Application of cluster analysis in short-term wind power forecasting model

The Journal of Engineering

  ^{1    1    1    2    2} 

[1] College of Information and Electrical Engineering, Shenyang Agricultural University, 120 Dongling Road, Shenhe District, Shenyang, People's Republic of China;Institute of Electric Power, Shenyang Institute of Engineering, 18 Puhe Road, Shenbei District, Shenyang, People's Republic of China;

关键词: pattern clustering;    weather forecasting;    wind power plants;    neural nets;    power engineering computing;    statistical analysis;    load forecasting;    NWP;    clustering data processing;    numerical weather prediction characteristic parameters;    clustering processing;    cluster analysis;    wind power prediction model;    wind farm;    neural network model;    short-term wind power prediction analysis;    neural network prediction model;    historical data;    data quality;    meteorological wind power generation data;    clustering analysis data processing method;    ultra-short-term predictions;    data integration calculation;    short-term wind power forecasting model;   

DOI  :  10.1049/joe.2018.5488

来源: publisher

【 摘 要 】

At present, the method of predicting wind power generation is mainly based on data integration calculation. Although this method is simple, it has shortcomings in short-term and ultra-short-term predictions owing to low accuracy. In this study, the clustering analysis data processing method is used to pre-process the meteorological wind power generation data, thus improving the data quality. This method builds model samples based on historical data with similar numerical weather prediction (NWP) characteristic parameters of the original sample data and forecast date, takes the NWP information of the forecast date as the basis of similarity measurement, and extracts effective data for the neural network prediction model after the improved clustering processing. Therefore, short-term wind power prediction analysis can be performed. Herein, the proposed data processing method is combined with the neural network model to create a software product that is applied to a wind farm in northeast China. The combined clustering data processing method of the wind power prediction model improved power prediction by ∼12% compared with that of the traditional continuous model. This demonstrates an obvious improvement in the prediction accuracy, thereby further proving the validity of the proposed method.

【 授权许可】

CC BY