中国电机工程学报Issue(19)：3162-3169,8.DOI:10.13334/j.0258-8013.pcsee.2014.19.016
基于模糊粗糙集与改进聚类的神经网络风速预测
Neural Network Wind Speed Prediction Based on Fuzzy Rough Set and Improved Clustering
摘要
Abstract
ABSTRACT:Improving wind power prediction accuracy is an effective means to ensure the safe and stable operation of wind farm and power system. Neural network methods have been widely applied to wind power prediction with satisfactory results, but the training set and the input variables affect its forecasting performance greatly. Based on that, an integrated neural networks approach combining fuzzy rough set and improved clustering was proposed in this paper. Fuzzy rough set was applied to carry on the attribute reduction for a variety of factors affecting wind speed to optimize the model input, and the importance of each attribute for wind speed was obtained. The traditional clustering was improved through the weighted Euclidean distance based on attributes’ importance, and similar data were extracted as the training set to optimize the training set. Matching model was selected to carry out the wind speed prediction. Taking a wind farm in north China as an example, the experimental results show that the method can effectively improve the forecasting accuracy with less model input.关键词
风电场/风速预测/神经网络/模糊粗糙集/属性约简/改进聚类/加权欧氏距离Key words
wind farm/wind speed forecast/neural networks/fuzzy rough set/attribute reduction/improved clustering/weighted Euclidean distance分类
信息技术与安全科学引用本文复制引用
刘兴杰,岑添云,郑文书,米增强..基于模糊粗糙集与改进聚类的神经网络风速预测[J].中国电机工程学报,2014,(19):3162-3169,8.基金项目
国家自然科学基金项目(51277075)；河北省自然科学基金项目(E2012502047)；中央高校基本科研业务费专项资金资助(12ZX19)。Project Supported by National Natural Science Foundation of China (51277075) （51277075）
Natural Science Foundation of Hebei Province, China (E2012502047) （E2012502047）
The Fundamental Research Funds for the Central Universities (12ZX19) （12ZX19）
