电测与仪表Issue(15)：1-7,7.
基于 FKNN 算法的风电功率短期预测
Short - Term Wind Power Prediction Based on FKNN Algorithm
摘要
Abstract
The improvement of wind farm’s output power prediction accuracy can greatly reduce the impact of wind power on the grid and improve the security and reliability of wind power integration. In this paper,the FKNN(Fast K- Nearest Neighbor algorithm)algorithm is proposed to improve the shortcomings of KNN(K - Nearest Neighbor algo-rithm)algorithm and is used for short - term wind power prediction. First,for each prediction sample,by using FKNN algorithm,which is based on the principle of similarity data,you can obtain the maximum priority queue of similar sample through traversing the set of training sample only one time. Then,gradually reduce the length of the priority queue to produce different size priority sub - queues of similar sample in which the majority class samples can be obtained and its average is used to predict the output power of prediction sample. Finally,the algorithm’s simplici-ty and practicality was fully proved through the prediction of a large amount historical data of a wind farm in Jilin Prov-ince.关键词
风电功率短期预测/FKNN 算法/相似数据/K - means 聚类算法Key words
short - term wind power prediction/FKNN algorithm/similar data/K - means clustering algorithm分类
信息技术与安全科学引用本文复制引用
郭晓利,张玉萍,曲朝阳,任有学,辛鹏..基于 FKNN 算法的风电功率短期预测[J].电测与仪表,2014,(15):1-7,7.基金项目
国家自然科学基金资助项目（51277023）；吉林省自然科学基金 （）
