⚡ VOLTSENSE-AI Smart Energy Meter Reading & Fraud Detection using AI 📌 Overview

VOLTSENSE-AI is an AI-powered system designed to automate electricity meter reading and detect anomalies or fraud using Machine Learning and Computer Vision.

It eliminates manual meter reading errors and provides a scalable solution for smart energy monitoring systems.

🚀 Key Features 🔍 Automatic Meter Reading using image processing 🧠 Hybrid ML Model (CNN + SVM) for accurate digit recognition 📊 Interactive Dashboard for monitoring readings and history ⚠️ Anomaly / Fraud Detection in energy consumption 🗂️ Data Logging & History Tracking 🌐 Scalable for real-world smart grid systems 🏗️ System Architecture Meter Image Input ↓ Image Preprocessing ↓ CNN Model (Feature Extraction) ↓ SVM Classifier (Digit Recognition) ↓ Prediction Output ↓ Dashboard + Database Storage 🧠 Tech Stack Programming Language: Python Machine Learning: SVM, CNN Libraries: OpenCV NumPy Scikit-learn TensorFlow / Keras Frontend / Dashboard: Streamlit Dataset: Smart Meter Image Dataset (Kaggle) 📂 Project Structure VOLTSENSE-AI/ │── app.py # Main Streamlit application
│── model/ # Trained ML models
│── dataset/ # Input dataset
│── preprocessing/ # Image preprocessing scripts
│── utils/ # Helper functions
│── requirements.txt # Dependencies
│── README.md # Project documentation
⚙️ Installation & Setup 1️⃣ Clone the Repository git clone https://github.com/MohmedZeibreal/VOLTSENSE-AI.git cd VOLTSENSE-AI 2️⃣ Install Dependencies pip install -r requirements.txt 3️⃣ Run the Application streamlit run app.py 📊 How It Works Upload a meter image System preprocesses the image CNN extracts features from digits SVM predicts the meter reading Results are displayed on the dashboard Data is stored for analysis and fraud detection 📸 Demo

(Add screenshots here — very important for GitHub visibility)

📈 Use Cases ⚡ Smart Electricity Billing Systems 🏢 Energy Monitoring in Industries 🏠 Automated Household Meter Reading 🔐 Fraud / Tampering Detection 🌍 Smart City Infrastructure 🔮 Future Enhancements 🔗 Integration with IoT-based smart meters 📱 Mobile application support ☁️ Cloud deployment (AWS / Firebase) 🤖 Real-time anomaly detection using Deep Learning 📊 Advanced analytics dashboard 🤝 Contributing

Contributions are welcome!

Fork the repository Create a new branch Make your changes Submit a Pull Request 📜 License

This project is licensed under the MIT License.

👨‍💻 Author

Mohmed Zeibreal

GitHub: https://github.com/MohmedZeibreal ⭐ Support

If you found this project useful: 👉 Give it a star ⭐ on GitHub