Author
Abstract
Africa's energy transmission infrastructure remains one of the most critical barriers to continental development, with over 600 million people still lacking reliable electricity access. The integration of artificial intelligence (AI) into Integrated Project Delivery (IPD) frameworks presents a transformative opportunity to accelerate infrastructure modernization while aligning with global Sustainable Development Goals (SDGs). This article develops a comprehensive AI-driven IPD framework tailored to African energy transmission contexts, drawing on an extensive synthesis of literature spanning AI applications in energy systems, smart grid technologies, renewable energy optimization, and African development policy. The proposed framework spans seven interdependent phases from conceptualization through long-term operational analytics embedding machine learning, IoT-AI integration, digital twin modeling, and predictive maintenance throughout the project lifecycle. By examining country-level case studies, policy landscapes, technical challenges, and continental opportunity structures, this work provides actionable guidance for policymakers, project developers, and technology implementers. The framework demonstrates how AI-IPD can reduce project delivery timelines by up to 30%, improve grid reliability, optimize renewable energy integration, and support inclusive development across all African sub-regions. This research contributes a novel, Africa-specific conceptual model and roadmap for harnessing AI as a strategic accelerator of energy infrastructure transformation.
Suggested Citation
Lukhanyiso Mpongoshe, 2024.
"AI-Driven Integrated Project Delivery Frameworks for Accelerating African Energy Transmission Infrastructure Modernization,"
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10(4), pages 1143-1160, August.
Handle:
RePEc:jbh:ijsrcs:v10:y2024:i4:id:2034
DOI: 10.32628/CSEIT26123339
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT26123339
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