Smart Energy Waste Detector 💡

A comprehensive IoT and Machine Learning solution designed to monitor energy consumption and identify patterns of electricity waste in real-time.

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📂 Project Structure

Folder/File	Description
data/	Contains raw and processed energy consumption datasets.
iot/	Firmware and scripts for hardware sensors (ESP32/Arduino).
ml/	Model training scripts, notebooks, and saved .pkl/.h5 models.
detect.py	The core execution script for real-time waste detection.
requirements.txt	Python dependencies (Pandas, Scikit-learn, etc.).
LICENSE	Project licensing information.

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🚀 Overview

This project bridges the gap between hardware and software to promote energy efficiency. It uses sensors to collect power metrics, which are then processed by a Machine Learning model to distinguish between "Essential Usage" and "Energy Waste."

Key Features

• Real-time Monitoring: Tracks voltage and current via IoT nodes.
• Anomaly Detection: Detects if appliances are left on during non-operational hours.
• Automated Alerts: (Optional) System can trigger alerts or cut off power via relays.

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🛠️ Installation & Setup

1. Clone the Repo

   git clone [https://github.com/aazainjan/smart-energy-waste-detector.git](https://github.com/aazainjan/smart-energy-waste-detector.git)
   cd smart-energy-waste-detector

Install Dependencies pip install -r requirements.txt Configure Hardware Upload the code found in the /iot folder to your microcontroller. Ensure your sensors are connected to the correct GPIO pins.

Run the Detector python detect.py

📊 How it Works

Data Acquisition: The iot/ scripts read data from [e.g., ACS712 current sensors].

Preprocessing: Data is cleaned and normalized in the ml/ pipeline.

Inference: detect.py loads the trained model to analyze the live stream.

Action: If waste is detected, the system logs the event or triggers a relay.

📜 License

Distributed under the MIT License. See LICENSE for more information.

🤝 Contributing

Contributions are welcome!

Fork the Project

Create your Feature Branch (git checkout -b feature/AmazingFeature)

Commit your Changes (git commit -m 'Add some AmazingFeature')

Push to the Branch (git push origin feature/AmazingFeature)

Open a Pull Request

Authored by aazainjan