Anomaly Detection in Buildings

Project information

This project explores anomaly detection in building data using machine learning. It delves into various techniques, both unsupervised (without pre-labeled data) and supervised (with labeled data). The project utilizes open-source building time series data and demonstrates anomaly detection methods like Isolation Forest and K Nearest Neighbors. It then compares unsupervised and supervised approaches, highlighting the benefits of each.

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Created on

February 21, 2024

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