Electrical power system load prediction method and device based on depth belief network

A deep belief network and power system technology, which is applied in the field of power system load forecasting based on a deep belief network, can solve problems such as differences in forecast results, large forecast errors, and uncertain relationships between input and output, and achieve reduced forecast errors and improved Convergence speed, effect of improving learning performance

Inactive Publication Date: 2017-05-24
POWER GRID TECH RES CENT CHINA SOUTHERN POWER GRID +5
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Problems solved by technology

However, the traditional neural network is a typical global approximation network, and one or more weights of the network have an influence on each output; on the other hand, the network has randomness in determining the weights, resulting ...

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  • Electrical power system load prediction method and device based on depth belief network
  • Electrical power system load prediction method and device based on depth belief network
  • Electrical power system load prediction method and device based on depth belief network

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Embodiment Construction

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0019] Aiming at the slow convergence speed and large prediction error of traditional neural network load forecasting, this paper proposes a power system load forecasting scheme based on Deep Belief Network (English full name: Deep Belief Network, English abbreviation: DBN). figure 1 As shown, the method includes the following steps:

[0020] 101. Obtain training samples and test samples.

[0021] 102. Construct the energy function of the RBM model.

[0022]...

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Abstract

The embodiment of the invention provides an electrical power system load prediction method and device based on a depth belief network (DBN) and relates to the field of electric power systems. Through the method and device, the convergence rate can be increased, and the prediction error can be lowered. According to the specific scheme, the method comprises the steps that a training sample and a test sample are acquired; an energy function of an RBM model is constructed; the training sample is utilized to perform layer-by-layer training on at least one hidden layer and visible layer to obtain weights of the training sample among nodes of the hidden layers and the visible layers; and a prediction value of an electrical power system load is obtained according to output data obtained through the training sample and the DBN obtained after test sample input is trained. The method and device are used for electrical power system load prediction.

Description

technical field [0001] Embodiments of the present invention relate to the field of power systems, and in particular to a method and device for power system load forecasting based on a deep belief network. Background technique [0002] Power system load forecasting is an important part of power system planning and the basis of power system economic operation. It starts from the known power demand and fully considers the influence of political, economic, climate and other related factors to predict future power demand. . Accurate load forecasting data is helpful for power grid dispatching control and safe operation, formulating reasonable power supply construction planning and improving the economic and social benefits of the power system. In load forecasting, methods such as regression model, time series forecasting technology and gray theory forecasting technology are often used. In recent years, with the rise of artificial neural network research, the use of neural networ...

Claims

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Application Information

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IPC IPC(8): G06Q50/06G06Q10/04
CPCG06Q10/04G06Q50/06
Inventor 吴争荣董旭柱陆锋刘志文陶文伟谢雄威陈立明何锡祺俞小勇陈根军禤亮苏颜李瑾陶凯
Owner POWER GRID TECH RES CENT CHINA SOUTHERN POWER GRID
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