TY - BOOK
T1 - IMG-NILM: A Deep learning NILM approach using energy heatmaps ...
AU - Edmonds, Jonah
AU - Abdallah, Zahraa S.
PY - 2022/12/12
Y1 - 2022/12/12
N2 - 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 ... : 10 pages, under review ...
AB - 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 ... : 10 pages, under review ...
UR - https://dx.doi.org/10.48550/arxiv.2207.05463
U2 - 10.48550/arxiv.2207.05463
DO - 10.48550/arxiv.2207.05463
M3 - Commissioned report
BT - IMG-NILM: A Deep learning NILM approach using energy heatmaps ...
PB - The 38th ACM/SIGAPP Symposium On Applied Computing
ER -
