Comprehensive Analysis of Renewable Energy Technologies Adoption in Remote Areas Using the Integrated Delphi-Fuzzy AHP-VIKOR Approach

Abdul, Daud; Wenqi, Jiang; Tanveer, Arsalan; Sameeroddin, Mohd.

doi:10.1007/s13369-023-08334-2

Cited by 6 publications

(1 citation statement)

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“…This study employs a detailed methodology to assess the AI-Integrated Energy Education Framework using sophisticated fuzzy decision-making techniques. The evaluation process incorporates two main methods: the Fuzzy Delphi Method [39] and the Fuzzy Analytic Hierarchy Process (FAHP) [40]. Initially, the Fuzzy Delphi Method is utilized to gather and synthesize expert opinions and collective intelligence regarding each layer and key factor of the framework.…”

Section: Research Design and Methodologymentioning

confidence: 99%

Artificial Intelligence Integrated Energy Education Framework. A Holistic Approach

Stecyk,

Miciuła

2024

Preprint

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This scientific article outlines the Artificial Intelligence Integrated Energy Education Framework (AI-IEEF), a transformative model designed to revolutionize energy education and management practices. The framework is organized into five distinct layers: Organizational (AI-enhanced administration systems), Financial (dynamic AI financial models), Technology (advanced simulation and modeling), Methodology (AI in curriculum development and personalized learning), and Social (AI for community engagement and impact). An expert panel used the fuzzy Delphi method to achieve consensus on twenty key factors within these layers, establishing a solid foundation for analysis. Following this, the Fuzzy Analytic Hierarchy Process (AHP) was employed to calculate precise weights for each layer and their respective factors, providing a quantitative assessment of their relative importance. These weight calculations are crucial, as they guide resource allocation and strategic decision-making to ensure the framework is optimized for the evolving needs of energy management. Furthermore, the article introduces five tailored variants of the AI-IEEF, each addressing specific aspects of energy education and offering a comprehensive approach to navigating the challenges in the field. The five variants include the Smart Campus Energy Education, Global Energy Policy Analysis, Renewable Energy Research and Development, Energy Workforce Development, and Community Engagement and Outreach variants. Each provides a distinct approach to energy education, from transforming campuses into living labs for hands-on learning to fostering international collaborations that explore the global implications of energy policy. These variants emphasize practical skills, policy analysis, and community-focused solutions, ensuring that students are well-prepared to contribute effectively to the energy sector.

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Section: Research Design and Methodologymentioning

confidence: 99%