Non-invasive load monitoring method based on equipment transfer learning

Abstract

The invention relates to a non-invasive load monitoring method based on equipment transfer learning, and the method comprises the steps: collecting the load amplitude data of household electrical appliances and the household total load amplitude data, and building an original data set; normalizing the data of the original data set; constructing an initialization sequence-to-point CNN model to construct a transfer learning equipment selection model, and selecting initial data and target data of transfer learning; performing a training process and a verification process on the initialization sequence-to-point CNN model by adopting the initial data to form a transfer learning initial model; finely adjusting a full connection layer of the initial model by adopting the target data to form a transfer learning target model; and inputting the normalized household total load amplitude data into the transfer learning target model to obtain the power amplitude of the target equipment, thereby realizing non-intrusive load monitoring of the target electrical equipment. According to the invention, the model training time is reduced, and the economic cost and time cost of non-intrusive load monitoring are reduced.

Description

technical field [0001] The invention relates to the field of power metering methods, in particular to a non-intrusive load monitoring method based on equipment migration learning. Background technique [0002] The detailed monitoring of power consumption load of power users is mainly used to obtain information such as power consumption and power consumption behavior of each load equipment in the user. The traditional intrusive power load monitoring needs to install inductive measurement and data transmission devices on each electrical equipment inside the load, which requires large economic investment, complicated management and maintenance, and is not suitable for large-scale promotion. Non-intrusive load monitoring (Non-Intrusive Load Monitoring, NILM) can refine the monitoring to the total load according to the total power consumption of users without installing a large number of monitoring equipment, and obtain the power consumption of each appliance. It has the advanta...

AI Technical Summary

Problems solved by technology

Compared with unsupervised learning methods, supervised learning methods represented by deep neural networks have higher accuracy and can cope with complex situations such as similar load imprints and simultaneous events, but training a model often takes a long time, and the differences between regions The model is not universal, and there is no good solution for data-poor areas

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