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Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning

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  • Sahand Somi

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Nima Gerami Seresht

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Aminah Robinson Fayek

    (Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

Abstract

Construction projects are highly risk-prone due to both internal factors (e.g., organizational, contractual, project, etc.) and external factors (e.g., environmental, economic, political, etc.). Construction risks can thus have a direct or indirect impact on project objectives, such as cost, time, safety, and quality. Identification of these risks is crucial in order to fulfill project objectives. Many tools and techniques have been proposed for risk identification, including literature review, questionnaire surveys, and expert interviews. However, the majority of these approaches are highly reliant on expert knowledge or prior knowledge of the project. Therefore, the application of such tools and techniques in risk identification for renewable energy projects (e.g., wind farm and solar power plant projects) is challenging due to their novelty and the limited availability of historical data or literature. This paper addresses these challenges by introducing a new risk identification framework for renewable energy projects, which combines case-based reasoning (CBR) with fuzzy logic. CBR helps to solve problems related to novel projects (e.g., renewable energy projects) based on their similarities to existing, well-studied projects (e.g., conventional energy projects). CBR addresses the issue of data scarcity by comparing novel types of construction projects to other well-studied project types and using the similarities between these two sets of projects to solve the different problems associated with novel types of construction projects, such as risk identification of renewable energy projects. Moreover, the integration of fuzzy logic with CBR, to develop fuzzy case-based reasoning (FCBR), increases the applicability of CBR in construction by capturing the subjective uncertainty that exists in construction-related problems. The applicability of the proposed framework was tested on a case study of an onshore wind farm project. The objectives of this paper are to introduce a novel framework for risk identification of renewable energy projects and to identify the risks associated with the construction of onshore wind farm projects at the work package level. The results of this paper will help to improve the risk management of renewable energy projects during the construction phase.

Suggested Citation

  • Sahand Somi & Nima Gerami Seresht & Aminah Robinson Fayek, 2020. "Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning," Sustainability, MDPI, vol. 12(13), pages 1-11, June.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5231-:d:377186
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    References listed on IDEAS

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    1. Doug Forbes & Simon Smith & Malcolm Horner, 2008. "Tools for selecting appropriate risk management techniques in the built environment," Construction Management and Economics, Taylor & Francis Journals, vol. 26(11), pages 1241-1250.
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    Cited by:

    1. Huirong Zhang & Zhenyu Zhang & Lixin Zhou & Shuangsheng Wu, 2021. "Case-Based Reasoning for Hidden Property Analysis of Judgment Debtors," Mathematics, MDPI, vol. 9(13), pages 1-17, July.
    2. Xin Ye & Wenhui Yu & Lina Lv & Shuying Zang & Hongwei Ni, 2021. "An Improved Case-Based Reasoning Model for Simulating Urban Growth," Sustainability, MDPI, vol. 13(11), pages 1-17, May.
    3. Wei Zhou & Chao Ke, 2022. "A Mass-Customization-Based Remanufacturing Scheme Design Method for Used Products," Sustainability, MDPI, vol. 14(16), pages 1-22, August.

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