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@article{196518,
        author = {M Dhinesh and Dr C Kavitha and R Bhargavi and S Dhamu and S Dhanalakshmi},
        title = {Intelligent Load Balancing in Hybrid Energy Systems Using Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4413-4418},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196518},
        abstract = {The increasing demand for reliable and efficient energy management systems has led to the development of intelligent power control solutions. This project presents a Reinforcement Learning (RL) based real-time load balancer designed for hybrid energy systems that utilize multiple power sources such as solar energy, battery storage, and grid supply. The proposed system monitors electrical parameters including voltage and current from each power source using INA219 sensors interfaced with an Arduino microcontroller.These sensor readings are transmitted to a reinforcement learning backend where a Q-learning algorithm analyze the system state and determines the optimal power source or load control action.The reinforcement learning model continuously updates a Q table based on system conditions and rewards associated with efficient energy usage. The poitback to the microcontroller, which controls relay modules to switch between solar, battery, and grid power or to disconnect non-critical loads during high demand conditions. This approach ensures efficient utilization of renewable energy, reduces dependency on grid power, and maintains stable operation of critical loads. The system demonstrates how machine learning techniques can be integrated with embedded systems to create adaptive and intelligent energy management solutions for modern power systems.},
        keywords = {Reinforcement Learning; Q-Learning; Hybrid Energy Systems; Smart Grid; Load Balancing; INA219; Arduino; PySerial; IoT.},
        month = {April},
        }