Photovoltaic generating capacity prediction method based on RBF neural network

Abstract

The invention discloses a photovoltaic generating capacity prediction method based on an RBF (Radial Basis Function) neural network. The photovoltaic generating capacity prediction method based on anRBF neural network includes the steps: constructing a training sample according to photovoltaic generating capacity and historical data of influence factors to be selected; based on the constructed training sample, selecting influence factors of photovoltaic generating capacity by using an improved genetic algorithm, and training the RBF neural network to obtain influence factors of photovoltaic generating capacity and a trained RBF neural network; and inputting the data of a day to be predicted of the influence factors of photovoltaic generating capacity into the trained RBF neural network toobtain a predicted value of the photovoltaic generating capacity. The photovoltaic generating capacity prediction method based on an RBF neural network can preferably solve the generalization problemof the RBF neural network, and can improve accuracy of the photovoltaic generating capacity prediction result.

Description

technical field [0001] The present invention relates to the technical field of photovoltaic power generation, in particular to a prediction of photovoltaic power generation based on a localized generalization error model (Localized Generalization Error Model, abbreviated as L-GEM) radial basis function (abbreviated as RBF) neural network method. Background technique [0002] Large-scale photovoltaic power generation is an effective way to utilize solar energy, but factors such as solar radiation, atmospheric temperature, weather type, and panel temperature easily affect photovoltaic power generation, and it is nonlinear. Therefore, the prediction of photovoltaic power generation is of great significance to rationally arrange the use time of electrical appliances, maximize the use of solar energy resources, and reduce electricity costs. The accurate prediction of photovoltaic power generation depends on the reasonable selection of factors affecting photovoltaic power generat...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a method for forecasting photovoltaic power generation based on RBF neural network, and solve the technical problem of low prediction accuracy of photovoltaic power generation

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