A Smart Home Electricity Meter Based on IoT with Bill Prediction Using Random Forest Algorithm - muhtaredi Muhtaredi (JAICT, 2026)
Electricity consumption in households continues to increase along with the growth of electrical appliances used in daily activities. However, most users do not have real-time information about their electricity usage, which often results in inefficient energy consumption and unexpected electricity bills. This study aims to design and implement a smart electricity metering system based on the Internet of Things (IoT) with a billing prediction feature using the Random Forest algorithm. The proposed system measures electrical parameters such as voltage, current, and power consumption using sensors connected to a microcontroller. The collected data are transmitted to an IoT platform to provide real-time monitoring of electricity usage through a mobile application. Furthermore, the Random Forest algorithm analyzes the collected data to predict future electricity bills. The results show that the developed system can monitor electricity consumption in real time and provide accurate billing predictions. The prediction model achieved a Root Mean Squared Error (RMSE) of 0.31% with an accuracy level of 99.69%. The system also provides electricity consumption information for three selected rooms in the household.