改进的并行模糊核聚类算法在电力负荷预测的应用

Application of the improved parallel fuzzy kernel C-means clustering algorithm in power load forecasting

用户用电典型模式的分类预测是电力负荷预测的重要组成部分。针对单核模糊C均值算法在电力大数据挖掘中不能兼顾预测精度和普适性能好的问题,提出了一种电力短期负荷场景中改进的无监督学习多核模糊C均值聚类算法,建立了双层神经网络的电力数据负荷预测模型对比该改进的算法对电力负荷预测效果的影响。用户数据由MapReduce并行化处理加速。数值实验结果表明:改进的算法在实际电力用户数据集中,具有广泛的适用性和有效性,同时能显著提高电力短期负荷预测的精度。

The classification prediction of the typical mode of user consumption is an important part of electric power load forecasting.Aiming at the problem that the single-kernel fuzzy C-means algorithm cannot balance the prediction accuracy and the universal performance in big data mining of electric power,so this paper presents an improved unsupervised learning multi-kernel fuzzy C-clustering algorithm in the short-term power load scenario.A power data load forecasting model of the double-layer neural network is established to compare the effects of the improved algorithm.User data is accelerated by MapReduce parallelization.The numerical experiments show that the improved algorithm has wide applicability and effectiveness in the actual power user data set,and can significantly improve the accuracy of short-term load forecasting.