Home Activity Recognition Using Low-Grain Branch Circuit Power Consumption

In recent years, HEMS (Home Energy Management System), which monitors the amount of electricity and gas used in the home and "automatically controls" home appliances, has been widespread. The government has set the goal of installing HEMS in all households by 2030 in its "Green Policy Framework," therefore, HEMS is expected to spread and be utilized more widely than ever before. 
If the activity of residents can be identified from HEMS data, it can be used for various applications such as feedback on energy consumption to residents, profiling of consumers based on their energy use trends, and short- and long-term prediction of energy demand. 
In addition to demand forecasting, it will enable low-cost applications of big data such as customer profiling and target marketing, estimation of household behavior trends, and remote healthcare, including monitoring of the elderly.
Although various activity recognition methods based on power consumption data have been studied, all previous studies assumed relatively high temporal granularity of power data.
In this study, we propose a method for estimating in-home behavior based only on the cumulative power consumption of each branch circuit every 30 minutes, which is obtained from the HEMS distribution board.
In this method, features that can be recognized at a low granularity are specially designed for each activity, and each activity is estimated using transition learning.