Perhitungan Intensitas Radiasi Matahari Berdasarkan Pola Sebaran Awan Menggunakan Metode Support Vector Regression (svr)

Abstract

Abstrak Intensitas radiasi matahari yang diterima oleh permukaan bumi dapat diketahui melalui lintasan matahari. Tingkat intensitas radiasi matahari dipengaruhi oleh banyak faktor, yang terpenting adalah posisi, pola, serta sebaran awan. Penelitian ini menganalisis hubungan antara awan dengan intensitas radiasi matahari menggunakan metode Support Vector Regression (SVR). Data awan diperoleh dari METARs dan data intesitas radiasi matahari dari PySolar dan University of Oregon. Hasil perhitungan model menunjukan nilai koefisien determinasi (R²) yang dihasilkan oleh model perhitungan adalah sebesar 0,80022, dimana model mampu menghitung nilai global solar pada kondisi clear sky dan cloudy sky dengan nilai persentase error dinyatakan dalam NMBE sebesar 10,38 %, serta CVRMSE sebesar 21,03%. Data hasil penelitian ini dapat diperlukan untuk membuat desain bangunan agar didapat kondisi termal yang baik.

Kata kunci: machine learning, intensitas radiasi matahari, awan, support vector regression (SVR)

Abstract The intensity of solar radiation received by the surface of the earth can be known through the path of the sun. The level of radiation intensity is influenced by many factors, the most important is the potition, pattern, and distributon of clouds. This research analyzes the relationship between clouds and the intensity of solar radiation using the Support Vector Regression (SVR) method. Cloud data were obtained from METARs and solar radiation intensity data from PySolar and the University of Oregon. The model calculation results show the coefficient of determination (R²) generated by the calculation model is 0.80022, where the model is able to calculate the global solar value in clear sky and cloudy sky conditions with the percentage error value expressed in NMBE of 10.38%, and CVRMSE of 21.03%. The data from the results of this study are needed to create a building design to obtain good thermal conditions.

Keywords: machine learning, radiation intensity, cloud, support vector regression (SVR)