Wavelet packet-neural network-based wind/photovoltaic power prediction method

A power prediction and neural network technology, applied in biological neural network models, special data processing applications, instruments, etc., can solve the problems of insufficient prediction ability, output power fluctuation, simple data structure, etc., to improve prediction accuracy and improve mapping. effect of ability

Inactive Publication Date: 2017-02-22
XINJIANG UNIVERSITY +1
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Problems solved by technology

[0007] The present invention provides, overcomes the above-mentioned deficiencies in the prior art, and it can effectively solve the problem that the statistical method based on historical data has a simple data structure, is not suitable for

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  • Wavelet packet-neural network-based wind/photovoltaic power prediction method
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  • Wavelet packet-neural network-based wind/photovoltaic power prediction method

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Embodiment 1

[0042] Embodiment 1: as attached figure 1 , 2 , 3, 4, 5, 6, and 7, a method for forecasting wind power based on wavelet packet-neural network includes the following steps:

[0043] Step 1: Determine the actual historical active power data and related meteorological data of wind farms and photovoltaic power plants, that is, historical data of wind speed, ambient temperature, solar radiation intensity, and relative humidity;

[0044] Step 2: Set the sampling interval for the historical data of wind speed, ambient temperature, solar radiation intensity, and relative humidity within the past three months before the prediction;

[0045] Step 3: Calculate the correlation coefficient between wind power, photovoltaic power and various meteorological factors, and select the meteorological factors with higher correlation coefficients as network input, among which the higher correlation coefficients with wind power are: wind speed and temperature; The higher correlation coefficients ar...

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Abstract

The invention relates to the technical field of wind/photovoltaic power generation, and discloses a wavelet packet-neural network-based wind/photovoltaic power prediction method. The method comprises the following steps of: firstly analyzing weather forecast data and history data, and assessing the influences, on the photovoltaic power prediction accuracy, of the weather forecast data and the history data; analyzing the influences, on the wind power prediction accuracy, of factors such as temperature, humidity and atmospheric pressure in the natural environment; carrying out wavelet packet decomposition on the data to obtain different frequency component sequences of a meteorological factor sequence and of a wind/photovoltaic active power sequence to ensure that the representation of change characteristics on various spatial and temporal scales is more accurate, wherein the low-frequency components and the high-frequency components respectively map the interior change rules of predicted objects from two scales of profile and detail; aiming at each sequence component, establishing a plurality of BP networks to carry out prediction; and finally reconstructing each network prediction output to obtain a complete prediction result. The method disclosed by the invention has the effects of improving the ability of mapping wind/photovoltaic power change characteristics and effectively improving the wind/photovoltaic power prediction accuracy.

Description

technical field [0001] The invention relates to the technical field of wind power generation, and relates to a wind power prediction method based on a wavelet packet-neural network. Background technique [0002] With the increasing installed capacity of wind power and photovoltaic power generation, the problems brought about by large-scale renewable energy power generation are also becoming more and more obvious. For example, wind energy and solar power generation rely on meteorological characteristics, showing obvious randomness, and the quality of the generated energy is low, which brings difficulties to power grid dispatching and control. Therefore, a more accurate forecast of the output power of wind power and photovoltaic power generation systems can help to rationally configure the output power of wind and solar power, so that it can achieve optimal regulation to meet the load and related grid-connected requirements. The accuracy of existing prediction methods such as...

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Application Information

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IPC IPC(8): G06F19/00G06N3/02
CPCG06N3/02G16Z99/00
Inventor 张新燕梅生伟王维庆常喜强何山吕光建陈杰李建李猛刘峰陈来军黄芙蓉
Owner XINJIANG UNIVERSITY
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