计及历史数据熵关联信息挖掘的短期风电功率预测

对风电功率历史数据进行关联信息挖掘,将有助于提高短期风电功率预测的准确度和计算效率。为解决风电功率预测模型的输入、输出变量的相关性冗余问题,尝试采用了一种基于信息熵和互信息的熵相关系数指标,旨在量化评估不同历史日风电样本与待预测日参考样本间的复杂非线性映射关系,并与线性相关系数、秩相关系数、欧氏距离指标进行了对比研究。同时,设计了一种BP神经网络改进模型,通过亲密样本筛选、隐含层结构寻优、网络权重赋初值等环节,克服了传统预测模型的训练数据冗余度大、收敛速度慢问题,提高了预测模型的泛化能力和计算效率。对某风电场实测数据的算例分析表明,所提出的方法在改善短期风电功率预测性能方面具有应用可行性。

Short-term Wind Power Prediction Based on Entropy Association Information Mining of Historical Data

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.