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The Machine Learning-Optimized Hybrid Inverter (MLOHI) integrates renewable energy sources, including solar and wind power, with electrical grids but poses some integration challenges. Traditional power distribution methods, such as employing Static Resource Allocation (SRA) and round-robin Scheduling (RRS), are inefficient in handling dynamic energy generation and lead to the wastage of energy and suboptimal utilization. To address these inefficiencies, a new power management methodology is proposed: the Hybrid Inverter Power Management System, wherein ML algorithms are embedded to optimize energy distribution in real-time. The MLOHI system optimizes energy flow intelligently by predicting demand and dynamically adjusting power allocation between renewable sources and the grid. Compared to conventional approaches, the MLOHI performs better by 0.30% in energy efficiency improvement, 0.25% in power loss reduction, and 0.20% in battery storage performance optimization. It develops more stability and reliability in renewable energy systems, turning them into more feasible options for modern power grids.

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Hybrid Inverter-Based Renewable Energy Optimization Using Machine Learning

  • 会议论文

  • 发表日期：

    2025年

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  • Basavanapura Muddumadappa Parashiva Murthy,
  • Sharmila Nagaraj,
  • Mallikarjunaswamy Srikantaswamy,
  • Komala Mallikarjunaiah,
  • Mahendra Hanumanapura Nanjundaswamy

摘要

The Machine Learning-Optimized Hybrid Inverter (MLOHI) integrates renewable energy sources, including solar and wind power, with electrical grids but poses some integration challenges. Traditional power distribution methods, such as employing Static Resource Allocation (SRA) and round-robin Scheduling (RRS), are inefficient in handling dynamic energy generation and lead to the wastage of energy and suboptimal utilization. To address these inefficiencies, a new power management methodology is proposed: the Hybrid Inverter Power Management System, wherein ML algorithms are embedded to optimize energy distribution in real-time. The MLOHI system optimizes energy flow intelligently by predicting demand and dynamically adjusting power allocation between renewable sources and the grid. Compared to conventional approaches, the MLOHI performs better by 0.30% in energy efficiency improvement, 0.25% in power loss reduction, and 0.20% in battery storage performance optimization. It develops more stability and reliability in renewable energy systems, turning them into more feasible options for modern power grids.