AMBAL: Realistic load signature generation for load disaggregation performance evaluation

Abstract

Well annotated power consumption traces are a crucial prerequisite for the development and analysis of load disaggregation algorithms. Due to the high efforts required to collect such traces in the real world, their synthetic generation has emerged as a viable alternative. However, many current models for the synthetic trace generation simply combine statistical information about household occupancy with the energy consumptions of the most frequently performed user activities. While this may suffice for high-level analyses (i.e., considering groups of households or entire cities), such models do not reflect the actual diversity of consumption signatures in real data. We overcome this limitation in this paper by presenting a system design to model appliance power consumption at a user-definable accuracy. Our Automated Model Builder for Appliance Loads (AMBAL) allows to derive models from real device power consumption data collected by means of smart plugs. These models are represented by sequences of parametrized signatures; each model's complexity is kept minimized for its desired level of accuracy. We evaluate the accuracy of AMBAL's models for device traces with consumption patterns of different complexity, taken from existing appliance-level data sets. Moreover, a synthetic appliance trace generator is presented which allows to recombine appliance models in an effort to simulate user activities in homes with a definable complexity. The generated data is valuable for the development of data analysis algorithms (e.g., Non-Intrusive Load Monitoring), and we integrate it with the NILMTK framework to demonstrate that a similar disaggregation performance is achieved for actual and generated traces.