IoT Energy Meter with Current, Voltage, and Cost Monitoring System

This guide will help you create an IoT-based energy meter system using Raspberry Pi. The system will monitor electrical parameters such as current, voltage, and energy cost, sending real-time data to the cloud for monitoring and analysis.

Components Required

• Raspberry Pi (Model 3 or later)
• Current sensor (e.g., ACS712)
• Voltage sensor (e.g., ZMPT101B)
• Energy meter IC or module (e.g., PZEM-004T)
• Connecting wires and breadboard
• Power supply (for Raspberry Pi)
• Cloud service account (e.g., ThingSpeak, Blynk, or AWS IoT)
• Optional: LCD or LED display (for local feedback)

Setting Up the Hardware

Follow these steps to set up the hardware:

1. Connect the Current Sensor

   Wire the current sensor to the Raspberry Pi using GPIO pins. This sensor will measure the current flowing through the electrical circuit.

2. Connect the Voltage Sensor

   Wire the voltage sensor to the Raspberry Pi. This sensor will measure the voltage across the electrical circuit.

3. Integrate the Energy Meter Module

   Connect the energy meter module to the Raspberry Pi to measure energy consumption. This module will provide readings for current, voltage, and power.

4. Power the System

   Ensure the Raspberry Pi and sensors are properly powered using the appropriate power supplies.

Programming the Raspberry Pi

Write the code to read data from the current, voltage, and energy sensors and calculate the energy cost. Below is a basic outline of the code using Python:

                import RPi.GPIO as GPIO
                import time
                import requests

                # Configuration
                CURRENT_SENSOR_PIN = 17
                VOLTAGE_SENSOR_PIN = 27
                ENERGY_METER_ADDRESS = 0x48  # Example address, adjust as necessary
                CLOUD_URL = "https://api.thingspeak.com/update?api_key=YOUR_API_KEY"
                COST_PER_KWH = 0.12  # Example cost per kWh in dollars

                # Setup
                GPIO.setmode(GPIO.BCM)
                GPIO.setup(CURRENT_SENSOR_PIN, GPIO.IN)
                GPIO.setup(VOLTAGE_SENSOR_PIN, GPIO.IN)

                def read_sensor_data():
                    # Placeholder function for reading from the energy meter
                    current = GPIO.input(CURRENT_SENSOR_PIN)  # Replace with actual reading code
                    voltage = GPIO.input(VOLTAGE_SENSOR_PIN)  # Replace with actual reading code
                    power = current * voltage  # Example calculation, adjust as needed
                    return current, voltage, power

                def calculate_cost(power, duration_hours):
                    return (power / 1000) * COST_PER_KWH * duration_hours

                def send_data(current, voltage, power, cost):
                    payload = {
                        "field1": current,
                        "field2": voltage,
                        "field3": power,
                        "field4": cost
                    }
                    response = requests.get(CLOUD_URL, params=payload)
                    print(f"Data sent to cloud: {response.status_code}")

                try:
                    while True:
                        current, voltage, power = read_sensor_data()
                        cost = calculate_cost(power, 1)  # Example: calculate cost for 1 hour
                        send_data(current, voltage, power, cost)
                        time.sleep(60)  # Send data every minute

                except KeyboardInterrupt:
                    GPIO.cleanup()
            

This code reads data from the current, voltage, and energy sensors, calculates the energy cost, and sends the data to the cloud service at regular intervals. Adjust the sensor reading and cost calculation as needed for your specific sensors and pricing.

Setting Up Cloud Integration

To visualize and analyze the energy data, integrate the system with a cloud service. Configure your cloud service to receive data from the Raspberry Pi and set up dashboards or alerts based on the received data for current, voltage, power, and cost.

Testing and Calibration

Test the system by comparing sensor readings with known values and verifying that the data is correctly sent to the cloud. Adjust sensor calibration and code parameters as needed for accurate measurements and cost calculations.