Research of Typical Line Loss Rate in Transformer District Based on Data-Driven Method
暂无分享,去创建一个
Xu Bo | Sun Guoqiang | Wang Jinran | Li Xinran | Liu Ling | Wang Liming | Zhao Yong | Liu Shubo
[1] Haibo He,et al. SOMKE: Kernel Density Estimation Over Data Streams by Sequences of Self-Organizing Maps , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[2] Ouyang Sen,et al. Line-loss rate calculation model considering feeder clustering features for medium-voltage distribution network , 2016 .
[3] Liu Da-wei. Calculation of line losses in distribution systems using artificial neural network aided by immune genetic algorithm , 2009 .
[4] Songyang Lao,et al. Fine-Grained Image Classification With Gaussian Mixture Layer , 2018, IEEE Access.
[5] Zhang Jian-hai. THE CALCULATION OF ENERGY LOSSES IN DISTRIBUTION SYSTEMS BASED ON RBF NETWORK WITH DYNAMIC CLUSTERING ALGORITHM , 2005 .
[6] Ding Xin. A NEW PRACTICAL METHOD FOR CALCULATING LINE LOSS OF DISTRIBUTION NETWORK——IMPROVED ITERATION METHOD , 2000 .
[7] Zhao Li-na. Calculation of Line Losses in Distribution Systems Using Artificial Neural Network Aided by Immune Genetic Algorithm , 2009 .
[8] Xin Kai. AN ADVANCED ALGORITHM BASED ON COMBINATION OF GA WITH BP TO ENERGY LOSS OF DISTRIBUTION SYSTEM , 2002 .
[9] Ping Zhu,et al. Hierarchical Clustering Problems and Analysis of Fuzzy Proximity Relation on Granular Space , 2013, IEEE Transactions on Fuzzy Systems.
[10] Hui Xiong,et al. K-means clustering versus validation measures: a data distribution perspective , 2006, KDD '06.
[11] Guo Zhizhong,et al. DISTRIBUTION SYSTEM THEORETICAL LINE LOSS CALCULATION BASED ON LOAD OBTAINING AND MATCHING POWER FLOW , 2005 .
[12] J. H. Zar,et al. Significance Testing of the Spearman Rank Correlation Coefficient , 1972 .
[13] D. Bonett,et al. Sample size requirements for estimating pearson, kendall and spearman correlations , 2000 .
[14] Korris Fu-Lai Chung,et al. Kernel Density Estimation, Kernel Methods, and Fast Learning in Large Data Sets , 2014, IEEE Transactions on Cybernetics.
[15] Tao Ding,et al. Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network , 2018, IET Generation, Transmission & Distribution.