ESTIMASI DAYA KELUARAN PLTS MINI BERDASARKAN INTENSITAS RADIASI MATAHARI MENGGUNAKAN REGRESI LINIER SEDERHANA

This study aims to estimate the power generated by a mini solar power plant (PLTS) using a linear regression method based on solar radiation intensity. Measurements from a 1,200 Wp mini PLTS throughout December 2025, recorded at 10-minute intervals, yielded 1,297 data points for the study. These data were divided into 1,037 training samples and 260 testing samples, maintaining an 80:20 ratio. Solar radiation intensity served as the independent variable (predictor), while PLTS output power served as the dependent variable (target). The linear regression model, constructed using the Ordinary Least Squares (OLS) method, yielded the equation . Model evaluation on the test data yielded an MAE of 5.997, an MSE of 98.682, an RMSE of 9.934, and a coefficient of determination (R²) of 0.9970. The results demonstrate that the linear regression method can effectively estimate mini PLTS output power based on solar radiation intensity. The resulting model can serve as a foundation for developing a system to estimate and manage PLTS power according to load requirements.

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Publication Details

Journal
JURNAL TELISKA
Published
2026-11-11
DOI
https://doi.org/10.36257/teliska.v19i3.12659
Primary Topic
Solar Radiation and Photovoltaics
Type
article
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article

ESTIMASI DAYA KELUARAN PLTS MINI BERDASARKAN INTENSITAS RADIASI MATAHARI MENGGUNAKAN REGRESI LINIER SEDERHANA

Pola Risma, Yurni Oktarina, Agum Gumelar Aria Putra
JURNAL TELISKA
Solar Radiation and Photovoltaics
article

ESTIMASI DAYA KELUARAN PLTS MINI BERDASARKAN INTENSITAS RADIASI MATAHARI MENGGUNAKAN REGRESI LINIER SEDERHANA

Pola Risma, Yurni Oktarina, Agum Gumelar Aria Putra
article en

Abstract

This study aims to estimate the power generated by a mini solar power plant (PLTS) using a linear regression method based on solar radiation intensity. Measurements from a 1,200 Wp mini PLTS throughout December 2025, recorded at 10-minute intervals, yielded 1,297 data points for the study. These data were divided into 1,037 training samples and 260 testing samples, maintaining an 80:20 ratio. Solar radiation intensity served as the independent variable (predictor), while PLTS output power served as the dependent variable (target). The linear regression model, constructed using the Ordinary Least Squares (OLS) method, yielded the equation . Model evaluation on the test data yielded an MAE of 5.997, an MSE of 98.682, an RMSE of 9.934, and a coefficient of determination (R²) of 0.9970. The results demonstrate that the linear regression method can effectively estimate mini PLTS output power based on solar radiation intensity. The resulting model can serve as a foundation for developing a system to estimate and manage PLTS power according to load requirements.

JURNAL TELISKAVol. 19(3)
Politeknik Negeri Sriwijaya (ID)
Solar Radiation and Photovoltaics
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