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@article{204137,
        author = {Ms. Bhagyashree C. Yernale and Dr. Sampath Kumar Bodapatla},
        title = {An IoT-Enabled Smart Energy Monitoring and Fault Prediction System Using ESP32 and Machine Learning In Power Grid Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {977-982},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204137},
        abstract = {This paper presents a smart energy monitoring and prediction system designed using the ESP32 microcontroller, calibrated voltage and current sensors, cloud-based IoT communication, and machine learning techniques. The system accurately measures real-time electrical parameters such as RMS voltage, RMS current, instantaneous power, and mains status using a ZMPT101B voltage sensor and an ACS712 current sensor. Special calibration and signal-conditioning techniques are employed to ensure instant response during mains ON and OFF conditions, eliminating residual and false readings. The measured data is transmitted to a cloud MQTT broker and visualized through a real-time web-based dashboard for continuous monitoring. In addition to monitoring, the system incorporates an over-voltage fault detection mechanism using both hardware indicators and software alerts. A machine learning regression model is trained on historical energy data to predict short-term future power consumption, enabling proactive energy management and fault anticipation. The proposed system offers a low-cost, scalable, and intelligent solution suitable for smart homes, industrial energy monitoring, and predictive maintenance applications.},
        keywords = {Energy monitoring, fault detection, Internet of Things, machine learning prediction, smart energy system.},
        month = {June},
        }