Path Signatures for Non-Intrusive Load Monitoring

Non-intrusive load monitoring (NILM) is the analysis of electricity loads by means of a single supply wire, so avoiding separate monitors on individual appliances. Some approaches to NILM use the V-I trajectory for feature generation but they apply ad-hoc rules to generate the feature vector. This paper demonstrates a systematic method of feature generation called the path signature which has recently been applied in machine learning, often with notable success. We show how the path signature generates features from the V-I trajectory to give a test set accuracy of 98.81% on the COOLL dataset. We conclude that the path signature is easier to use and generalize than ad-hoc features, and it can be applied to many other applications which use multivariate sequential data.