Inter-seasons and Inter-households Domain Adaptation Based on DANNs and Pseudo Labeling for Non-Intrusive Occupancy Detection

These days the demand for non-intrusive occupancy detection using low time frequency e.g. 30 minutes household energy data from smart meters has been increased, because it is beneficial to society in many applicable areas such as removing absent delivery and controlling supply of energy automatically. There are privacy concerns for the public thus gathering the supervised occupancy labels on a large scale is infeasible. Previous signal processing studies in unsupervised domain adaptation realise unsupervised prediction, though assumption that a distribution shifts once by domains is inappropriate for non-intrusive occupancy detection because two time shifts occur naturally between seasons and between households. This paper proposes novel unsupervised domain adaptation strategy interseasons and inter-households domain adversarial neural networks (isih-DA) using domain adversarial neural networks (DANNs) and pseudo labeling, which smoothly learns distribution gaps in two dimensional domains. The isih-DA splits the problem into two sub-problems converting domain adaptation between seasons and between households into (1)domain adaptation between seasons (2)domain adaptation between households, thus divergence of each data pattern in sub-problems is decreased compared with direct domain adaptation between seasons and between households, finally this eases learning convergence in terms of classification performance for target data. We demonstrated experimental superiority over the previous studies using publicly largest ECO data set and temporal shift synthetic data we created via ECO data set. Temporal shift synthetic data corresponds to a simplified version of the situation inherent in the data that isih-DA excels at modeling.