An Optimized Power Management System for Solar and Wind Energy Using Hybrid Inverters and Machine Learning

Traditional power management systems, such as standalone inverters and basic hybrid models, often struggle to efficiently manage the variability and unpredictability inherent in renewable energy sources. This inefficiency leads to significant energy losses and reduced reliability. Addressing this challenge, this paper presents the Optimized Hybrid Inverter-based Power Management System (O-HIPMS), a novel solution leveraging machine learning to dynamically adapt to changing energy conditions. O-HIPMS significantly enhances energy management by employing predictive algorithms that optimize power conversion, distribution, and storage. Comparative analysis reveals that O-HIPMS achieves a remarkable improvement in energy conversion efficiency and system resilience, demonstrating a 0.30% increase in energy yield and a 0.25% enhancement in load management efficiency compared to conventional methods. This substantial enhancement in performance not only underscores the limitations of existing power management solutions but also establishes O-HIPMS as a superior alternative, setting a new standard for the integration of renewable energy into the power grid.