Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant
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 meteor...
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MDPI AG
2021-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/4/1776 |
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| author | Young Seo Kim Han Young Joo Jae Wook Kim So Yun Jeong Joo Hyun Moon |
| author_facet | Young Seo Kim Han Young Joo Jae Wook Kim So Yun Jeong Joo Hyun Moon |
| author_sort | Young Seo Kim |
| collection | DOAJ |
| description | 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/m<sup>2</sup>), 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. |
| first_indexed | 2024-03-09T00:48:33Z |
| format | Article |
| id | doaj.art-5c8cba8b575a4bfa95841bd007486fb4 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T00:48:33Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj.art-5c8cba8b575a4bfa95841bd007486fb42023-12-11T17:22:05ZengMDPI AGApplied Sciences2076-34172021-02-01114177610.3390/app11041776Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power PlantYoung Seo Kim0Han Young Joo1Jae Wook Kim2So Yun Jeong3Joo Hyun Moon4Department of Energy Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan 31116, Chungnam, KoreaDepartment of Energy Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan 31116, Chungnam, KoreaDepartment of Energy Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan 31116, Chungnam, KoreaDepartment of Energy Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan 31116, Chungnam, KoreaDepartment of Energy Engineering, Dankook University, 119, Dandae-ro, Dongnam-gu, Cheonan 31116, Chungnam, KoreaThis 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/m<sup>2</sup>), 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.https://www.mdpi.com/2076-3417/11/4/1776solar power plantSamcheonpometeorological databig dataregression model |
| spellingShingle | Young Seo Kim Han Young Joo Jae Wook Kim So Yun Jeong Joo Hyun Moon Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant Applied Sciences solar power plant Samcheonpo meteorological data big data regression model |
| title | Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant |
| title_full | Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant |
| title_fullStr | Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant |
| title_full_unstemmed | Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant |
| title_short | Use of a Big Data Analysis in Regression of Solar Power Generation on Meteorological Variables for a Korean Solar Power Plant |
| title_sort | use of a big data analysis in regression of solar power generation on meteorological variables for a korean solar power plant |
| topic | solar power plant Samcheonpo meteorological data big data regression model |
| url | https://www.mdpi.com/2076-3417/11/4/1776 |
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