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Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 March 2021) | Viewed by 7658

Special Issue Editors

Prof. Dr. Aminah Robinson Fayek

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB T6G 2R3, Canada
Interests: fuzzy logic; fuzzy hybrid systems; machine learning; decision support systems; simulation; optimization; system dynamics; agent-based modeling; subjective knowledge; construction
Special Issues, Collections and Topics in MDPI journals
Dr. Nima Gerami Seresht

E-Mail Website
Guest Editor
Department of Mechanical & Construction Engineering, Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8QH, UK
Interests: resilient infrastructure; sustainability of built environment; agent-based modelling; system dynamics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Renewable energy projects have recently gained popularity due to their low adverse environmental impacts. While the improvement of the construction and maintenance of such projects requires that project and operation managers make the right decisions in a timely fashion, the complexity and novelty of these projects leads to numerous challenges related to decision-making. Renewable energy projects involve numerous uncertain factors; these projects often require managers to coordinate many complex and dynamic processes for decision-making; and managers must consider sometimes contradictory criteria and/or objectives for decision-making. In recent years, the application of advanced modeling and computational techniques has emerged in different engineering disciplines to develop decision support systems for supporting practitioners in dealing with such challenges. This Special Issue focuses on the development and application of decision support systems for improving the construction and maintenance of renewable energy projects. It also includes extensions of selected papers from the 9th Asia-Pacific International Symposium on Advanced Reliability and Maintenance Modelling (APARM 2020).

Papers are invited that cover, but are not limited to, the main topics of:

  • Risk analysis and management for the construction of renewable energy infrastructure
  • Decision-making for the construction or maintenance of renewable energy infrastructure
  • Fault detection models for renewable energy infrastructure
  • Simulation modeling of renewable energy infrastructure projects during construction, operation, and maintenance phases
  • Artificial intelligence modeling of renewable energy infrastructure projects during construction, operation, and maintenance phases
  • Decision-making for design and development of renewable energy infrastructure projects
  • Health monitoring methods for the assessment of renewable energy infrastructure projects

Prof. Dr. Aminah Robinson Fayek
Dr. Nima Gerami Seresht
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Risk analysis
  • computational techniques
  • artificial intelligence
  • machine learning
  • optimization
  • simulation
  • renewable energy
  • infrastructure
  • construction
  • operation
  • maintenance
  • decision-making
  • uncertainty modeling

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Published Papers (2 papers)

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Research

27 pages, 5278 KiB  
Article
Simulation-Based Approach for Lookahead Scheduling of Onshore Wind Projects Subject to Weather Risk
by Emad Mohamed, Parinaz Jafari, Adam Chehouri and Simaan AbouRizk
Sustainability 2021, 13(18), 10060; https://doi.org/10.3390/su131810060 - 8 Sep 2021
Cited by 4 | Viewed by 2813
Abstract
Executed outdoors in high-wind areas, adverse weather conditions represent a significant risk to onshore wind farm construction activities. While methods for considering historical weather data during pre-construction scheduling are available, approaches capable of quantitatively assessing how short-term weather fluctuations may impact upcoming construction [...] Read more.
Executed outdoors in high-wind areas, adverse weather conditions represent a significant risk to onshore wind farm construction activities. While methods for considering historical weather data during pre-construction scheduling are available, approaches capable of quantitatively assessing how short-term weather fluctuations may impact upcoming construction activities have yet to be developed. This study is proposing a hybrid simulation-based approach that uses short-term precipitation, wind speed, and temperature forecasts together with planned and as-built activity durations to develop lookahead (e.g., upcoming 14-day) schedules for improved project planning and control. Functionality and applicability of the method was demonstrated on a case study of a 40 MW onshore wind project, and the method was validated using event validity, face validation, and sensitivity analysis. As expected, favorable weather conditions experienced during the tested lookahead periods resulted in a negligible impact (less than 10% reduction) on the productivity of weather-sensitive activities, which translated into a project delay of one day. The responsiveness of the framework was confirmed through sensitivity analysis, which demonstrated a 50% reduction in productivity resulting from poor weather conditions. The ability of the method to provide decision-support not currently offered by commercially-available scheduling systems was confirmed by subject experts, who endorsed the ability of the method to enhance lookahead scheduling and to facilitate the monitoring and control of weather impact uncertainty on project durations. Full article
(This article belongs to the Special Issue Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects)
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11 pages, 1213 KiB  
Article
Framework for Risk Identification of Renewable Energy Projects Using Fuzzy Case-Based Reasoning
by Sahand Somi, Nima Gerami Seresht and Aminah Robinson Fayek
Sustainability 2020, 12(13), 5231; https://doi.org/10.3390/su12135231 - 27 Jun 2020
Cited by 15 | Viewed by 3693
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Decision Support Systems for Improving the Construction and Maintenance of Renewable Energy Projects)
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