Photovoltaic output power ultra-short-term local emotion reconstruction neural network prediction method

Abstract

The invention relates to a photovoltaic output power ultra-short-term local emotion reconstruction neural network prediction method, and the method employs a singular spectrum analysis method to reduce the noise of original photovoltaic power, and can reduce the complexity of a photovoltaic power time sequence. Implicit fluctuation information mining of a photovoltaic power time sequence through achaotic nonlinear dynamics method is facilitated, meteorological data does not need to be acquired in advance in actual engineering, and the problem of model complexity caused by improper selection of input parameters and the problem of error accumulation caused by inaccurate prediction of the meteorological data can be avoided; the built local emotion reconstruction neural network does not needto determine the number of hidden layer nodes, modeling is simple, and the prediction cost is reduced; the established local emotion reconstruction neural network determines expansion signals and emotion parameters according to photovoltaic power chaotic attractors, so that the prediction model more pays attention to tracking each input mode and selects the most useful input mode information, themapping relationship is more accurate, the prediction accuracy is higher under different weather conditions, and the adaptability is higher.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a neural network prediction method for ultra-short-term local emotion reconstruction of photovoltaic output power based on singular spectrum analysis. Background technique [0002] In order to reduce carbon emissions caused by fossil fuels and comply with the trend of global environmental protection, photovoltaic power generation has been widely used as an easily accessible and environmentally friendly energy source. However, due to the influence of various meteorological factors, photovoltaic power presents a high degree of randomness and volatility in a short period of time, and the photovoltaic output power curve fluctuates greatly. The huge impact, photovoltaic power generation power forecasting technology is an important measure to solve this problem. Therefore, by accurately predicting the output power of photovoltaic power generation and coordinating...

AI Technical Summary

Problems solved by technology

[0005] The present invention is aimed at the problem of ultra-short-term photovoltaic power prediction, and proposes an ultra-short-term local emotion reconstruction neural network prediction method for photovoltaic output power, and the ultra-short-term prediction is within hours

Images