Bibliographical progress in hybrid renewable energy systems’ integration, modelling, optimization, and artificial intelligence applications: A critical review and future research perspective

Wei, Pengyu; Bamisile, Olusola; Adun, Humphrey; Cai, Dongsheng; Obiora, Sandra; Li, Jian; Huang, Qi

doi:10.1080/15567036.2023.2181888

Cited by 9 publications

(8 citation statements)

References 139 publications

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“…The Delphi method promotes consensus-building among experts through iterative feedback, enhancing the credibility of their decisions. ISM helps analyze the structural aspects of decision problems by identifying the hierarchical relationships among factors [41], providing insights into which factors drive or depend on others. This hierarchy aids decision-makers in prioritizing actions and allocating resources effectively.…”

Section: Advantages and Drawbacks Of The Adopted Mcdm Toolsmentioning

confidence: 99%

“…Analyzing the interrelationships among factors with ISM and MICMAC can be computationally complex, particularly with many factors, limiting the scalability of this hybrid concept to larger problems. Additionally, parameterizing fuzzy set-based components, like membership functions and fuzzy rules, requires careful calibration and validation, making it difficult to determine accurate parameter values [41,42]. Despite these drawbacks, the benefits are compelling, motivating the authors to adopt these tools for the ongoing analysis.…”

Section: Advantages and Drawbacks Of The Adopted Mcdm Toolsmentioning

confidence: 99%

“…These factors encompass different AI technologies, methodologies, strategies, or considerations that affect how cost management is conducted, optimized, and improved. The importance of these critical AI parameters in cost management lies in their ability to enhance decision-making, optimize resource allocation, and increase project efficiency [41,45]. After several in-person meetings, the panel identified 17 AI factors that significantly influence cost management in civil engineering, which were further categorized into eight groups, as shown in Figure 1.…”

Section: The Critical Ai Factors Identified By the Expertsmentioning

confidence: 99%

“…Social impact (F52): Social impact focuses on community engagement, stakeholder satisfaction, and reduced project disruptions, ultimately contributing to project success. Considering social impact supports sustainable development, mitigates reputational risks, and ensures positive outcomes for both the project and surrounding communities [39,41,42,46].…”

mentioning

confidence: 99%

“…User interface (F72): A user-friendly interface promotes ease of use, reduces training time, and encourages the effective utilization of cost management software among stakeholders. An intuitive interface streamlines data entry, analysis, and reporting, leading to informed decision-making and optimal cost management practices within the project's budget [41][42][43][44]48,49].…”

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confidence: 99%

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Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Hu,

Jiang,

Goswami

et al. 2024

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This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks.

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Section: Advantages and Drawbacks Of The Adopted Mcdm Toolsmentioning

confidence: 99%

Section: Advantages and Drawbacks Of The Adopted Mcdm Toolsmentioning

confidence: 99%

Section: The Critical Ai Factors Identified By the Expertsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Hu,

Jiang,

Goswami

et al. 2024

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Green building practices to integrate renewable energy in the construction sector: a review

Chen,

Hu,

Wang

et al. 2023

Environ Chem Lett

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The building sector is significantly contributing to climate change, pollution, and energy crises, thus requiring a rapid shift to more sustainable construction practices. Here, we review the emerging practices of integrating renewable energies in the construction sector, with a focus on energy types, policies, innovations, and perspectives. The energy sources include solar, wind, geothermal, and biomass fuels. Case studies in Seattle, USA, and Manama, Bahrain, are presented. Perspectives comprise self-sufficiency, microgrids, carbon neutrality, intelligent buildings, cost reduction, energy storage, policy support, and market recognition. Incorporating wind energy into buildings can fulfill about 15% of a building's energy requirements, while solar energy integration can elevate the renewable contribution to 83%. Financial incentives, such as a 30% subsidy for the adoption of renewable technologies, augment the appeal of these innovations.

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Multi-objective optimization of solar energy systems for electricity and hot water generation in collective residential buildings considering the power-to-heat concept

Allouhi

Amine

Reisch

2023

Applied Thermal Engineering

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Bibliographical progress in hybrid renewable energy systems’ integration, modelling, optimization, and artificial intelligence applications: A critical review and future research perspective

Cited by 9 publications

References 139 publications

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering

Green building practices to integrate renewable energy in the construction sector: a review

Multi-objective optimization of solar energy systems for electricity and hot water generation in collective residential buildings considering the power-to-heat concept

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