Automated classification of appliances using elliptical fourier descriptors

Non-intrusive load monitoring methods aim to disaggregate the total power consumption of a household into individual appliances by analysing changes in the voltage and current measured at the grid connection point of the household. The goal is to identify the active appliances, based on their unique fingerprint. This information can be communicated to the electricity provider and the end-user enabling the potential of smart grids. An informative characteristic to attain the appliance classification is the voltage-current trajectory. In this paper, this trajectory is represented as a binary image from which the contours are extracted. From these contours, the elliptic Fourier descriptors are calculated and used as input for several classification algorithms outputting the appliance name. Benchmarking this method on the PLAID dataset shows that the descriptors can yield a prediction accuracy up to 79%, comparable to the state-of-the-art, based on only a very compact representation (12 numbers).