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Optimization of Renewable Energy Systems Using Artificial Intelligence in Nigeria: A Focus on Generation and Distribution

Johnson Oluwatuyi Nelson, EmmanuelOjodu Oladipupo, Nneka Perpetua Onuoha, BEng, MEng, PhD, PGDE

Abstract

Nigeria’ s energy sector is marred by structural inefficiencies, unreliable grid infrastructure, and an overdependence on fossil fuels, resulting in inadequate electricity access and persistent blackouts. Despite possessing vast renewable energy resources—particularly solar and wind—the country continues to underutilize them due to technical, financial, and regulatory barriers. This study examines the transformative potential of Artificial Intelligence (AI) in addressing these challenges and optimizing renewable energy generation, distribution, and management. Employing a qualitative secondary research methodology, the study analyzes peer- reviewed literature, national policy documents, and global case studies to evaluate the applicability of AI technologies such as machine learning, neural networks, and predictive analytics within the Nigerian context. Case studies of solar farms, wind energy forecasting, smart grid operations, and urban load management illustrate how AI can enhance forecasting accuracy, improve grid reliability, reduce operational costs, and support decentralized energy access in rural areas. However, the adoption of AI faces significant hurdles, including high implementation costs, limited technical expertise, and a lack of robust data infrastructure. To overcome these barriers, the study recommends strategic public-private partnerships, targeted capacity-building programs, supportive data policy frameworks, and pilot initiatives to demonstrate feasibility. The integration of AI into Nigeria’ s renewable energy systems offers a promising pathway toward achieving national energy security, sustainability, and socioeconomic development.

Keywords

Artificial IntelligenceRenewable EnergyNigeriaEnergy GenerationEnergy

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