Non-invasive load identification model training method, load monitoring method and device

Abstract

The invention relates to the technical field of power load monitoring, in particular to a non-invasive load identification model training method, a load monitoring method and a device. The monitoringmethod comprises the following steps: acquiring the original load monitoring data at the entrance of the user's electric power through the Internet of Things equipment, and acquiring the usage situation of the electric appliance; processing The original load monitoring data by noise reduction and time-frequency decomposition. Selecting part load monitoring data, obtaining the corresponding time data and frequency data as training samples, taking the use of electrical appliances as the identification result, constructing the load identification model by training convolutional neural network. The non-invasive load identification model training method of the invention trains the convolution neural network according to the load monitoring data after the time-frequency decomposition, The non-invasive load monitoring method and system of the present invention input load monitoring data based on the load identification model to identify the usage of consumer electrical appliances, thereby improving the accuracy and efficiency of load identification.

Description

technical field [0001] The invention relates to the technical field of electric load monitoring, in particular to a non-invasive load identification model training method, a load monitoring method and a device. Background technique [0002] Residential power load monitoring and decomposition technology is a brand-new technology for monitoring the details of load power consumption. Residential power consumption detail monitoring can optimize power grid planning, operation and management for power companies, save power consumption and electricity charges for power users, and provide household services for the society. It is of great significance in terms of electrical safety. [0003] The current load monitoring system can be roughly divided into two categories: intrusive and non-invasive. Traditional intrusive load monitoring systems install sensors at each load to monitor the operation of each load. A notable feature of this approach is that it usually has complex hardware...

AI Technical Summary

Problems solved by technology

[0004] Bottlenecks and research difficulties encountered in the current non-intrusive load monitoring technology: including high sampling frequency requirements for power monitoring equipment, low decomposition accuracy, difficulty in effectively handling multi-working mode electrical appliances, etc.

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