Photovoltaic power generation forecasting method based on GRNN

Abstract

The invention relates to a photovoltaic power generation forecasting method based on a GRNN. According to the method, firstly factors such as solar terms, weather, and sunlight are considered to establish a photovoltaic curve pattern, then a photovoltaic power generation forecasting model based on the GRNN is provided, and a solution algorithm design is conducted. The method has the following advantages that weight does not need to be amended through error reverse calculation in the training process of the GRNN, and the transfer function can be adjusted by just changing a smoothing parameter Sigma, so that the training time is reduced, and the network learning speed is increased; a GRNN forecasting model is high in nonlinear mapping ability and good in approximation performance, and has good robustness, and thus is suitable for processing unstable data; the photovoltaic power generation forecasting technology of the GRNN obviously improves the forecasting accuracy; the forecasting result can provides decision information for grid photovoltaic scheduling, and has great significance in guaranteeing safe operation of the grid.

Description

technical field [0001] The invention relates to a method for predicting photovoltaic power generation based on a generalized regression (GRNN) neural network. Background technique [0002] Since solar energy is a renewable and clean energy source, new energy sources such as solar energy have been widely used at home and abroad. The photovoltaic power generation power of solar energy is also affected by uncertain factors such as weather and sunshine, and has strong random intermittency. It brings instability to the safe operation of the power grid. Improving the prediction accuracy of photovoltaic power generation has extensive application value for the formulation of power generation plans and ensuring the safe operation of the power grid. Contents of the invention [0003] The purpose of the present invention is to provide a photovoltaic power generation prediction method based on the GRNN neural network, first consider the factors of solar terms, weather, and sunshine, ...

AI Technical Summary

Problems solved by technology

[0002] Since solar energy is a renewable and clean energy source, new energy sources such as solar energy have been widely used at home and abroad. The photovoltaic power generation of solar energy and wind power generation are also affected by uncertain factors such as weather and sunshine, and have strong random intermittency. Brings instability to the safe operation of the power grid

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