基于PCA-MP-BP的智能电网数据融合方法

Data Fusion for Smart Grid Based on PCA-MP-BP

为了提高对智能电网大数据的挖掘效率,提出了基于PCA-MP-BP的智能电网数据融合方法。首先对智能电网大数据技术和智能电网大数据融合技术进行了分析。采用主成分分析方法(PCA)提取出对预测结果有影响的主要特征值,作为BP神经网络的输入;然后提出了一种MapReduce和BP算法相结合的数据融合算法,该算法加快了数据处理效率;将所提的PCA-MP-BP算法用于智能电网大数据功率预测。实验仿真结果验证了所提方法具有更快的数据处理能力和更高的预测精度。

In order to improve the mining efficiency of big data of smart grid,a data fusion method based on PCA-MR-BP is proposed.Firstly,it analyzes the big data technology and the big data fusion technology of smart grid.Principal component analysis(PCA)is used to extract the main characteristic values that have an impact on the prediction results,which are used as the input of BP neural network.Then,a data fusion algorithm is proposed by combining MapReduce and BP algorithms,which speeds up the efficiency of data processing.The proposed PCA-MP-BP algorithm is applied to power prediction of smart grid.Experimental simulation results show that the proposed method has faster data processing capability and higher prediction accuracy.