMUNICATION SERVICES > 2299 Other information and communication services > 229999 Other information and communication services not elsewhere classified @ 100%
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page