| Title |
Discovering Anomalous Power Usage Patterns in Rental Housing Through Small-Scale Data |
| DOI |
https://doi.org/10.5659/JAIK.2024.40.3.81 |
| Keywords |
Public Rental Housing Management; Energy Consumption Analysis; Occupancy Analysis; Anomaly Detection Housing Type |
| Abstract |
Employing machine learning, this study detected occupancy anomalies in Seoul's public rental housing by analyzing energy usage data
spanning from 2016 to 2021. Through the examination of electricity consumption patterns, the model successfully pinpointed instances of underreported or illegal occupancy, identifying approximately 8% of households as anomalies. This approach highlights the promise of
data-driven methodologies in public housing management, promoting adherence to regulations and equitable resource allocation. Visualization of results using GIS further facilitates their practical utilization by housing authorities. |