MohiniPriya/Anomaly-Detection-Using-UnSupervised-Machine-Learning-Algorithms-in-HVAC-System

Applied unsupervised machine learning algorithms (K-Means Clustering and Isolation Forest) on time series data collected from an Air Handling Unit of a building to detect anomalous behavior of the system. Applied exploratory data analysis using Python to identify non-optimal working conditions of the AHU. Designed an automated anomaly detection system and a corrective strategy to control the AHU effectively.

Jupyter Notebook