基于改进密度峰值聚类的超短期工业负荷预测

密度峰值聚类的收敛速度较快且无需人工设置最佳聚类数,更具备高鲁棒性特点,可以在工业负荷预测中进行用户用电行为的模式识别与分类,然后进行预测,具有较高的实际应用价值。但是该算法在小样本条件下聚类效果不佳,容易“遗漏”样本中的聚类中心。针对这种情况,进行类间距离优化和类内距离优化,使待聚类数据更容易被分类,对用户用电行为进行深入地挖掘分类,再使用灰色关联度确定待预测日所属类簇,使用GRNN神经网络进行负荷预测。通过Matlab仿真,可以得出结论,文中方法可以有效提高工业用户用电负荷数据的聚类效果,并提高负荷预测的精度。

Ultra-short term industrial load prediction based on improved density peak clustering

Density peak clustering has the advantages of fast convergence,strong robustness,and there is no need to manually set the optimal clustering number,etc.It can be used for pattern recognition and classification of user electricity behavior in industrial load forecasting,and then,makes prediction,which has high practical application value.However,this algorithm has a poor clustering effect under the condition of small samples,which is easy to"miss"the clustering center in the sample.In view of this situation,inter-class optimization and intra-class optimization are carried out for the density peak clustering to enhance the data separability and conduct in-depth mining and classification of electricity consumption behaviors of users.Then,gray relational degree is used to determine the category to be predicted,and GRNN neural network is used for load prediction.Through Matlab simulation,it can be concluded that the method proposed in this paper can effectively improve the clustering effect of power load data of industrial users and improve the accuracy of load prediction.