Abstract-With recent developments in the infrastructure of smart meters and smart grid, more electric power data is available and allows real-time easy data access. Modeling individual home appliance loads is important for tasks such as non-intrusive load disaggregation, load forecasting, and demand response support. Previous methods usually require sub-metering individual appliances in a home separately to determine the appliance models, which may not be practical, since we may only be able to observe aggregated real power signals for the entire-home through smart meters deployed in the field. In this paper, we propose a model, named Explicit-Duration Hidden Markov Model with differential observations (EDHMM-diff), for detecting and estimating individual home appliance loads from aggregated power signals collected by ordinary smart meters. Experiments on synthetic data and real data demonstrate that the EDHMM-diff model and the specialized forward-backward algorithm can effectively model major home appliance loads. Index Terms-Disaggregation, explicit duration hidden Markov model (HMM), forward-backward, load modeling.
Guo Z et al. REF presented a model, named Explicit-Duration Hidden Markov Model with differential observations (EDHMM-diff) to detect and estimate the status of individual household appliances through the signal of the total monitoring load, and solve the overlapping phenomenon of the active signals, which are between the target electric appliance and other electrical equipment.
Home Appliance Load Modeling From Aggregated Smart Meter Data
IEEE Transactions on Power Systems
IEEE Transactions on Power Systems
Engineering