Smart Renewable Energy Management Using Internet of Things and Reinforcement Learning
V. Dhayalan, Ramakrishnan Raman, N. Kalaivani, Ankit Shrirvastava, Ramireddy Sasidhar Reddy, Balasubramanian Meenakshi · 2024
Residential energy management is an important component of study in the effort for economical and green energy use. The majority of the time, traditional approaches create inadequate use of energy storage devices (ESS) and can't adapt to the ever-changing patterns of household energy usage. This research recognized these challenges and provided a new way to tackle them by integrating reinforcement learning (RL) techniques with IoT technology to make household ESS more efficient. The Internet of Things (IoT) allows the real-time capture of data, and RL shown promise in resolving optimization issues related to component mobility. The combination of these two elements could end up in a modern energy management system designed to meet the demands of residential environments. By employing a reinforcement learning approach that incorporates past data and adapts to new circumstances, efficient personalized energy optimization may be accomplished. By integrating IoT devices, real-time data can be collected, enabling the system to respond quickly to changes in demand and supply of energy. Key insights into the possibilities for intelligent and flexible systems for energy management in homes are provided by the study's actual demonstration of their application.