Perhitungan Intensitas Radiasi Matahari Berdasarkan Pola Sebaran Awan Menggunakan Metode Support Vector Regression (svr) - Ventiano Ventiano; Ery Djunaedy; Amaliyah Rohsari Indah Utami (eProceedings of Engineering, 2019)
Abstrak Intensitas radiasi matahari yang diterima oleh permukaan bumi dapat diketahui melalui lintasanmatahari. Tingkat intensitas radiasi matahari dipengaruhi oleh banyak faktor, yang terpenting adalahposisi, pola, serta sebaran awan. Penelitian ini menganalisis hubungan antara awan dengan intensitasradiasi matahari menggunakan metode Support Vector Regression (SVR). Data awan diperoleh dariMETARs dan data intesitas radiasi matahari dari PySolar dan University of Oregon. Hasil perhitunganmodel menunjukan nilai koefisien determinasi (RÂ²) yang dihasilkan oleh model perhitungan adalahsebesar 0,80022, dimana model mampu menghitung nilai global solar pada kondisi clear sky dan cloudysky dengan nilai persentase error dinyatakan dalam NMBE sebesar 10,38 %, serta CVRMSE sebesar21,03%. Data hasil penelitian ini dapat diperlukan untuk membuat desain bangunan agar didapat kondisitermal yang baik.Kata kunci: machine learning, intensitas radiasi matahari, awan, support vector regression (SVR)AbstractThe intensity of solar radiation received by the surface of the earth can be known through the path of thesun. 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 theintensity of solar radiation using the Support Vector Regression (SVR) method. Cloud data were obtainedfrom METARs and solar radiation intensity data from PySolar and the University of Oregon. The modelcalculation results show the coefficient of determination (RÂ²) generated by the calculation model is0.80022, where the model is able to calculate the global solar value in clear sky and cloudy sky conditionswith the percentage error value expressed in NMBE of 10.38%, and CVRMSE of 21.03%. The data fromthe 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)