Photovoltaic generation capacity prediction method based on particle swarm algorithm wavelet neural network

Abstract

The invention relates to a photovoltaic generation capacity prediction method based on a particle swarm algorithm wavelet neural network. Prediction of solar photovoltaic generation capacity is realized, and organic combination between the particle swarm algorithm and a wavelet neural network learning nervous system is realized. A prediction system comprises a module for optimizing model parameters of a wavelet neural network through the particle swarm algorithm, a wavelet neural network learning training module after optimization, and a wavelet neural network prediction module after training. The prediction method integrates the advantages of the particle swarm algorithm and the advantages of the wavelet neural network. Therefore, the prediction accuracy is improved effectively, the prediction error is reduced, and technical support can be provided for large-scale connection of photovoltaic generated power to the grid. Moreover, the method is portable, and can provide generation capacity prediction for wind and other new energy through simple modification.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a method for predicting photovoltaic power generation based on a particle swarm algorithm wavelet neural network. Background technique [0002] Renewable energy power generation is a relatively efficient and clean renewable energy utilization method, and it is also one of the most mature, large-scale development conditions and commercial development prospects among current renewable energy utilization technologies. Photovoltaic power generation is the main utilization method of renewable energy and is the main component of smart grid. The prediction of short-term power generation is the key to the successful promotion of photovoltaic power generation. It is also the basis for the power dispatching department to formulate power dispatching plans, and it is also an important guarantee for the benefits of self-built photovoltaic power generation systems such as...

AI Technical Summary

Problems solved by technology

[0006] (1) There is only feedforward but no feedback, and the sensitivity to historical data is too poor, which may easily lead to the disappearance of the information of the learned learning mode and is not stable enough;

[0007] (2) The ability to process dynamic information is too weak to directly and dynamically reflect the characteristics of the photovoltaic power generation system in the dynamic process, and it does not have the ability to adapt to time-varying characteristics, and the prediction accuracy fluctuates greatly

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