Multifunctional Microstructured Polymer Films for Boosting Solar Power Generation of Silicon-Based Photovoltaic Modules

2015 ◽  
Vol 7 (4) ◽  
pp. 2349-2358 ◽  
Author(s):  
Jung Woo Leem ◽  
Minkyu Choi ◽  
Jae Su Yu
Keyword(s):  
Power Generation ◽  
Polymer Films ◽  
Solar Power ◽  
Photovoltaic Modules ◽  
Solar Power Generation ◽  
Silicon Based

scholarly journals Artificial inverted compound eye structured polymer films with light-harvesting and self-cleaning functions for encapsulated III–V solar cell applications

RSC Advances ◽  
2015 ◽  
Vol 5 (75) ◽  
pp. 60804-60813 ◽  
Author(s):  
Jung Woo Leem ◽  
Jae Su Yu
Keyword(s):  
Power Generation ◽  
Solar Cells ◽  
Solar Cell ◽  
Polymer Films ◽  
Compound Eye ◽  
Solar Power ◽  
Light Harvesting ◽  
Solar Power Generation ◽  
Self Cleaning

Inverted compound eye structured polydimethylsiloxane (i.e., ICESs PDMS) antireflection layer enhances the solar power generation of encapsulated III–V solar cells.

scholarly journals Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant

2021 ◽  
Vol 11 (4) ◽  
pp. 1776
Author(s):  
Young Seo Kim ◽  
Han Young Joo ◽  
Jae Wook Kim ◽  
So Yun Jeong ◽  
Joo Hyun Moon
Keyword(s):  
Power Generation ◽  
Data Analysis ◽  
Power Plant ◽  
Relative Humidity ◽  
Regression Model ◽  
Regression Models ◽  
Solar Power ◽  
Meteorological Variables ◽  
Solar Power Plant ◽  
Solar Power Generation

This study identified the meteorological variables that significantly impact the power generation of a solar power plant in Samcheonpo, Korea. To this end, multiple regression models were developed to estimate the power generation of the solar power plant with changing weather conditions. The meteorological data for the regression models were the daily data from January 2011 to December 2019. The dependent variable was the daily power generation of the solar power plant in kWh, and the independent variables were the insolation intensity during daylight hours (MJ/m2), daylight time (h), average relative humidity (%), minimum relative humidity (%), and quantity of evaporation (mm). A regression model for the entire data and 12 monthly regression models for the monthly data were constructed using R, a large data analysis software. The 12 monthly regression models estimated the solar power generation better than the entire regression model. The variables with the highest influence on solar power generation were the insolation intensity variables during daylight hours and daylight time.

scholarly journals PV-Solar Power Generation in Educational Institutions

2021 ◽  
Vol 1879 (3) ◽  
pp. 032070
Author(s):  
Fadhil Mahmood Oleiwi ◽  
Naseer K. Kasim ◽  
Ahmed F. Atwan
Keyword(s):  
Power Generation ◽  
Solar Power ◽  
Educational Institutions ◽  
Solar Power Generation
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