Home Activity Recognition Using Aggregated Electricity Consumption Data

In this paper, we propose a low-cost, non-invasive home activity recognition method using low-resolution power consumption data. Notably, we tackle the following two challenges. Firstly, we use only the time series of power consumption data aggregated per house and measured every few tens of seconds, which is usually used for demand monitoring by smart meters. We design a set of activities that can be recognized by such low-resolution data, and find out an appropriate feature set to train and test balanced random forest classifiers. Secondly, we consider the divergence of activity patterns seen in different households. Since supervised learning dedicated to each household is not a realistic solution, we arrange different classifiers trained by different household data in supervised learning, and present a method to automatically choose the best-fit classifier for the household of interest in the online phase. The experiment was conducted to collect aggregated power consumption data from eight real homes for 191 days. The result of activity recognition using the dataset shows that the proposed method achieved 70% recognition accuracy in identifying activities like cooking and sleeping, which is significant for non-invasive remote monitoring.