A New Method of Extracting RGB V-I Trajectory for Non-intrusive Load Monitoring. Issue 1 (1st July 2022)

• Record Type: Journal Article
• Authors: Liu, Rui; Sun, Mingyang; Zheng, Zhe; Cui, Wenpeng; Gong, Xiangfeng; Yang, Bokuan; Qi, Bing
• Is Part Of: Journal of physics. Volume 2303:Issue 1(2022)
• Publication Date: 2022-07-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2303/1/012082
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Existing methods of extracting V-I trajectory in RGB space used as load signatures for non-intrusive load monitoring (NILM) are complex and not suitable for edge computed devices. To solve this problem, we propose a new method to obtain the RGB V-I trajectory. The new method is simple and efficient by using the time derivatives of instantaneous reactive current and voltage to map the grayscale V-I trajectory to RGB space. Then, a lightweight CNN model, ShuffleNetV2, is selected to carry out a transfer learning study for NILM problem. Using RGB V-I trajectory obtained by using the method in this paper as load signature achieves a 5.1% improvement in F1-score than using grayscale V-I trajectory. A V-I trajectory of larger resolution avails the improvement of transfer learning performance, and the model trained on RGB V-I trajectory seems to be less affected by input resolutions for a smaller variance in F1-score among various input resolutions.