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Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture

A satellite cloud image and power prediction technology, applied in neural learning methods, biological neural network models, predictions, etc., can solve problems such as slow convergence speed, poor global stability, and large errors, and achieve improved accuracy, good global stability, and convergence fast effect

Pending Publication Date: 2022-02-01
DONGYING POWER SUPPLY COMPANY STATE GRID SHANDONG ELECTRIC POWER
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The inventor found that in the photovoltaic power generation prediction model, the historical power generation data contains limited features, while the traditional recurrent network BP neural network has no dynamic characteristics, and has slow convergence speed and poor global stability
The application has a large error in the prediction of photovoltaic power generation, and cannot improve the prediction accuracy very well.

Method used

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  • Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture
  • Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture
  • Photovoltaic power prediction method and system based on Elman neural network and satellite cloud picture

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Embodiment 1

[0043] like Figure 1-8 As shown, this embodiment provides a photovoltaic power prediction method based on the Elman neural network and satellite cloud images. This embodiment uses the method applied to the server as an example. It can be understood that the method can also be applied to the terminal. It can be applied to a terminal, a server and a system, and is realized through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or it can provide cloud services, cloud database, cloud computing, cloud function, cloud storage, network server, cloud communication, intermediate Cloud servers for basic cloud computing services such as software services, domain name services, security service CDN, and big data and artificial intelligence platforms. The terminal may be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart sp...

Embodiment 2

[0089] The present embodiment provides a kind of photovoltaic power prediction system based on Elman neural network and satellite cloud image, comprising:

[0090] The data acquisition module is configured to acquire historical power consumption data and satellite images of the power consumption system to be predicted and perform preprocessing;

[0091] The model building module is configured to build an Elman dynamic recursive neural network model and input preprocessed data for training;

[0092] The photovoltaic power prediction module is configured to input the preprocessed data into the trained Elman model and output the prediction result.

[0093]It should be noted here that the above-mentioned data acquisition module, model building module and photovoltaic power prediction module correspond to steps S100 to S300 in Embodiment 1, and the examples and application scenarios implemented by the above-mentioned modules are the same as those of the corresponding steps, but are...

Embodiment 3

[0095] This embodiment provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, it implements the photovoltaic power prediction method based on the Elman neural network and satellite cloud image as described in the first embodiment above. step.

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Abstract

The invention belongs to the related technical field of photovoltaic power generation, and provides a photovoltaic power prediction method and system based on an Elman neural network and a satellite cloud picture, and the method comprises the steps of: acquiring historical power utilization data and a satellite image of a to-be-predicted power utilization system, and carrying out the preprocessing of the historical power utilization data and the satellite image; building an Elman dynamic recurrent neural network model, and inputting the preprocessed data for training; inputting the preprocessed data into the trained Elman model, and outputting a prediction result; carrying out high-precision extraction on the gray value of the satellite cloud picture, and making preparation for modeling of an Elman neural network prediction model; utilizing an Elman photovoltaic power prediction model and algorithm considering historical operation data and a satellite cloud picture gray value at the same time; acquiring model error evaluation through an error calculation mode of a measured value and a real value, thus improving the precision of power prediction.

Description

technical field [0001] The invention belongs to the technical field related to photovoltaic power generation, and in particular relates to a photovoltaic power prediction method and system based on Elman neural network and satellite cloud images. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] With the acceleration of industrialization and electrification, human demand for energy has soared, especially the demand for electric energy, which is showing a rising trend year by year. For power generation energy, the consumption burden of primary energy such as coal, oil, and natural gas is intensified, and the accompanying cost and pollution problems are particularly prominent. New energy power generation dominated by photovoltaics has been vigorously developed, but due to the instability of weather conditions, photovoltaic power generation is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08G06T7/90G06V10/762
CPCG06Q10/04G06Q50/06G06N3/04G06N3/08G06T7/90G06F18/23Y04S10/50
Inventor 王元元刘航航司君诚蔡言斌刘琪苏小向张丹马晓祎李士峰
Owner DONGYING POWER SUPPLY COMPANY STATE GRID SHANDONG ELECTRIC POWER
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