Area Under Time Series Transformation for Home Appliance Classification
Leo Ogrizek, Blaž Bertalanič, Mihael Mohorčič, Carolina Fortuna · 2023
Time series classification is an important task in many fields. In intrusive and non-intrusive load monitoring (N)ILM, time series data are obtained from power measurements of electrical appliances that are not known in advance, therefore extracting the type of appliance from the data is a relevant problem in smart grids. We propose a transformation that encodes a time series into an image that can be effectively recognised by well known image classification algorithms. The transformation is based on plotting the time series into a matrix and filling the area under it, producing a more pronounced representation of its shape. We perform an extensive evaluation on 1 synthetic and 4 measured datasets. Our experiments on synthetic and mixed measured data yielded F1 scores of 99.2% and 85.9%, respectively, and outperformed the state-of-the-art on three out of four tested datasets. Additionally, we conclude that our method tends to work better on longer time series segments, as the resulting images contain more distinguishing features.