Clustering-analysis-based wind power short-term prediction system and prediction method

A short-term forecasting and clustering analysis technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as analysis of wind speed data without meteorological history

Inactive Publication Date: 2015-01-21
SHENYANG INST OF ENG +2
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Problems solved by technology

[0004] Although the theory of cluster analysis is applied in the above patents, it is limited to the classification method of this theory, an

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  • Clustering-analysis-based wind power short-term prediction system and prediction method

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Embodiment

[0033] A short-term prediction system of wind power based on cluster analysis, including a short-term prediction server and a real-time data acquisition device. The real-time data acquisition device includes wind measuring tower, wind turbine data monitoring device, numerical weather forecast receiving device, real-time data acquisition device and short-term forecasting server, and real-time transmission of wind speed, wind direction, temperature, humidity, air pressure, wind power and other data information. The short-term forecasting server is equipped with a power forecasting functional unit and a forecasting database. The power forecasting functional unit receives the data sent by the real-time data acquisition device and stores it in the forecasting database. At the same time, it performs short-term wind power forecasting and saves the forecasted data in the forecasting database. The short-term wind power prediction adopts the wind power prediction method based on cluster ...

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Abstract

The invention discloses a clustering-analysis-based wind power short-term prediction system and prediction method. The prediction system comprises a short-term prediction server and a real-time data acquisition apparatus; and a power prediction function unit and a prediction database are installed at the short-term prediction server. According to the prediction method, a daily correlation analysis is carried out by using a pearson product moment correlation coefficient to determine consistency of the daily correlation of the wind power and daily similar situation of the available weather information; clustering analysis pretreatment is carried out on historical weather database by using a K mean value clustering method; historical day data similar to a weather feature parameter of a prediction day are selected by using a method using an Euclidean distance as a similarity measure and the data are used as the training samples for neural network prediction model establishment; after training based on the similar samples after clustering, a wind power prediction model based on the cluster analysis is obtained; and the prediction day NWP information is used as input parameter of the model and the wind power is used as the model output, so that prediction power data of the prediction day are obtained.

Description

technical field [0001] The invention relates to a short-term wind power forecasting system and forecasting method based on cluster analysis, belonging to the application field of new energy wind power generation. Background technique [0002] In order to effectively utilize wind energy resources, the grid-connected operation of large wind farms needs to be equipped with wind power forecasting systems. If there is no support from the forecasting system, or if the forecast is not accurate enough, the wind farm may be subject to power rationing, resulting in the effective installed capacity of the wind farm not being fully utilized. The power generated by wind turbines in actual operation mainly depends on the local wind resources, and the characteristics of wind resources mainly refer to the changing characteristics of wind speed. Wind speed is the distance that air moves in the horizontal direction per unit time, which is mainly affected by meteorological factors, terrain, s...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/06375G06Q50/06
Inventor 高阳董存葛延峰刘莉刘宝贵李广磊李献伟赵毅
Owner SHENYANG INST OF ENG
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