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Wind power generation capacity prediction method based on graph neural model and related equipment thereof

A forecasting method and power generation technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as insufficient accuracy, large fluctuation range of wind power generation time series values, and difficulties in modeling and forecasting, etc. To achieve the effect of improving accuracy

Pending Publication Date: 2021-12-10
华润数字科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The time series of wind power generation has a large fluctuation range, modeling and forecasting are relatively difficult, and the accuracy of existing forecasting methods is not high enough

Method used

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  • Wind power generation capacity prediction method based on graph neural model and related equipment thereof
  • Wind power generation capacity prediction method based on graph neural model and related equipment thereof
  • Wind power generation capacity prediction method based on graph neural model and related equipment thereof

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Embodiment Construction

[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field of the application; the terms used herein in the description of the application are only to describe specific embodiments The purpose is not to limit the present application; the terms "comprising" and "having" and any variations thereof in the specification and claims of the present application and the description of the above drawings are intended to cover non-exclusive inclusion. The terms "first", "second" and the like in the description and claims of the present application or the above drawings are used to distinguish different objects, rather than to describe a specific order.

[0045] Reference herein to an "embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The occurrenc...

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Abstract

The embodiment of the invention belongs to the technical field of computers, and relates to a wind power generation capacity prediction method based on a graph neural model and related equipment thereof. The method comprises the steps of obtaining the features of fans in a preset space and the relation between the fans, and generating a fan feature matrix and a fan adjacency matrix, inputting the fan feature matrix and the fan adjacency matrix into a pre-trained graph neural model to obtain output fan spatial features, and inputting the fan spatial features into a pre-trained power generation capacity prediction model to obtain an output fan power generation capacity prediction value. The accuracy of power generation capacity prediction can be improved.

Description

technical field [0001] The present application relates to the technical field of wind power generation, in particular to a graph neural model-based wind power generation prediction method and related equipment. Background technique [0002] With the development of computer technology and the application of advanced control systems, the equipment and structure of modern industrial systems are becoming more and more complex, and industrial processes have accumulated a large amount of historical data, which includes the operating rules of the process, operator experience, product quality and process problems. and other rich information. Wind power is a kind of renewable energy, which can effectively save standard coal and reduce gas emissions such as sulfur dioxide, carbon dioxide and nitrogen oxides in the process of power generation. The short-term forecasting of wind power generation is of great significance for power system dispatchers to formulate power generation plans, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06N3/04G06N3/08G06Q10/04G06Q50/06G06F113/06
CPCG06F30/27G06Q10/04G06Q50/06G06N3/08G06F2113/06G06N3/044G06N3/045Y04S10/50
Inventor 刘雨桐石强熊娇王国勋张兴
Owner 华润数字科技有限公司