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

A neural network and output power technology, applied in the field of photovoltaic output power ultra-short-term local emotion reconstruction neural network prediction

Pending Publication Date: 2020-12-25
SHANGHAI MUNICIPAL ELECTRIC POWER CO +1
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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 loc

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

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[0060] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0061] Such as figure 1 The shown photovoltaic output power ultra-short-term local emotional reconstruction neural network prediction method, the method includes the following steps:

[0062] S1. Use the singular spectrum analysis method to perform noise reduction processing on the original photovoltaic power time series to obtain a relatively stable time series, and use the chaotic nonlinear dynamics method to further mine the hidden fluctuation information of this series to reconstruct the phase space;

[0063] S11. Take the window length L (L is a positive integer), and slide the ...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084G06N3/045G06N3/044Y04S10/50Y02A30/00
Inventor 时珊珊王育飞张宇徐琴薛花方陈魏新迟
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
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