摘要
密度峰值聚类算法具有收敛速度快、鲁棒性强、无需人为确定最佳聚类数等特点,具备较好的应用前景。为提高光伏功率预测的精度,提出一种将密度峰值聚类算法应用于短期光伏功率预测的方法,并进行了必要优化。该方法首先通过类间距离优化增强气象数据的可分性;然后利用密度峰值聚类对其进行无标签归类,通过灰色关联度匹配出与待预测日相关度最高的类别;最后将其作为Elman神经网络的训练样本,得到预测结果。Matlab仿真结果表明,该方法能够明显提高气象数据的聚类效果,并有效提高光伏功率的短期预测精度。
The density peak clustering algorithm has the characteristics of fast convergence speed,strong robustness,and no need to manually determine the optimal clustering numbers,which has a good application prospect.Therefore,a method of applying the density peak clustering algorithm to short-term photovoltaic power prediction is proposed and necessarily optimized to improve the accuracy of photovoltaic power prediction.In the method,the separability of meteorological data is enhanced by means of inter-class distance optimization.The density peak clustering is adopted to classify the meteorological data without using labels.The category that has the highest degree of correlation with to-be predicted days is matched by using the grey correlation degree,and finally taken as the training sample of the Elman neural network to obtain the prediction results.The Matlab simulation results show that the method can significantly improve the clustering effect of meteorological data and short-term pre-diction accuracy of photovoltaic power.
作者
王帅
杜欣慧
姚宏民
WANG Shuai;DU Xinhui;YAO Hongmin(School of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《现代电子技术》
北大核心
2018年第20期141-145,149,共6页
Modern Electronics Technique
关键词
密度峰值聚类
光伏发电
灰色关联度
相似日匹配
ELMAN神经网络
短期功率预测
density peak clustering
photovoltaic power generation
grey correlation degree
similar day matching
Elman neural network
short.term power prediction
