  Energy disaggregation estimates appliance-by-appliance electricity
consumption from a single meter that measures the whole home's electricity
demand. Compared with intrusive load monitoring, NILM (Non-intrusive load
monitoring) is low cost, easy to deploy, and flexible. In this paper, we
propose a new method, coined IMG-NILM, that utilises convolutional neural
networks (CNN) to disaggregate electricity data represented as images. Instead
of the traditional approach of dealing with electricity data as time series,
IMG-NILM transforms time series into heatmaps with higher electricity readings
portrayed as 'hotter' colours. The image representation is then used in CNN to
detect the signature of an appliance from aggregated data. IMG-NILM is robust
and flexible with consistent performance on various types of appliances;
including single and multiple states. It attains a test accuracy of up to 93%
on the UK-Dale dataset within a single house, where a substantial number of
appliances are present. In more challenging settings where electricity data is
collected from different houses, IMG-NILM attains also a very good average
accuracy of 85%.

Machine Learning;Signal Processing