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Regional wind power prediction method and system based on space-time quantile regression

A technique of quantile regression and forecasting methods, which is applied in forecasting, information technology support systems, data processing applications, etc. to reduce intermittency and volatility and improve stability.

Active Publication Date: 2020-01-03
SHANDONG UNIV +3
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the deficiencies of the prior art, the present disclosure provides a regional wind power forecasting method and system based on spatio-temporal quantile regression, which solves the problem of selection of explanatory variables in regional wind power forecasting with large input information , which greatly improves the accuracy and reliability of wind power forecasting, and provides a specific solution for regional wind power generation probability forecasting with the characteristics of big data

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  • Regional wind power prediction method and system based on space-time quantile regression
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  • Regional wind power prediction method and system based on space-time quantile regression

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Embodiment 1

[0051] Such as figure 1 As shown, Embodiment 1 of the present disclosure provides a regional wind power prediction method based on spatiotemporal quantile regression, and the steps are as follows:

[0052] Collect the operation and numerical weather prediction data of multiple wind farms within a preset time period, convert the collected data into feature maps, and establish training sets, verification sets and test sets;

[0053] Establish a spatio-temporal quantile regression model, use the training set to train the model, use the verification set to diagnose the adaptability of the model and optimize the model hyperparameters, use the test set to evaluate the reliability and sharpness of the model, and further optimize the model according to the evaluation results;

[0054] The operating data and environmental data of each wind farm are collected in real time, and the regional wind power generation prediction for a certain period of time in the future is carried out accordi...

Embodiment 2

[0137] Embodiment 2 of the present disclosure provides a regional wind power forecasting system based on spatiotemporal quantile regression, including:

[0138] The data acquisition and preprocessing module is configured to: collect the operation and numerical weather prediction data of multiple wind farms within a preset time period, convert the collected data into feature maps, and establish training sets, verification sets and test sets;

[0139] The model building module is configured to: establish a spatiotemporal quantile regression model, use the training set to train the model, use the verification set to diagnose the model adaptability and optimize the model hyperparameters, use the test set to evaluate the reliability and sharpness of the model, Further optimize the model according to the evaluation results;

[0140] The prediction module is configured to: collect the operating data and environmental data of each wind farm in real time, and perform regional wind powe...

Embodiment 3

[0142] Embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the regional wind power prediction method based on spatiotemporal quantile regression described in Embodiment 1 of the present disclosure is implemented. A step of.

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Abstract

The invention provides a regional wind power prediction method and system based on space-time quantile regression. The method comprises the following steps: collecting the operation and numerical weather prediction data of a plurality of wind power plants in a preset time period, converting the collected data into a feature map, and building a training set, a verification set and a test set; establishing a space-time quantile regression model, and training and optimizing the model by utilizing the training set, the training set, the verification set and the test set; acquiring operation data and environment data of each wind power plant in real time, and predicting regional wind power generation in a certain time period in the future according to the optimized space-time quantile regression model. According to the invention, short-term non-parameterized probability prediction is carried out on regional wind power through the space-time quantile regression model; the selection problem of explanatory variables in regional wind power prediction with large input information is solved, the prediction accuracy and reliability are greatly improved, and a specific solution is provided forregional wind power generation probability prediction with big data.

Description

technical field [0001] The present disclosure relates to the technical field of wind power forecasting, in particular to a regional wind power forecasting method and system based on spatiotemporal quantile regression. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Due to the intermittent and fluctuating characteristics of wind power generation, a high proportion of wind power will pose a serious challenge to the safe and stable operation of the power system. Accurate short-term wind power forecasting can effectively alleviate the adverse effects of wind power. Previous studies have mainly focused on point forecasting of wind power generation. However, forecast errors are inevitable due to the inherent intermittent and fluctuating nature of the wind. Alternatively, probabilistic forecasts can quantify wind uncertainty information such...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06F17/18
CPCG06Q10/04G06Q50/06G06N3/049G06F17/18G06N3/045Y04S10/50Y02A30/00
Inventor 杨明于一潇韩学山杨佳峻韩月段方维
Owner SHANDONG UNIV
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