Load identification method, device and terminal
A load identification and load technology, applied in the field of machine learning, can solve problems such as long time-consuming and low efficiency, and achieve the effects of improving practicability, expanding the search range, and improving training effects
Inactive Publication Date: 2020-08-21
广州水沐青华科技有限公司
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Problems solved by technology
[0003] During the implementation process, the inventors found that there are at least the following problems in the traditional technology: the traditional load identification method has the problems of low efficiency and long time consumption
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[0064] In order to make the purpose, technical solution and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present application, and are not intended to limit the present application.
[0065] In one embodiment, such as figure 1 As shown, a load identification method is provided, including steps:
[0066] S110, establishing an initial neural network model, and acquiring parameters to be optimized in the initial neural network model;
[0067] Specifically, to establish a neural network model, it is first necessary to determine the number of neurons in the input layer, the number of hidden layers, the number of neurons in the hidden layer, and the number of neurons in the output layer, and then determine the input mode of the neurons and the number of neur...
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The invention relates to a load identification method and device and a terminal. The load identification method comprises the steps of establishing an initial neural network model, and obtaining to-be-optimized parameters in the initial neural network model; obtaining an initial solution of the to-be-optimized parameter and a fitness value of the initial solution according to a particle swarm optimization algorithm; performing iterative updating processing on the initial solution to obtain an iterative solution and obtain a fitness value of the iterative solution; if the fitness value of the iterative solution is smaller than the fitness value of the initial solution, replacing the initial solution with the iterative solution; if the fitness value of the iterative solution is greater thanthe fitness value of the initial solution, replacing the initial solution with the iterative solution according to the acceptance probability; performing the next iteration until the number of iterations reaches a preset value, and confirming the current global optimal solution of the to-be-optimized parameter as the initial parameter of the initial neural network model; adjusting the initialization parameters to obtain a current neural network model; and identifying the input load by adopting the current neural network model to obtain the category of each input load.
Description
technical field [0001] The present application relates to the technical field of machine learning, in particular to a load identification method, device and terminal. Background technique [0002] In recent years, with the continuous deepening of research on artificial intelligence, the power grid has gradually become more intelligent, and non-intrusive load identification has become a research hotspot. The so-called non-intrusive load identification is to install non-intrusive terminal equipment at the user's power entrance to decompose the user's power consumption. The significance of this technology is that for power companies, it can help them obtain detailed electricity consumption information of users, improve the scientificity of power grid planning schemes and ensure the real-time safe and economic operation of the power grid; Make adjustments to save electricity. It can be seen that this technology has broad development prospects and research value. [0003] Duri...
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Login to View More IPC IPC(8): G06Q10/06G06N3/00G06N3/04G06N3/06G06N3/08G06Q50/06
CPCG06Q10/0639G06Q50/06G06N3/006G06N3/061G06N3/08G06N3/045
Inventor 孙立明余涛
Owner 广州水沐青华科技有限公司