IoT-Based Home Power Monitoring with Smart Forecasting

With the advent of smart living, household energy efficiency is highly important for economic viability and also for sustainable ecology. The paper presents an Internet of Things (IoT) sensor-based, machine learning (ML)-algorithms-enabled real-time smart home energy management system to track, analyze, and predict home energy usage at an appliance level. The platform employs low-cost sensors such as ACS712 and DHT22 to sense real-time current, temperature, and humidity and stream it over Wi-Fi to a cloud processing platform. The platform employs a hybrid ML model comprising XGBoost and Random Forest algorithms to forecast energy consumption patterns and generate actionable insights. To reduce latency and enhance responsiveness, edge-based processing is provided through the ESP32 microcontroller, supporting on-device inference and seamless operation in periods of sporadic internet connectivity. The user interface is developed in HTML, CSS, and Python (Flask), offering an interactive web-based dashboard that displays real-time energy trends, past consumption, and customized recommendations. This affordable and elastic solution cuts off the reliance on smart devices, bringing the cutting-edge energy optimization to a wide variety of families. The use of edge computing, IoT, and ML allows the system to help build smarter and sustainable residential environments.