End-to-end NILM model of industrial power data based on autoencoder transformer

Ce Li; Fanglin  Guo; Rong  Yang; Hua Wang; Bo Yao

Ce Li Energy Internet Division, State Grid Gansu Electric Power Company; School of Information Science and Engineering
Fanglin Guo Energy Internet Division, State Grid Gansu Electric Power Company
Rong Yang Energy Internet Division, State Grid Gansu Electric Power Company
Hua Wang Energy Internet Division, State Grid Gansu Electric Power Company
Bo Yao Energy Internet Division, State Grid Gansu Electric Power Company

Article ID: 427

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Keywords: non-intrusive load monitoring, transformer, deep learning, smart grid

Abstract

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%.

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