PENGARUH VARIASI BAYANGAN TERHADAP DAYA
KELUARAN PHOTOVOLTAIC JENIS POLYCRISTALLINE
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Institusion
Institut Teknologi Perusahaan Listrik Negara
Author
RISKI ARSYAD, JAYATI
Makkulau, Andi
Makkulau, Andi
Subject
Teknik Elektro
Datestamp
2022-11-07 04:00:10
Abstract :
Future energy sources with the most potential include solar energy, which is also one of the most environmentally beneficial forms of energy. Solar cell modules enable the direct conversion of solar energy into electrical energy. Solar panels' production will, however, drop if they are in the shadows. As a result, this study was carried out to ascertain the impact of shadow changes on the output power of polycrystalline solar panels. The shadow variables given were 0%, 25%, 50%, and 75%, with a tilt angle of 36.7. The outcomes of linear regression analysis performed using Excel The calculated R value is 0.99, indicating a very strong correlation, and the R square value is 0.9978, or 99.78%, indicating that shadow variations account for the remaining 0.22% of the reduction in value. The regression's standard error is 0.00333 the lower the number, the more accurate the regression. The standard error number determined from the Excel software in direct study and simulated testing is 0.00562.
Future energy sources with the most potential include solar energy, which is also one of the most environmentally beneficial forms of energy. Solar cell modules enable the direct conversion of solar energy into electrical energy. Solar panels' production will, however, drop if they are in the shadows. As a result, this study was carried out to ascertain the impact of shadow changes on the output power of polycrystalline solar panels. The shadow variables given were 0%, 25%, 50%, and 75%, with a tilt angle of 36.7. The outcomes of linear regression analysis performed using Excel The calculated R value is 0.99, indicating a very strong correlation, and the R square value is 0.9978, or 99.78%, indicating that shadow variations account for the remaining 0.22% of the reduction in value. The regression's standard error is 0.00333 the lower the number, the more accurate the regression. The standard error number determined from the Excel software in direct study and simulated testing is 0.00562.
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