ANALISIS PREDIKSI RADIASI MATAHARI DENGAN ALGORITMA MACHINE LEARNING DAN IMPLEMENTASI BAYESIAN OPTIMIZATION DI PROVINSI DKI JAKARTA
DOI:
https://doi.org/10.36595/misi.v8i1.1338Keywords:
Radiasi matahari harian, prediksi radiasi matahari, SVR, XGboost, multivariate time seriesAbstract
Peningkatan populasi menyebabkan peningkatan permintaan energi. Hingga saat ini, masalah terkait energi adalah sumber daya yang terbatas. Energi alternatif terbarukan dapat dimanfaatkan secara optimal di masa depan. Salah satu sumber energi terbarukan adalah energi matahari karena jumlahnya melebihi kebutuhan energi saat ini dan masa depan. Hal ini sejalan dengan target 7.2 dalam Sustainable Development Goals (SDGs) 2030, yaitu meningkatkan porsi energi terbarukan secara signifikan dalam bauran energi global. Indonesia memiliki potensi energi matahari melalui radiasi matahari. Namun, pemanfaatan potensi energi surya sebagai pembangkit listrik di Provinsi DKI Jakarta belum optimal. Penelitian ini bertujuan untuk memprediksi nilai radiasi matahari melalui Global Horizontal Irradiance (GHI) harian di DKI Jakarta menggunakan Support Vector Regression (SVR) dengan Bayesian Optimization dan membandingkannya dengan XGBoost untuk menemukan model terbaik dari hasil prediksi. Metode BO-SVR terbukti memberikan hasil prediksi yang baik dan kuat pada data yang digunakan karena MAPE dan RMSE untuk data pengujian masing-masing adalah 0,182 dan 34,412. Penerapan Bayesian Optimization dalam menentukan hiperparameter optimal dalam membentuk model prediksi telah terbukti meningkatkan kinerja model. Penelitian ini menghasilkan prediksi radiasi matahari yang memberikan informasi bagi pemerintah, khususnya PT Perusahaan Listrik Negara (PLN) dan peneliti terkait karakteristik radiasi matahari.
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