An Extensible Approach for Non-Intrusive Load Disaggregation With Smart Meter Data

Abstract

Appliance-level load models are expected to be crucial to future smart grid applications. Unlike direct appliance monitoring approaches, it is more flexible and convenient to mine smart meter data to generate load models at device level nonintrusively and generalise to all households with smart meter ownership. This paper proposes a comprehensive and extensible framework to solve the load disaggregation problem for residential households. Our approach examines both the modelling of home appliances as hidden Markov models and the solving of non-intrusive load monitoring based on segmented integer quadratic constraint programming to disaggregate a household power profile into the appliance level. Structure of our approach to be implemented with current smart meter infrastructure is given and simulations are performed based on public datasets. All data are down-sampled to the rate that is consistent with the Australia smart meter infrastructure minimum functionality. The results demonstrate that our approach is able to work with existing smart meters to generate device level load model for other smart grid research and applications.