A smart grids knowledge transfer paradigm supported by experts' throughput modeling artificial intelligence algorithmic processes

Rodgers, Waymond; Cardenas, Jesus A.; Gemoets, Leopoldo A.; Sarfi, Robert J.

Abstract

This paper presents an artificial intelligence algorithmic knowledge transfer approach to the models that have been developed throughout the world for smart grid networks. Many nations are moving forward to implement smarter ways to generate, distribute and network energy, while others are expecting the leading countries to take the initiative and then follow suit. Therefore, we theoretically identify three dimensions of experts' competencies—perception, judgment, and decision choice supported by the Throughput Model algorithms for knowledge transfer. Integrating the Throughput Model algorithmic framework and Deming Cycle (i.e., plan, do, check, act), we propose that Information and Communication Technology (ICT) systems influence experts' decision making towards implementation of Smart Grids (SG). This model was backed up with the perspectives of 32 global experts as surveyed using Carnegie Mellon Maturity model questions and analyzed the results using PLS to validate the findings and compare them to our enhanced knowledge transfer developed from Deming's PDCA cycle. Our results suggest that these key algorithmic decision-making components are critical in explaining the successful application of planning, doing, checking/ acting, and planning of renewable energy technology as well as for a greener environment.

Citation

Rodgers, W., Cardenas, J. A., Gemoets, L. A., & Sarfi, R. J. (2023). A smart grids knowledge transfer paradigm supported by experts' throughput modeling artificial intelligence algorithmic processes. Technological Forecasting and Social Change, 190, Article 122373. https://doi.org/10.1016/j.techfore.2023.122373

• Journal Article Type: Article
• Acceptance Date: Jan 21, 2023
• Online Publication Date: Mar 3, 2023
• Publication Date: 2023-05
• Deposit Date: Mar 16, 2023
• Publicly Available Date: Mar 17, 2023
• Journal: Technological Forecasting and Social Change
• Print ISSN: 0040-1625
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 190
• Article Number: 122373
• DOI: https://doi.org/10.1016/j.techfore.2023.122373
• Keywords: Artificial intelligence; Algorithms; Decision making; Knowledge management; Throughput model
• Public URL: https://hull-repository.worktribe.com/output/4241248