A Semi-supervised and Online Learning Approach for Non-Intrusive Load Monitoring

Abstract

Non-Intrusive Load Monitoring (NILM) approaches aim at identifying the consumption of a single appliance from the total load provided by smart meters. Several research works based on Hidden Markov Models (HMM) were developed for NILM where training is performed offline. However, these approaches suffer from different issues: First, they fail to generalize to unseen appliances with different configurations or brands than the ones used for training. Second, obtaining data about all active states of each appliance requires long time, which is impractical for residents. Third, offline training requires storage of huge amount of data, yielding to share resident consumption data with external servers and causing privacy issues. Therefore, in this paper, a new approach is proposed in order to tackle these issues. This approach is based on the use of a HMM conditioned on discriminant contextual features (e.g., time of usage, duration of usage). The conditional HMM (CHMM) is trained online using data related to a single appliance consumption extracted from aggregated load in order to adapt its parameters to the appliance specificity’s (e.g., brand, configuration, etc.). Experiments are performed using real data from publicly available data sets and comparative evaluation are performed on a publicly available NILM framework.