Smart Energy Monitoring System

Smart Energy Monitoring System Using Arduino Nano 33 IoT and Raspberry Pi 4

Introduction

In industrial environments, monitoring energy consumption is critical to ensure efficiency, reduce downtime, and identify issues such as equipment failures. This project, presents a Smart Energy Monitoring System that uses an Arduino Nano 33 IoT and Raspberry Pi 4 to monitor energy consumption in real-time.

This system measures current flow through machines, detects anomalies (e.g., if current flow stops), and sends notifications, making it an ideal solution for industry-level energy monitoring.

Project Overview

Objectives

• Monitor energy usage in machines.
• Detect and alert when current flow stops.
• Provide real-time data for analysis and optimization.

Hardware Components

• Arduino Nano 33 IoT: Captures sensor data and communicates with the Raspberry Pi.
• SCT013 Current Sensor: Measures current non-invasively.
• Raspberry Pi 4: Processes the data and sends alerts.

Software and Tools

• Arduino IDE: For programming the Arduino Nano.
• Python: Runs the data processing code on Raspberry Pi.

System Architecture

The system follows these steps:

• The SCT013 sensor measures the current flowing through a machine.
• The Arduino Nano 33 IoT reads this data and publishes it to the Raspberry Pi using MQTT.
• The Raspberry Pi processes the incoming data and sends notifications if an anomaly is detected, such as current flow stopping.

Here’s a high-level flow diagram of the system:(SCT013 → Arduino → WiFi → Raspberry Pi → Notification)

Hardware Setup

• Connect the SCT013 current sensor to the Arduino Nano 33 IoT using the analog input pin.
• Power the Arduino Nano via USB or a Lithium ion cell power source.
• Set up the Raspberry Pi 4 with Wi-Fi and install necessary libraries.
• Ensure a stable network connection between the devices.

Code Implementation

Arduino Nano Code

The Arduino Nano reads the current data from the SCT013 sensor and publishes it via WiFi.

Raspberry Pi Code

The Raspberry Pi listens for current data and processes it to detect anomalies, it uses Python code.

Challenges and Solutions

• Sensor Calibration: Fine-tuning the SCT013 sensor required several tests.
• Network Stability: Setting up a reliable WiFi connection took some troubleshooting.
• Data Processing: Managing real-time data efficiently on the Raspberry Pi involved optimizing Python code.

Results and Demonstration

The system successfully:

• Monitors energy consumption in real-time.
• Alerts users when current flow stops.
• Provides accurate and actionable data.

Future Improvements

• Adding fault tolerance if WiFi connection breaks shift to the Bluetooth.
• Implementing predictive maintenance using machine learning.
• Scaling the system to monitor multiple machines simultaneously.

Conclusion

This Smart Energy Monitoring System provides a robust solution for industrial energy monitoring, enhancing efficiency and preventing potential failures. With its modular design, this project can be extended to meet various industrial requirements.