IoT-Driven Smart Energy Monitoring: Real-time Insights and AI-Based Unit Predictions

This paper unveils a meticulous creation process for an Internet of Things (IoT)-powered intelligent energy monitoring framework. This system adeptly gauges current, voltage, active power, power factor, energy, and unit consumption in real-time, spanning hourly, weekly, and monthly intervals. This instantaneous data is seamlessly relayed via the integrated WiFi module within the ESP32 module. Subsequently, the cloud orchestrates the dissemination of these measurements to an Android application, wherein real-time values are conveniently visualized. The Android application, far from being restricted to visualization, demonstrates an additional facet by harnessing machine learning-powered time-series forecasting models to predict future unit consumption. Remarkably, the implementation of the ARIMA, Prophet, and LSTM models for future unit prediction highlights LSTM’s superiority in accuracy over the other models.