Photovoltaic generating capacity prediction method based on fuzzy EBF (Elliptical Basis Function) network

Abstract

The invention discloses a photovoltaic generating capacity prediction method based on a fuzzy EBF (Elliptical Basis Function) network. The photovoltaic generating capacity prediction method comprises the following steps of: selecting the influence factor of a photovoltaic generating capacity, collecting the historical data of the influence factor of the photovoltaic generating capacity and photovoltaic generating capacity historical data corresponding to the historical data of the influence factor of the photovoltaic generating capacity, and determining a sample set; generating the historical data of the influence factor of the photovoltaic generating capacity in a training sample set into an input vector, taking the photovoltaic generating capacity historical data corresponding to the historical data of the influence factor of the photovoltaic generating capacity as an output vector, carrying out normalization processing, and determining a training sample; utilizing the training sample to train the fuzzy EBF network through a Levenberg-Marquardt algorithm, collecting the influence factor data of the photovoltaic generating capacity of a day to be predicted, generating a prediction input vector, carrying out the normalization processing, inputting the prediction input vector into the trained fuzzy EBF network to obtain a photovoltaic generating capacity prediction output vector, and carrying out reverse normalization processing on the prediction output vector to obtain a prediction photovoltaic generating capacity vector of the day to be predicted. By use of the photovoltaic generating capacity prediction method, the prediction accuracy of the photovoltaic generating capacity is improved.

Description

technical field [0001] The invention relates to a method for forecasting photovoltaic power generation based on fuzzy EBF network. Background technique [0002] With the rapid development of society, the massive consumption of traditional energy has caused people to face problems such as the exhaustion of non-renewable energy and serious environmental pollution in industrial development and daily life. As a renewable energy source, solar energy has become an important part of energy used by human beings due to its characteristics of cleanness, environmental protection, safety, and low cost, and it has been continuously developed. [0003] Large-scale photovoltaic power generation is an effective way to utilize solar energy, but factors such as solar radiation, weather temperature, weather type, and conversion rate of solar photovoltaic panels easily affect photovoltaic power generation, and it is nonlinear. Therefore, the prediction of solar power generation is of great sig...

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Problems solved by technology

[0007] In order to solve the above problems, the present invention proposes a method for predicting photovoltaic power generation based on fuzzy EBF networks. This method solves the problems that the algorithm can fall into local extreme values, the parameter initialization selection is too wide, and the solution accuracy is low.

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