A Naive Bayes Classifier Based Energy Scheduling for Home Energy Management System (HEMS)

With the increase of domestic energy consumption, an efficient energy management scheme for smart home is needed. Just as solar power can be used as a supplement to grid power in order to use smart appliances in case of grid unavailability or blackout. In this research, a load and power management system based on load priority and instant grid energy price has been proposed using Fuzzy logic algorithms. The home energy management systems (HEMS) modeled using Naive Bayes classifier shows that the loads can be shifted on or off based on grid availability, solar availability, pricing demand, urgency and loads can be changed their power source based on Interrupted, Non-interrupted, and Fixed appliances. This sort of technologies would be needed in Bangladesh whereas cost adopts grid energy price variation over time and demand. Simulation results reveal that the proposed protocol saves 47.1% ~ 48.47% of monthly electricity cost compare to the traditional scheme.