https://ojs.acad-pub.com/index.php/ICSE <p><em>Intelligent Control and System Engineering&nbsp;</em>(ICSE) is an international, peer-reviewed open access journal. It publishes various article types including Original Research Articles, Reviews, Editorials, and Perspectives. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full account of the research must be provided so that the results can be reproduced.</p> <p>&nbsp;</p> <p>Topics covered in <em>Intelligent Control and System Engineering </em>include (not limited to):</p> <ul> <li class="show">Artificial intelligence</li> <li class="show">Electrical engineering</li> <li class="show">Computer science and engineering</li> <li class="show">Electronics</li> <li class="show">Software engineering</li> <li class="show">Control engineering</li> <li class="show">Communication engineering</li> <li class="show">Optical engineering</li> <li class="show">Neural network</li> <li class="show">Machine learning</li> <li class="show">Evolutionary learning</li> <li class="show">Genetic Algorithm</li> <li class="show">Information engineering methods and practice</li> </ul> Academic Publishing Pte. Ltd. en-US Intelligent Control and System Engineering https://ojs.acad-pub.com/index.php/ICSE/article/view/427 <p>Energy detection is an important part of intelligent power consumption, and its key technology is non-intrusive load monitoring (NILM). In this study, an end-to-end model is proposed to realize the NILM of commercial power data using the autoencoder-based transformer method. Firstly, we measured the operating power of different electrical appliances across different modes and combined the operating modes of electrical appliances. Considering the relatively large number of industrial electrical appliances, to ensure accuracy, we used Autoencoder to recode and reduce the dimension of the combined coding. Secondly, the transformer model was used to train the translation of the total power consumption information sequence and the state sequence of electrical appliances. Through our model, the electrical signals to be decomposed can be translated into different electrical state codes so as to realize load energy decomposition. Finally, when our model was applied to the gas station field data, the accuracy was as high as 90.17%.</p> Ce Li Fanglin Guo Rong Yang Hua Wang Bo Yao Copyright (c) 2024 Ce Li, Fanglin Guo, Rong Yang, Hua Wang, Bo Yang https://creativecommons.org/licenses/by/4.0 2024-02-28 2024-02-28 2 1 427 427