scholarly journals Dimension Reduction Method for Probabilistic Power Flow Computation Considering Wind Farms

2020 ◽  
Vol 1585 ◽  
pp. 012041
Author(s):  
Haoyan Fu ◽  
Lianghong Wu ◽  
Hongqiang Zhang ◽  
Zhenzhu Liu ◽  
Jingzhe Li
Keyword(s):  
Dimension Reduction ◽  
Power Flow ◽  
Reduction Method ◽  
Wind Farms ◽  
Dimension Reduction Method ◽  
Flow Computation ◽  
Probabilistic Power Flow

A multivariate dimension-reduction method for probabilistic power flow calculation

2016 ◽  
Vol 141 ◽  
pp. 272-280 ◽  
Author(s):  
Wei Wu ◽  
Keyou Wang ◽  
Guojie Li ◽  
Xiuchen Jiang ◽  
Lin Feng ◽  
...  
Keyword(s):  
Dimension Reduction ◽  
Power Flow ◽  
Reduction Method ◽  
Dimension Reduction Method ◽  
Power Flow Calculation ◽  
Flow Calculation ◽  
Probabilistic Power Flow

Investigating Univariate Dimension Reduction Model for Probabilistic Power Flow Computation

2019 ◽  
Vol 47 (6-7) ◽  
pp. 561-572 ◽  
Author(s):  
Qing Xiao ◽  
Shaowu Zhou ◽  
Lianghong Wu ◽  
Yanming Zhao ◽  
You Zhou
Keyword(s):  
Dimension Reduction ◽  
Power Flow ◽  
Reduction Model ◽  
Flow Computation ◽  
Probabilistic Power Flow

scholarly journals Multidimensional Scaling-Based Data Dimension Reduction Method for Application in Short-Term Traffic Flow Prediction for Urban Road Network

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yi Zhao ◽  
Satish V. Ukkusuri ◽  
Jian Lu
Keyword(s):  
Dimension Reduction ◽  
Traffic Flow ◽  
Reduction Method ◽  
Prediction Models ◽  
Urban Traffic ◽  
Dimension Reduction Method ◽  
Short Term ◽  
Urban Road ◽  
Traffic Flow Prediction ◽  
Flow Prediction

This study develops a multidimensional scaling- (MDS-) based data dimension reduction method. The method is applied to short-term traffic flow prediction in urban road networks. The data dimension reduction method can be divided into three steps. The first is data selection based on qualitative analysis, the second is data grouping using the MDS method, and the last is data dimension reduction based on a correlation coefficient. Backpropagation neural network (BPNN) and multiple linear regression (MLR) models are employed in four kinds of urban traffic environments to test whether the proposed method improves the prediction accuracy of traffic flow. The results show that prediction models using traffic data after dimension reduction outperform the same prediction models using other datasets. The proposed method provides an alternative to existing models for urban traffic prediction.

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