Photovoltaic power generation prediction system based on T-S-type fuzzy neural network

Abstract

The invention discloses a photovoltaic power generation prediction system based on a T-S-type fuzzy neural network. The photovoltaic power generation prediction system comprises a fuzzy neural network structure module, a fuzzy neural network training module and a fuzzy neural network prediction module. According to the photovoltaic power generation prediction system based on the T-S-type fuzzy neural network, prediction of solar photovoltaic power generation capacity is achieved, dynamic integration of a fuzzy inference system and a neural network learning system is achieved, algorithms are advanced, prediction accuracy is high, power grid dispatching efficiency is improved, safe operation of a power grid is guaranteed, meteorological factors are introduced, prediction accuracy and reliability are improved, technical support is provided for large-scale grid connection of solar photovoltaic power generation, transportability is high, simple modifications are only needed, and the power generation prediction system can also be provided for wind energy source and other new energy sources.

Description

technical field [0001] The invention relates to the technical field of solar photovoltaic power generation forecasting systems, in particular to a photovoltaic power generation forecasting system based on a T-S type fuzzy neural network. Background technique [0002] Solar photovoltaic power generation has the advantages of high conversion efficiency, long service life, and no moving parts. At present, foreign solar photovoltaic power generation has completed the initial development stage and is developing into the stage of large-scale application. However, due to the intermittent and random characteristics of solar energy, with the rapid expansion of photovoltaic installed capacity and large-scale photovoltaic grid connection, it will not be conducive to the stability of the grid and have a profound impact on the power market. Therefore, predicting photovoltaic power generation The power generation of the system is of great significance to the dispatching of power grid powe...

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Problems solved by technology

[0004] At present, the prediction technology mainly includes neural network technology. Neural network has a strong ability to deal with nonlinear problems, but neural network prediction technology also has some problems of its own, which requires a large number of learning samples, the randomness of network initial Disadvantages such as uncertainty in structural design

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