Project Overview:

The Smart Energy Meter with Load Forecasting is an IoT + AI/ML-based solution that enables real-time monitoring of energy consumption and predicts future load using machine learning. The system is designed to help households, industries, and utility providers track electricity usage, identify peak hours, and optimize energy consumption by forecasting future loads.

This smart meter system not only tracks real-time energy usage but also uses historical data and time-series analysis to predict future demand, making it an ideal fit for smart grids and sustainable energy management.

Technologies Used:

Hardware:

• Microcontroller: NodeMCU (ESP8266) or Raspberry Pi

• Energy Sensor: INA219 / ACS712 current sensor (to measure voltage & current)

• Wi-Fi Module: Built-in with ESP8266 for cloud connectivity

• LCD / OLED Display (optional for local display)

Frontend:

• HTML, CSS, Bootstrap

• JavaScript (Chart.js for real-time graphs)

Backend:

• Option 1: PHP + MySQL

• Option 2: Node.js + MongoDB

• Option 3: Java Spring Boot + PostgreSQL

AI/ML:

• Python (pandas, scikit-learn, statsmodels, or TensorFlow)

• ML Algorithms:

  • Time-series forecasting (ARIMA, LSTM)

  • Regression models (Linear, Random Forest) for load prediction

System Architecture:

1. The current and voltage sensors measure energy consumption in real time.

2. The microcontroller (ESP8266 or Pi) sends this data to the backend via Wi-Fi.

3. Backend server stores the energy data into a database.

4. An ML model analyzes usage patterns and forecasts future consumption (hourly/daily).

5. The web dashboard displays:

   • Real-time power usage

   • Consumption trends

   • Predicted future load

6. Users receive alerts when usage exceeds a defined threshold.