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

A technology of load identification and load monitoring, applied in biological neural network models, instruments, data processing applications, etc., can solve the problem of low decomposition accuracy, difficulty in effectively handling multi-working mode electrical appliances, and high sampling frequency requirements for power monitoring equipment. problems, to achieve the effect of improving accuracy and recognition efficiency

Inactive Publication Date: 2019-03-22
SHENZHEN URBAN PUBLIC SAFETY & TECH INST CO LTD
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Problems solved by technology

[0004] Bottlenecks and research difficulties encountered in the current non-intrusive load monitoring technology: including high sampling frequency req

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

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[0040] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0041] In order to make the description of the present disclosure more detailed and complete, the following provides an illustrative description of the implementation modes and specific examples of the present invention; but this is not the only form for implementing or using the specific embodiments of the present invention. The description covers features of various embodiments as well as method steps and their sequences for constructing and operating those embodiments. However, other embodiments can also be used to achieve the same or equivalent functions and step sequences.

[0042] ...

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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...

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Application Information

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IPC IPC(8): G06Q10/06G06Q50/06G06N3/04
CPCG06Q10/0639G06Q10/067G06Q50/06G06N3/045
Inventor 尹继尧汪大卫何晨伟
Owner SHENZHEN URBAN PUBLIC SAFETY & TECH INST CO LTD
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