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

Zuneera Umair; Inez M. Zwetsloot; Kin Ming Marco Luk; Jiwoo Shim; Daniil Kostromin

doi:10.1109/IECON48115.2021.9589414

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

Zuneera Umair
, Inez M. Zwetsloot
, Kin Ming Marco Luk
, Jiwoo Shim
, Daniil Kostromin

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781665435543
ISBN (Print)	9781665402569
DOIs	https://doi.org/10.1109/IECON48115.2021.9589414
Publication status	Published - 16 Oct 2021
Event	IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society - Toronto, Canada Duration: 13 Oct 2021 → 16 Oct 2021 https://ieeexplore.ieee.org/xpl/conhome/9588332/proceeding

Publication series

Name	Annual Conference of Industrial Electronics Society - Proceedings
ISSN (Print)	1553-572X
ISSN (Electronic)	2577-1647

Conference

Conference	IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society
Country/Territory	Canada
City	Toronto
Period	13/10/21 → 16/10/21
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9588332/proceeding

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

User-Defined Keywords

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

Access to Document

10.1109/IECON48115.2021.9589414

Cite this

@inproceedings{b2b35bcc2f2142e2b7a4b3a907dd80d0,

title = "Designing a System for Data-driven Risk Assessment of Solar Projects",

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.",

keywords = "data analytics, decision support system, PV project, quality assurance, risk assessment, solar energy project",

author = "Zuneera Umair and Zwetsloot, \{Inez M.\} and Luk, \{Kin Ming Marco\} and Jiwoo Shim and Daniil Kostromin",

year = "2021",

month = oct,

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doi = "10.1109/IECON48115.2021.9589414",

language = "English",

isbn = "9781665402569",

series = "Annual Conference of Industrial Electronics Society - Proceedings",

publisher = "IEEE",

pages = "1--6",

booktitle = "IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society",

address = "United States",

note = "IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society ; Conference date: 13-10-2021 Through 16-10-2021",

url = "https://ieeexplore.ieee.org/xpl/conhome/9588332/proceeding",

}

Umair, Z, Zwetsloot, IM, Luk, KMM, Shim, J & Kostromin, D 2021, Designing a System for Data-driven Risk Assessment of Solar Projects. in IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. Annual Conference of Industrial Electronics Society - Proceedings, IEEE, pp. 1-6, IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, Ontario, Canada, 13/10/21. https://doi.org/10.1109/IECON48115.2021.9589414

Designing a System for Data-driven Risk Assessment of Solar Projects. / Umair, Zuneera; Zwetsloot, Inez M.; Luk, Kin Ming Marco et al.
IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2021. p. 1-6 (Annual Conference of Industrial Electronics Society - Proceedings).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Designing a System for Data-driven Risk Assessment of Solar Projects

AU - Umair, Zuneera

AU - Zwetsloot, Inez M.

AU - Luk, Kin Ming Marco

AU - Shim, Jiwoo

AU - Kostromin, Daniil

PY - 2021/10/16

Y1 - 2021/10/16

N2 - 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.

AB - 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.

KW - data analytics

KW - decision support system

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KW - risk assessment

KW - solar energy project

U2 - 10.1109/IECON48115.2021.9589414

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T2 - IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society

Y2 - 13 October 2021 through 16 October 2021

ER -

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

Abstract

Publication series

Conference

UN SDGs

User-Defined Keywords

Access to Document

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Cite this