IoT based Optimal Energy Management System with Electricity Selling for Residential Applications

The Integration of smart technology and predictive algorithms enables real-time monitoring and adjustment, optimizing the utilization of renewable energy sources like solar power. These systems factor in weather forecasts to anticipate variations in solar generation, enhancing the efficiency of energy distribution within the household. Additionally, they facilitate remote monitoring and control, empowering users to track energy flow and make informed decisions to align with their economic objectives. Moreover, these advancements foster a more resilient energy infrastructure, reducing dependence on centralized power grids and mitigating the impact of grid failures or fluctuations. Furthermore, the evolving landscape of energy storage technologies complements these systems by using IoT, allowing for greater flexibility in managing energy surpluses and deficits. Efficient battery storage solutions facilitate the optimization of energy usage, further enhancing the economic benefits and reliability of residential PV systems. The system utilizes a machine learning-based predictive algorithm to forecast residential energy consumption patterns, optimizing electricity management by dynamically usage selling surplus energy back to the grid, ensuring efficient and cost-effective power utilization. The scalability of these solutions renders them adaptable to varying household needs and preferences, ensuring inclusivity across different demographics and geographic locations. This democratization of energy transactions contributes to a more sustainable and equitable energy landscape. Overall, the convergence of these technologies marks a significant step towards a resilient, decentralized, and environmentally friendly energy future.