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All Journal Jurnal Rekayasa Elektro Sriwijaya
Handayani, Sita Tri
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Prediksi Iradiasi Matahari Menggunakan Machine Learning untuk Estimasi Output PLTS di Wilayah Malang Humaidi, Haneef Nouval Alannibras; Handayani, Sita Tri; Kasan, Nur; Hakim, Ermanu Azizul; Al Rasyid , Zya Jamaluddin; Ningtias, Dieta Wahyu Asry
Jurnal Rekayasa Elektro Sriwijaya Vol. 7 No. 2 (2026): Jurnal Rekayasa Elektro Sriwijaya
Publisher : Jurusan Teknik Elektro Fakultas Teknik Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36706/hsar2478

Abstract

Accurate solar irradiance prediction is fundamental for planning and operating solar photovoltaic (PV) power systems. This study compares the performance of four machine learning algorithms — Random Forest (RF), Support Vector Regression (SVR), XGBoost, and Artificial Neural Network (ANN) — in predicting daily Global Horizontal Irradiance (GHI) in Malang, East Java. The dataset was obtained from NASA POWER spanning 10 years (2014–2023), comprising 3,646 daily records with 11 input features including meteorological parameters, temporal features, and autoregressive features. Data splitting was performed chronologically (70% training, 15% validation, 15% testing). Results show that XGBoost achieved the best performance with R² = 0.6797, RMSE = 0.5212 kWh/m²/day, and MAPE = 8.35%. Seasonal analysis reveals all models perform better during the dry season (R² = 0.74; MAPE = 6.63%) compared to the wet season (R² = 0.54; MAPE = 11.06%). A 5 kWp PV system in Malang is estimated to produce 7,626 kWh/year.
Co-Authors Al Rasyid , Zya Jamaluddin Ermanu Azizul Hakim Humaidi, Haneef Nouval Alannibras Ningtias, Dieta Wahyu Asry Nur Kasan, Nur