All-weather solar radiation remote sensing forecasting method under deep learning support

A technology of solar radiation and deep learning, which is applied in the field of remote sensing forecasting, can solve the problems that the absolute numerical accuracy of solar radiation and the distribution of spatial and temporal characteristics cannot be taken into account at the same time, and achieve the effect of superior anti-interference ability, strong robustness, and high precision

Pending Publication Date: 2022-08-02
SUN YAT SEN UNIV
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Problems solved by technology

[0005] In order to solve the above-mentioned problems in the prior art, the present invention provides an all-weather solar radiation remote sensing forecasting method supported by deep learning, which breaks through the inability of the

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  • All-weather solar radiation remote sensing forecasting method under deep learning support
  • All-weather solar radiation remote sensing forecasting method under deep learning support
  • All-weather solar radiation remote sensing forecasting method under deep learning support

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Embodiment 1

[0066] like figure 1 and figure 2 The present embodiment as shown discloses an all-weather solar radiation remote sensing forecasting method supported by deep learning, which includes the following steps:

[0067] S1. Obtain time series multispectral remote sensing data; it should be noted that the time series multispectral remote sensing data here includes continuous time series multispectral remote sensing data and instantaneous time series multispectral remote sensing data.

[0068] S2. Perform data preprocessing on the acquired time series multispectral remote sensing data to obtain a preprocessed time series multispectral remote sensing data set;

[0069] S3. Input the acquired preprocessed time series multispectral remote sensing data set into the ConvLSTM network model to obtain instantaneous solar shortwave radiation prediction data or multi-time scale solar shortwave radiation prediction; the ConvLSTM network model obtains normalized solar shortwave radiation data, ...

Embodiment 2

[0113] In this embodiment, there are two parallel routes, which are:

[0114] 1. Instantaneous solar shortwave radiation prediction supported by deep learning technology;

[0115] 2. Multi-time scale solar shortwave radiation prediction supported by deep learning technology;

[0116] like figure 1 As shown: the first technical route in this embodiment, that is, the instantaneous solar shortwave radiation prediction supported by deep learning technology, the technical process is as follows:

[0117] Taking the Sunflower 8 multi-spectral optical remote sensing satellite as an example, the multi-spectral optical satellite remote sensing data of continuous time series are firstly obtained. Three types of raw data of zenith angle SOZ (Solar of Zenith) data and solar shortwave radiation data;

[0118] Data preprocessing is carried out on the acquired remote sensing data. The data preprocessing includes two parts: 1) Data quality control, checking the quality of the obtained data,...

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Abstract

The invention relates to an all-weather solar radiation remote sensing forecasting method under deep learning support. The method comprises the following steps: S1, acquiring time sequence multispectral remote sensing data; s2, performing data preprocessing on the obtained time sequence multispectral remote sensing data to obtain a preprocessed time sequence multispectral remote sensing data set; s3, inputting the obtained preprocessed time sequence multispectral remote sensing data set into a ConvLSTM network model to obtain instantaneous solar short-wave radiation prediction data or multi-time scale solar short-wave radiation prediction; the ConvLSTM network model is a deep learning network model which faces remote sensing images and is combined with a space attention mechanism. According to the forecasting method provided by the invention, the spatial characteristics and the time sequence characteristics of the solar radiation are completely considered, a spatial attention mechanism is added in the deep neural network, and mutation of the solar radiation in the nature is more effectively captured.

Description

technical field [0001] The invention belongs to the technical field of remote sensing forecasting, in particular to an all-weather solar radiation remote sensing forecasting method supported by deep learning. Background technique [0002] In the field of solar photovoltaic forecasting (solar radiation forecasting), accurately predicting the trend of solar radiation changes and predicting the value of solar radiation in advance is of great significance for new energy industries such as photovoltaic power generation peak forecasting and photovoltaic power station site selection. With the continuous development of deep neural network methods, some neural network models have been applied to the prediction of solar radiation and have shown good performance. However, the training and testing data that traditional deep neural network models rely on when predicting solar radiation are mostly site data, that is, point data at a single location, and the true value referenced by tradit...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06Q10/04G06N3/08G06N3/045Y02A90/10
Inventor 孔轩王天星程文杰
Owner SUN YAT SEN UNIV
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