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@article{176496,
        author = {Abhay Rathore and Ayushi Tyagi and Anjali Dubey and Anuj Gupta},
        title = {AI-Driven Hybrid Renewable Energy Optimization for Off-Grid Communities},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {7279-7286},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176496},
        abstract = {The rising demand for sustainable and dependable energy solutions is particularly crucial for off-grid communities without access to conventional power infrastructure. Hybrid Renewable Energy Systems (HRES), which blend solar, wind, and battery storage, present an effective alternative. However, achieving optimal performance requires intelligent management. Advancements in Artificial Intelligence (AI) and Machine Learning (ML) empower data-driven strategies for real-time energy forecasting, smart resource distribution, and system optimization. By analyzing weather conditions, energy usage patterns, and battery performance data, AI-driven models enhance system efficiency, minimize energy loss, and cut operational costs. This study investigates AI’s role in optimizing HRES to ensure affordable, sustainable, and resilient power solutions for off-grid regions.},
        keywords = {Hybrid Renewable Energy, Off-Grid Power Solutions, AI Energy Optimization, Machine Learning, Energy Forecasting, Smart Grid, Sustainable Energy Management, Renewable Resource Optimization.},
        month = {April},
        }