An Improved Kernel Principal Component Analysis for Large-Scale Data Set

2010 ◽  
pp. 9-16 ◽  
Author(s):  
Weiya Shi ◽  
Dexian Zhang
Keyword(s):  
Principal Component Analysis ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Data Set ◽  
Large Scale Data ◽  
Scale Data

scholarly journals Large‐scale data principal component analysis of phase transition from a‐Si:H to nc‐Si:H using PECVD optical emission spectra

2020 ◽  
Vol 15 (5) ◽  
pp. 323-326
Author(s):  
Li‐Han Kau ◽  
Hung‐Jui Huang ◽  
Hsueh‐Er Chang ◽  
Yu‐Lin Hsieh ◽  
Yiin‐Kuen Fuh ◽  
...  
Keyword(s):  
Phase Transition ◽  
Principal Component Analysis ◽  
Large Scale ◽  
Emission Spectra ◽  
Principal Component ◽  
Optical Emission ◽  
Component Analysis ◽  
Large Scale Data ◽  
Optical Emission Spectra ◽  
Scale Data

scholarly journals Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
E. Zhu ◽  
D. Pi
Keyword(s):  
Power Generation ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Large Scale ◽  
Principal Component ◽  
Component Analysis ◽  
Power Prediction ◽  
Large Scale Data ◽  
Pv Generation ◽  
Scale Data

In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.

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