Abstract

Author:
Onum Friday Okoh, Israel Grace

Doi: 10.26480/egnes.02.2025.111.119

This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

The integration of machine learning (ML) technologies with renewable energy systems holds immense potential for addressing energy deficits and promoting sustainable development in Nigeria’s educational sector. This paper explores the dynamic relationship between machine learning applications and the adoption of renewable energy solutions within educational institutions, particularly in underserved and rural regions. As Nigeria grapples with unreliable electricity supply and rising energy costs, educational infrastructure suffers, undermining learning outcomes and institutional efficiency. Machine learning offers innovative tools for optimizing energy consumption, predicting power demand, and enhancing the operational efficiency of solar, wind, and hybrid energy systems. When deployed in educational settings, these technologies can improve energy reliability, reduce costs, and create smart learning environments powered by sustainable energy sources. Furthermore, the intersection of ML and renewable energy promotes skill development in cutting-edge technologies, positioning students and educators as active participants in the country’s digital and green economies. This nexus also fosters environmental consciousness and supports national goals for carbon reduction. The paper highlights how the successful integration of these technologies can enhance the quality of education, stimulate local innovation, and contribute to Nigeria’s broader agenda for sustainable economic growth. The study underscores the urgent need for policy alignment, investment, and capacity-building to unlock these transformative opportunities.