Work

AI-Driven Optimization for Off-Grid Renewable Energy Systems: A Hybrid Solar-Wind-Battery Approach

Year: 2025

Type: article

Abstract: Off-grid renewable energy systems are essential for providing sustainable electricity in remote and underserved areas. However, their reliability and efficiency are often hindered by the intermittent nature of renewable resources. This research presents an AI-driven optimization framework employing machine learning (ML) algorithms to enhance the performance of hybrid solar-wind-battery systems. By integrating historical meteorological data, load profiles, and component degradation patterns, a neural-network-based model was developed to forecast energy generation and consumption. A genetic algorithm was then applied to optimize energy dispatch and storage. The results demonstrate up to 15% improvement in energy utilization efficiency and a 20% reduction in battery cycling losses. The proposed system provides a scalable, intelligent control mechanism suitable for real-world deployment in rural electrification projects.

Source: Iconic Research and Engineering Journals

DOI: 10.64388/irev9i5-1710989

Author: Ibekwe Arinze Ignatius

Institution: Nnamdi Azikiwe University

Language: English

Topic: Hybrid Renewable Energy Systems

Subfield: Energy Engineering and Power Technology

Field: Energy

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: bronze

OpenAlex ID: W4416866335