电力系统自动化2017，Vol.41Issue(3)：13-18,6.DOI:10.7500/AEPS20160504020
计及历史数据熵关联信息挖掘的短期风电功率预测
Short-term Wind Power Prediction Based on Entropy Association Information Mining of Historical Data
摘要
Abstract
The historical association information mining is important for improving the accuracy and computational efficiency of short-term wind power prediction.In order to solve the problem of redundancy in the input and output variables of wind power prediction model,an index of entropy correlation coefficient (ECC) based on information entropy and mutual information is adopted.It is used to quantitatively evaluate the complex non-linear relationship between daily wind power samples of historical data and the equivalent wind speed of the next few days,and is compared with the linear correlation coefficient,rank correlation coefficient and Euclidean distance.Through intimate-samples selection,hidden layer structure optimization and network weights assignment,a modified model of short term wind power prediction is designed to overcome the defect of the redundant degree training samples and slow convergence in traditional neural network training process,and improve the generalization ability and computational efficiency of the forecasting model.The example analysis on the measured data from a wind farm shows that the proposed method has application feasibility in improving performance of short-term wind power prediction.关键词
关联信息挖掘/熵相关系数/相关性冗余/模型泛化能力Key words
association information mining/entropy correlation coefficient (ECC)/correlation redundancy/model generalization ability引用本文复制引用
史坤鹏,乔颖,赵伟,黄松岭,刘志君,郭雷..计及历史数据熵关联信息挖掘的短期风电功率预测[J].电力系统自动化,2017,41(3):13-18,6.基金项目
国家自然科学基金资助项目(51077078) （51077078）
国家科技支撑计划资助项目(2015BAA01B01).This work is supported by National Natural Science Foundation of China (No.51077078) and National Key Technologies R&D Program (No.2015BAA01B01). （2015BAA01B01）
