AI-Powered Optimization of Nano-Grid Energy Control using Deep Q-Learning and Real-Time Feedback Integration

Nowadays, people need more sustainable and smart ways to use energy which makes it necessary for nano-grid systems to use advanced control strategies. Methods such as rule-based logic are not agile and often fail to react well as the load and generation conditions change. To solve these limitations, this study presents a Deep Q-Learning (DQL) based system for energy control in nano-grids which uses live feedback. It uses a Markov Decision Process (MDP) to describe the energy dispatch challenge with states that represent the major climate, system control and time information. Actions are deciding the most intelligent schedule for energy, charging/discharging batteries efficiently and sorting loads by priority. Sensors provide data to the DQL model through efficient MQTT communication and the model rewards good behavior when the house uses less grid electricity, relies less on batteries and improves battery life. Study outcomes show that DQL performs well on various performance indicators. Especially, it reaches accuracy of 96.18%, a F1-score of 94.92% and an AUC of 97.63%, higher than SARSA, A3C, DDPG and rule-based algorithms. The model being proposed is 94.73% energy-efficient, makes the grid less important by using 53% less and continues to ensure the good health of the batteries. As per this paper, the DQL model can be used effectively for intelligent applications in nano-grids since it is functional in real time and supports scaling. More work can be done to add federated learning and edge-AI deployment to the architecture to increase both security and protection of privacy in decentralized energy systems.