电测与仪表Issue(24)：38-43,6.
基于改进 KNN 算法的风电功率实时预测研究
Wind power real-time prediction research based on the improved KNN algorithm
摘要
Abstract
Integration of large-scale wind power into the power grid will greatly influence grid planning and construc-tion, analysis and control, and energy quality.Accurate short-term wind power forecasting can provide a reliable basis for safety dispatching and stable operation of the power system containing large-scale wind power generating units. This paper studied wind power short-term prediction methods.With the chaos theory as the basis, the parameters for phase space reconstruction were calculated, and a wind power real-time prediction method based on the improved KNN( K-Nearest Neighbor) algorithm was proposed.Multiple evaluation indexes were applied to evaluate the forecast results, and the effectiveness of the model was verified with the measured data of a wind farm in the west of Jilin as the sample.关键词
风力发电/功率预测/混沌时间序列/相空间重构/C-C方法/KNN算法Key words
wind power generation/power prediction/chaotic time series/phase space reconstruction/C-C method/KNN algorithm分类
信息技术与安全科学引用本文复制引用
杨茂,贾云彭,穆钢,严干贵,刘佳..基于改进 KNN 算法的风电功率实时预测研究[J].电测与仪表,2014,(24):38-43,6.基金项目
国家重点基础研究发展计划项目（973计划）（2013CB228201）；国家自然科学基金资助项目（51307017）；吉林省科技发展计划项目（20140520129JH）；吉林省教育厅“十二五”科学技术研究项目（吉教科合字［2014］第474号）；吉林市科技发展计划资助项目 （）
