PALDi: Online Load Disaggregation via Particle Filtering

• Published in: IEEE transactions on instrumentation and measurement 2015-02, Vol.64 (2), p.467-477
• Main Authors: Egarter, Dominik, Bhuvana, Venkata Pathuri, Elmenreich, Wilfried
• Format: Article
• Language: English
• Subjects: Appliances; Approximation methods; Computational modeling; Energy management; Factorial hidden Markov model (FHMM); Filtering; Filtration; hidden Markov model (HMM); Hidden Markov models; Home appliances; Households; Instrumentation; load disaggregation; Load modeling; Markov analysis; Mathematical models; Measurement; Measuring instruments; Meters; non-intrusive load monitoring (NILM); particle filter (PF); Power demand; state estimation
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2014.2344373

Smart metering and fine-grained energy data are one of the major enablers for future smart grid and improved energy efficiency in smart homes. Using the information provided by smart meter power draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter. In this paper, we present an NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for nonlinear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. ON/OFF appliances, multistate appliances, or combinations of them are modeled by hidden Markov models, and their combinations result in a factorial hidden Markov model modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.