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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.

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