Anomaly Detection in Energy Consumption

This project focuses on identifying abnormal patterns in energy consumption data using advanced machine learning techniques. Detecting anomalies in energy usage helps in identifying issues like equipment malfunctions, inefficiencies, or unauthorized usage.

Features

• Uses advanced machine learning models like Isolation Forest, Autoencoders, and Local Outlier Factor (LOF).
• Real-time detection of anomalies in energy consumption data.
• Provides insights into potential equipment faults or unusual energy usage patterns.
• Scalable backend for integrating with IoT devices and energy monitoring systems.

Installation

To set up the anomaly detection system on your local machine, follow these steps:

1. Clone the Repository

   git clone https://github.com/username/energy-anomaly-detection.git
   cd energy-anomaly-detection

2. Set Up Virtual Environment (Optional but Recommended)

   python3 -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate

3. Install Required Packages

   pip install -r requirements.txt

Usage

To start the anomaly detection server, run the main script:

python detect_anomalies.py

The system will analyze energy consumption data and flag any anomalies, providing insights into potential issues.

System Demo