[1D4-GS-10] AI application: Behavioral model

[1D4-GS-10-02] Modelling of occupant attributes and behavior with data measured by HEMS

〇Masataka Yuasa¹, Hideaki Uchida¹, Yohei Yamaguchi¹, Yoshiyuki Shimoda¹ (1. Osaka University)

Keywords:HEMS data, Gaussian Mixture Model, Hidden Markov Model, occupant behavior

Recently, HEMS (Home Energy Management System), manage and optimize the energy consumption of a house, has been spreading toward the realization of a decarbonized society. Although the detailed electricity consumption data measured by HEMS is useful from an energy management perspective, its utilization methods are not yet fully established. Therefore, this study aims to clarify the characteristics of residents' lifestyles by extracting states of rooms and home appliances using GMM (Gaussian Mixture Model) and identifying state transitions with HMM (Hidden Markov Model) based on HEMS data. In addition, the accuracy of this method will be evaluated through surveys conducted with the residents. As a result, it was possible to extract the usage status of each room and appliance, living patterns such as sleeping and going out with relatively high accuracy from the HEMS data alone.