He, N.; Liu, D.; Zhang, Z.; Lin, Z.; Zhao, T.; Xu, Y. Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management. Preprints2024, 2024040356. https://doi.org/10.20944/preprints202404.0356.v1
APA Style
He, N., Liu, D., Zhang, Z., Lin, Z., Zhao, T., & Xu, Y. (2024). Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management. Preprints. https://doi.org/10.20944/preprints202404.0356.v1
Chicago/Turabian Style
He, N., Tiesong Zhao and Yiwen Xu. 2024 "Learning-Based Non-Intrusive Electric Load Monitoring for Smart Energy Management" Preprints. https://doi.org/10.20944/preprints202404.0356.v1
Abstract
The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.
Keywords
Smart City; Smart Electric Energy Management; Electric Load Monitoring; Load Recognition Algorithm; Computer Vision
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
