Application of attention mechanism enhanced neural network in non-invasive load monitoring of industrial power data

• Published in: Measurement and control (London) Vol. 56; no. 9-10; pp. 1780 - 1787
• Main Authors: Wei, Jun, Li, Ce, Yang, Rong, Li, Fangjun, Wang, Hua
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.11.2023; Sage Publications Ltd; SAGE Publishing
• Subjects: Decomposition; Electric power systems; Electrical loads; Electricity consumption; Monitoring; Neural networks; Power consumption; Service stations; deep learning; combined encoding; non-intrusive load monitoring; attention mechanism; Industrial electricity
• ISSN: 0020-2940; 2051-8730
• DOI: 10.1177/00202940231180617

The non-intrusive appliance load monitoring (NILM) decomposes the total power consumption of a power system into its contributing appliances. Previous studies only considered using the total power consumption information of appliances to decompose the load consumption. Besides the total electricity consumption, there is also important information such as current, voltage, and time in the total electricity consumption data, which can be used to analyze the load consumption information. Therefore, we proposed a sequence-to-sequence network enhanced by an attention mechanism, which effectively integrated the external features besides the total electricity consumption in grid data. Finally, we applied and evaluated the proposed model on the electricity consumption data of a gas station with 12 appliances, and our model achieved a 90.5% accuracy in load decomposition. Our solution provides a new solution on the application of NILM in the industrial field and helps to manage energy more rationally.