Smart Home Power Monitoring System Based on ESP32 and Machine Learning: From Real-Time Monitoring to Intelligent Prediction

The smart home power monitoring system introduced in this article is based on the ESP32 microcontroller. It integrates real-time power monitoring (voltage, current, power), remote device control, and safety alert functions, and realizes electricity bill prediction through machine learning, providing users with a comprehensive energy management solution. The project was developed by ABISHEK-A-AI, with source code hosted on GitHub, and released on June 16, 2026.

With the popularization of smart homes and rising energy costs, home electricity management has become an important issue. Traditional power monitoring is limited to simple meter readings, lacking fine-grained analysis and intelligent management methods. This project combines IoT and AI to transform passive electricity usage records into active intelligent management, enabling timely detection of abnormal electricity usage and prediction of electricity bills, helping users make informed electricity decisions.

Core Hardware: ESP32 Microcontroller

Reasons for choosing ESP32: Dual-core architecture can handle data collection and network communication simultaneously; built-in Wi-Fi simplifies design; rich interfaces support sensor and relay expansion; low power consumption is suitable for long-term home monitoring.

Sensors and Measurement Modules

Professional power sensors based on Hall effect or resistance voltage division principles are used to convert high voltage/current into low-level signals that can be processed by the microcontroller.

Real-Time Power Monitoring

Collects voltage (fluctuation, over/under voltage), current (load, overload), power (active power, energy consumption), and power factor data with second/millisecond precision. After ADC collection and calibration, the data is uploaded to the cloud or local gateway via MQTT/HTTP.

Remote Device Control

Connected to ESP32 GPIO via relays, it supports remote switching, scheduled tasks, and scene linkage, enabling two-way control.

Safety Alert Mechanism

Includes overload protection, leakage detection, temperature monitoring, and abnormal electricity usage pattern recognition. Alerts are sent to users via SMS/push notification/email.

Data Collection and Feature Engineering

Extracts time features (hour/week/holiday), electricity usage patterns (peak/off-peak), environmental factors (temperature/humidity), and device features (power curve/duration).

Prediction Models and Algorithms

Uses linear regression (baseline), decision trees/random forests (non-linear interactions), time series models (ARIMA/Prophet), and neural networks (complex patterns). Training is done on the cloud or edge devices.

Application Value

Helps users with budget planning, peak-valley optimization (time-of-use electricity pricing), anomaly detection, and personalized energy-saving suggestions.

Hardware Installation

Qualified electricians are required to install sensors (safe isolation of high voltage). The ESP32 should be placed in a dry and ventilated area, and relays should match the device power.

Software Configuration

The ESP32 connects to the home Wi-Fi. Sensor parameters, alert thresholds, and cloud connection information are configured via APP/Web.

Typical Scenarios

Ordinary households (overall monitoring + alerts), rental properties (landlord remote monitoring), elderly care (abnormal electricity usage habit recognition), smart home integration (energy management module).

Measurement Accuracy and Calibration

Multi-point calibration (fitting curve/lookup table), temperature compensation, periodic self-check (comparison with standard meters).

Network Stability

Disconnection reconnection mechanism, local caching (stores data when offline), offline mode (core functions like overload protection still work).

Data Privacy and Security

Transmission/storage encryption, strict user authentication and permissions, support for pure local deployment.

Future Directions

Edge AI (model deployment to ESP32), energy trading (connection to virtual power plants), device identification (power fingerprint), carbon footprint calculation (emission reduction suggestions).

Summary

This project is a typical application of IoT + AI, covering full-stack technologies including hardware selection, embedded development, cloud analysis, and machine learning. It provides a reference for developers and represents an intelligent, safe, and energy-saving lifestyle for users. It is expected to become a household standard in the future, contributing to carbon neutrality.