SMART ENERGY CONSERVATION SYSTEM USING IOT AND ML

Authors:

Susil Kumar Pradhan, Pradeesh.V, Thoufeeq.R, Mrs.C.Subalakshmi, Dr.V.Sai Shanmuga Raja,

Dr. M.Sujitha

Abstract-

Electrical energy consumption has increased significantly due to the growing use of home appliances and electrical devices in residential and small-scale environments. Traditional energy monitoring systems mainly provide cumulative billing information and do not offer real-time insights into appliance- level energy usage. This paper presents a smart energy conservation system that integrates IoT-based real-time energy monitoring, cloud storage, machine learning-based energy prediction, and anomaly detection for efficient energy management. Voltage and current sensors connected to an ESP32 microcontroller are used to monitor the energy consumption of electrical appliances in real time. The collected data is transmitted to a Firebase cloud database for storage and analysis. An LSTM-based machine learning model is used to predict future energy consumption and detect abnormal energy usage patterns by comparing predicted and measured power values. The system also includes a web-based dashboard for visualization of real-time and historical energy consumption data and anomaly alerts. The proposed modular architecture integrates sensing, edge computing, cloud storage, analytics, and visualization into a single platform. The results demonstrate that the system can effectively monitor appliance energy consumption, predict future energy usage, and detect abnormal power consumption, thereby supporting efficient energy conservation and intelligent energy management.

Keywords: Anomaly Detection, Cloud Computing, Data Analytics, Energy Conservation, Energy Prediction, ESP32, Firebase, Home Appliance Energy Monitoring, Internet of Things (IoT), LSTM, Smart Energy Monitoring