李钢,杜欣慧,裴玥瑶,刘浩洋.基于改进密度峰值聚类的超短期工业负荷预测[J].电测与仪表,2021,58(5):159-163. LI Gang,DU Xinhui,PEI Yueyao,LIU Haoyang.Ultra-short term industrial load prediction based on improved density peak clustering[J].Electrical Measurement & Instrumentation,2021,58(5):159-163.
基于改进密度峰值聚类的超短期工业负荷预测
Ultra-short term industrial load prediction based on improved density peak clustering
Density peak clustering has the advantages of strong robustness,fast convergence,no need to 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 make prediction,which has high practical application value. However,this algorithm has a poor clustering effect under the condition of small samples, the classification division is not obvious, and there are a lot of noise points. In view of this situation, inter-class optimization and intra-class optimization were carried out for the density peak clustering to enhance the data separability and conduct in-depth mining and classification of users" electricity consumption behaviors. Then,gray relational degree was used to determine the category to be predicted, and GRNN neural network was used for load prediction. Through Matlab simulation,it can be concluded that the method in this paper can effectively improve the clustering effect of power load data of industrial users and improve the accuracy of load prediction.
