J. Jee, Il-Sung Zo, Kyu-Tae Lee, Won-Hak Lee, Sung-Jin Choi
{"title":"Analysis of Photovoltaic Panel Temperature and Photovoltaic Electric Power at Chuncheon Meteorological Station using Intensive Observation Period Data","authors":"J. Jee, Il-Sung Zo, Kyu-Tae Lee, Won-Hak Lee, Sung-Jin Choi","doi":"10.7836/kses.2022.42.2.023","DOIUrl":null,"url":null,"abstract":"In this study, photovoltaic (PV) electricity power and PV panel temperature for operation and monitoring of PV power plant were calculated and analyzed. A PV panel temperature sensor was installed at the Chuncheon Meteorological Observatory solar power plant for intensive observation from May 1 to August 19, 2018. When the calculated PV panel temperature was analyzed using the measured PV panel temperature, the calculated PV panel temperature was overestimated at a higher temperature compared to the measured PV panel temperature, which was overestimated at a lower temperature; however, the determination coefficient (R 2 ) was 0.88 or more. The bias was -0.33°C and RMSE was 3.43°C when the ground observation data were used. However, when the Local Data Assimilation and Prediction (LDAPS) model were used, the bias was 0.22°C and RMSE was 4.27°C. The PV electricity power generation by ground meteorological observation data (Korea Meteorological Administration, KMA), LDAPS model prediction data (LDAPS), and Communication Ocean and Meteorological Satellite (COMS) data using the PV module temperature were compared with those of the Chuncheon PV power plant. The determination coefficient (R 2 ) of PV power generation was the highest for KMA (0.91) followed by COMS (0.88) and LDAPS (0.84). The slope of the linear regression, (1.05) for KMA, and the smallest bias (2.24 kWh) and RMSE (3.38 kWh) were similar to the measured values. However, compared to the LDAPS, the slope (1.23) of the linear regression was the largest in COMS, and the bias (4.77 kWh) and RMSE (6.23 kWh) were slightly higher.","PeriodicalId":276437,"journal":{"name":"Journal of the Korean Solar Energy Society","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Korean Solar Energy Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7836/kses.2022.42.2.023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this study, photovoltaic (PV) electricity power and PV panel temperature for operation and monitoring of PV power plant were calculated and analyzed. A PV panel temperature sensor was installed at the Chuncheon Meteorological Observatory solar power plant for intensive observation from May 1 to August 19, 2018. When the calculated PV panel temperature was analyzed using the measured PV panel temperature, the calculated PV panel temperature was overestimated at a higher temperature compared to the measured PV panel temperature, which was overestimated at a lower temperature; however, the determination coefficient (R 2 ) was 0.88 or more. The bias was -0.33°C and RMSE was 3.43°C when the ground observation data were used. However, when the Local Data Assimilation and Prediction (LDAPS) model were used, the bias was 0.22°C and RMSE was 4.27°C. The PV electricity power generation by ground meteorological observation data (Korea Meteorological Administration, KMA), LDAPS model prediction data (LDAPS), and Communication Ocean and Meteorological Satellite (COMS) data using the PV module temperature were compared with those of the Chuncheon PV power plant. The determination coefficient (R 2 ) of PV power generation was the highest for KMA (0.91) followed by COMS (0.88) and LDAPS (0.84). The slope of the linear regression, (1.05) for KMA, and the smallest bias (2.24 kWh) and RMSE (3.38 kWh) were similar to the measured values. However, compared to the LDAPS, the slope (1.23) of the linear regression was the largest in COMS, and the bias (4.77 kWh) and RMSE (6.23 kWh) were slightly higher.