Model Prediksi Penjadwalan Produksi Energi Terbarukan dengan Algoritma XGBoost dan Analisis Interpretatif Menggunakan SHAP
DOI:
https://doi.org/10.53513/jursi.v4i4.11443Keywords:
Energi terbarukan, Extreme Gradient Boosting, Penjadwalan, Prediksi, SHapley Additive exPlanationsAbstract
Penjadwalan produksi energi terbarukan adalah kegiatan untuk menyeimbangkan antara pasokan dan permintaan energi dalam siklus sistem energi berkelanjutan. Berbagai jenis energi terbarukan seperti hidro, angin, matahari, dan lainnya akan melalui pemodelan prediktif dari jadwal produksi menggunakan algoritma Extreme Gradient Boosting (XGBoost) yang dikombinasikan dengan pendekatan interpretabilitas model menggunakan SHapley Additive exPlanations (SHAP). Penelitian ini menggunakan data sekunder dengan parameter Tahun, Negara, Energi Surya, Energi Angin, Energi Hidro, Energi Terbarukan Lainnya, dan Total Energi Terbarukan. Pemodelan menunjukkan bahwa energi angin dan energi matahari memiliki prediksi produksi yang meningkat ketika nilai fitur tinggi dan energi angin memiliki efek negatif ketika nilai fitur rendah. Penelitian ini memiliki kontribusi yang signifikan terhadap faktor yang mempengaruhi penjadwalan dan juga berpeluang untuk penerapan sistem cerdas dalam pengambilan keputusan sektor energi. Hasil penelitian ini dapat menjadi dasar untuk merumuskan strategi manajemen energi berkelanjutan yang memiliki potensi untuk mengintegrasikan kecerdasan buatan dan transparansi model dalam kebijakan energi terbarukan.References
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