Predictive Modelling for Smart Home Energy Optimization using Appliance-Level Data

With the rise of smart home technologies, optimizing energy consumption is crucial for reducing costs and environmental impact. This research establishes a predictive modeling approach that examines appliance- level energy data to predict usage patterns and to detect inefficiencies. Employing machine learning, it makes smart scheduling suggestions, taking voltage variations and bandwidth information into account for increased precision. An interactive dashboard will provide actionable insights, assisting homeowners with energy optimization methods, thus reducing electricity bills and minimizing carbon signatures. This research also investigates combining IoT with renewable energy for smart home energy management.