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Load prediction method based on ant colony neural network, system comprising load prediction method and memory comprising load prediction method

A neural network and load forecasting technology, which is applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as poor forecasting accuracy of BP neural network forecasting models, and achieve fast calculation speed, accelerated convergence speed, and good applicability Effect

Inactive Publication Date: 2019-08-30
广州水沐青华科技有限公司
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Problems solved by technology

[0005] In order to solve the poor problem of BP neural network prediction model prediction accuracy, solve the problem of ant colony algorithm optimal solution and algorithm complexity matching, the purpose of the present invention adopts the following technical solutions

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  • Load prediction method based on ant colony neural network, system comprising load prediction method and memory comprising load prediction method
  • Load prediction method based on ant colony neural network, system comprising load prediction method and memory comprising load prediction method
  • Load prediction method based on ant colony neural network, system comprising load prediction method and memory comprising load prediction method

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[0046] In order to solve the problem of poor prediction accuracy of the BP neural network prediction model, the present invention will use the ant colony algorithm to confirm the initial parameters in the neural network, accelerate the convergence speed of the BP neural network and improve the accuracy of the algorithm. First, extract the factors that affect the power load and the historical load data of the corresponding time period; then, preprocess the data, remove the abnormal data and perform normalization processing, and establish the BP neural network model; finally, use the ant colony algorithm to find the most The excellent value is used as the parameter of the BP neural network, and the output result is compared with the actual value to calculate the error. The convergence speed of the improved prediction model is improved, and the prediction accuracy is also improved. Below, in conjunction with accompanying drawing and specific embodiment, the present invention is d...

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Abstract

The invention discloses a load prediction method based on an ant colony neural network, and a system and a memory comprising the method, and the method comprises the steps: taking factors influencinga power load and historical load data of a corresponding time period as training samples, carrying out the preprocessing of the data, and building a BP neural network prediction model optimized by theant colony algorithm. According to the method, the ant colony algorithm is used for searching the optimal value as the parameter of the BP neural network, so that the convergence rate of the BP neural network is increased, and meanwhile, the prediction accuracy is effectively improved. The invention further provides a system comprising the method and a storage device according to the method.

Description

technical field [0001] The invention relates to the field of smart grid forecasting, in particular to a load forecasting method based on an ant colony neural network, a system and a memory including the method. Background technique [0002] Since the advent of electromagnetism in the 17th century, electricity has played an increasingly important role in people's lives. Electric energy is not easy to store. If more electric energy is produced than used, it will be wasted. If less electric energy is produced than used, it will cause a shortage of electric energy. Therefore, electric load forecasting is an important research topic from beginning to end. [0003] BP neural network is one of the load forecasting methods based on ant colony neural network that has been widely used in recent years. It is more convenient to deal with factors such as temperature, humidity, and wind series, and has strong self-adaptability to unstructured data. It is widely used in the field of big ...

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00G06N3/04G06N3/08G06Q50/06
CPCG06Q10/04G06Q50/06G06N3/006G06N3/08G06N3/044G06N3/045
Inventor 孙立明
Owner 广州水沐青华科技有限公司
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