Model Estimasi Global Tilted Irradiance Menggunakan Principal Component Regression Di Wilayah Sumatera
Abstract
This study aims to develop a model for estimating Global Tilted Irradiance (GTI) in the Sumatra region using the Principal Component Regression (PCR) method. The data used consist of solar radiation and meteorological parameters obtained from the Global Solar Atlas, including Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), Diffuse Irradiance (DIF), air temperature, and regional elevation across 154 districts/cities. The multicollinearity test indicates a high correlation among independent variables, with Variance Inflation Factor (VIF) values > 10 and tolerance values < 0.10, making multiple linear regression unsuitable. Therefore, variable transformation was performed using Principal Component Analysis (PCA) before constructing the regression model. The PCA results show that two principal components can explain 86.9% of the data variance. The resulting PCR model is statistically significant with an R² value of 0.792, indicating that the model can explain 79.2% of the variation in GTI. The findings demonstrate that PCR is effective in addressing multicollinearity and produces a stable GTI estimation model in tropical regions.
Keywords:
Global Tilted Irradiance, Principal Component Regression, Principal Component Analysis, Multicollinearity, Solar Radiation
Abstrak
Penelitian ini bertujuan untuk membangun model estimasi Global Tilted Irradiance (GTI) di wilayah Sumatera menggunakan metode Principal Component Regression (PCR). Data yang digunakan merupakan data radiasi surya dan parameter meteorologi yang diperoleh dari Global Solar Atlas, meliputi Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), Diffuse Irradiance (DIF), temperatur udara, dan elevasi wilayah pada 154 kabupaten/kota. Uji multikolinearitas menunjukkan adanya korelasi tinggi antar variabel independen dengan nilai Variance Inflation Factor (VIF) > 10 dan tolerance < 0,10 sehingga regresi linear berganda tidak layak digunakan. Oleh karena itu, dilakukan transformasi variabel menggunakan Principal Component Analysis (PCA) sebelum membangun model regresi. Hasil PCA menunjukkan dua komponen utama mampu menjelaskan 86,9% variasi data. Model PCR yang dihasilkan signifikan secara statistik dengan nilai R² sebesar 0,792, yang menunjukkan bahwa model mampu menjelaskan 79,2% variasi GTI. Hasil penelitian menunjukkan bahwa PCR efektif dalam mengatasi multikolinearitas dan menghasilkan model estimasi GTI yang stabil di wilayah tropis.
Kata Kunci:
Global Tilted Irradiance; Principal Component Regression; Principal Component Analysis; Multikolinearitas; Radiasi Surya
Keywords
Full Text:
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DOI: https://doi.org/10.38038/vocatech.v8i2.332
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