Anomaly Detection in Kitchen Appliance Usage using Machine Learning Approach

Anomaly detection in domestic energy consumption is crucial for improving energy efficiency, identifying faulty appliances, and identifying unusual patterns of consumption. In this paper, the Isolation Forest (IF) algorithm is employed to detect anomalies in domestic appliance power consumption. The data consists of high-resolution (1Hz) domestic appliance power measurements with three chosen features: DISHWASH, HEATHOME, and AIRCOND, corresponding to dishwasher consumption, home heating, and air conditioning, respectively. These features were selected because they are major contributors to overall energy usage and have the ability to point towards unusual trends. Preprocessing involved normalizing the selected features and using Isolation Forest for anomaly detection. IF separates anomalies by recursive partitioning, effectively separating outliers from regular data. KDE analysis identified that all three features have a bimodal distribution, which indicates different consumption patterns. Pair plot and 3D visualization outcomes verify that IF accurately detected two separate outliers. Further, the histogram of IF anomaly scores verify these results, wherein a 5% contamination level is utilized to distinguish normal and anomalous points. The findings show that unsupervised machine learning models such as Isolation Forest are able to identify effectively anomalies in household energy consumption, providing information on potential energy losses and system errors. The study aims to enhance smart energy monitoring systems through predictive maintenance and energy optimization applications.