Demonstrates improved energy efficiency and bill forecasting in smart homes, suggesting better resource management.
The Smart Home Energy Monitoring and Bill Forecasting System is an IoT-based intelligent energy management solution designed to monitor household power consumption in real time and predict future electricity bills using Machine Learning techniques. The system integrates IoT simulation, Fire base cloud database, web technologies, mobile application development (MAD), R programming, and machine learning algorithms to create a complete smart energy ecosystem. The IoT module continuously simulates voltage, current, power, and energy consumption data using Python. The generated sensor data is transmitted to Firebase Real time Database for cloud storage and live synchronization. Machine learning models are integrated to analyze energy usage patterns and forecast future electricity bills based on consumption history. A web dashboard and mobile application provide users with live monitoring, energy analytics, alerts, and forecast reports. The project also demonstrates practical implementation of real-time cloud communication, smart energy analytics, predictive systems, and scalable web integration. The proposed system helps users reduce unnecessary energy usage, identify abnormal power spikes, and improve energy efficiency in smart homes.
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Babu et al. (2026) studied this question.
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