Sarkar, Abu Musa (2022) Artificial Intelligence and Advanced Optimization Framework for Next-Generation Renewable Energy Manufacturing and Supply Chain Resilience. American Journal of Economics and Business Management, 5 (8). ISSN 2576-5973

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Abstract

The rapid global transition toward low-carbon energy systems has increased the need for intelligent and resilient renewable energy manufacturing under uncertain operating conditions. The intermittency of renewable energy sources such as solar and wind creates major challenges for forecasting, resource allocation, operational efficiency, and supply chain stability. This study examines the integration of artificial intelligence (AI) and stochastic optimization as a robust framework for enhancing renewable energy manufacturing and sustainable energy management in uncertain environments. AI technologies, including machine learning, predictive analytics, and digital twins, enable real-time monitoring, forecasting, predictive maintenance, and intelligent decision-making to improve system efficiency and reliability. Stochastic optimization complements these capabilities by incorporating uncertainty into mathematical decision models, supporting optimal energy scheduling, resource allocation, storage management, and cost-effective planning under variable supply and demand conditions. Particular attention is given to applications in microgrids, energy storage, photovoltaic systems, wind energy, and hydrogen production, where AI-driven optimization improves system resilience, operational flexibility, and energy conversion efficiency. The study also discusses key implementation challenges, including poor data quality, interoperability limitations, scalability issues, regulatory constraints, and the need for explainable AI frameworks. Overall, the integration of AI and stochastic optimization provides a promising pathway for strengthening renewable energy manufacturing and supply chain resilience, improving sustainability, reducing operational risks, and accelerating the transition toward reliable, low-carbon, and zero-carbon energy systems.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence (AI), Stochastic Optimization, Renewable Energy Manufacturing, Supply Chain Resilience, Sustainable Energy Systems
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	02 Jul 2026 02:44
Last Modified:	02 Jul 2026 02:44
URI:	http://eprints.umsida.ac.id/id/eprint/16719

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