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

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...

AI Technical Summary

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 in the input after each training , the relationship between the outputs is uncertain, and the prediction results are different

It can be seen that the existing technology scheme has the problems of slow convergence speed and large prediction error

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