Date of Publication :5th February 2025

Abstract: Residential energy management is a critical area of research aimed at promoting economical and sustainable energy use. Traditional methods often underutilize energy storage systems (ESS) and struggle to keep up with the dynamic nature of household energy consumption. This study addresses these challenges by proposing a novel solution that integrates reinforcement learning (RL) techniques with Internet of Things (IoT) technology to enhance the efficiency of residential ESS. IoT facilitat es real-time data acquisition, while RL has shown potential in optimizing complex decision-making tasks. The synergy between these technologies creates a modern energy management system tailored to the needs of households. Energy optimization can be personalized and more efficient through RL, which leverages historical data and adapts to e volving conditions. The integration of IoT enables real-time system responsiveness to fluctuations in energy demand and supply. This research demonstrates the practical application of intelligent, adaptive energy management systems in residential settings, offering valuable insights into the future of flexible energy solutions.

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