IMG-NILM: A Deep learning NILM approach using energy heatmaps
This addresses the problem of non-intrusive load monitoring for energy management, offering a flexible approach with competitive performance, though it appears incremental as it adapts existing CNN techniques to a new data representation.
The paper tackled energy disaggregation by proposing IMG-NILM, a method that transforms electricity time series data into heatmaps and uses convolutional neural networks to detect appliance signatures, achieving up to 93% accuracy on a single-house dataset and 85% average accuracy across different houses.
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%.