A Numerical Simulation Study of a Wind–Rain  Synergy System (WRSS) for Multi–Source  Maritime Renewable Energy Harvesting Using Predictive Dynamic Positioning in Indonesian  Waters

Jogi Ruben Natanael Panggabean; Suaydhi; Aulia Ziani Al–Fazry; Nadhira Fasya Victoria; Ferima Lenawati; Intan Sugiarti; Galuh Pramudya

doi:10.21009/risenologi.111.04

Authors

Jogi Ruben Natanael Panggabean Univesitas Padjajaran
Suaydhi National Research and Innovation Agency
Aulia Ziani Al–Fazry Universitas Padjadjaran
Nadhira Fasya Victoria Universitas Padjadjaran
Ferima Lenawati Universitas Padjadjaran
Intan Sugiarti Universitas Padjadjaran
Galuh Pramudya Universitas Padjadjaran

DOI:

https://doi.org/10.21009/risenologi.111.04

Keywords:

Dynamic positioning, Offshore renewable energy, Wind-rain synergy

Abstract

The maritime industry faces pressure to achieve net–zero emissions by 2050, with offshore renewable energy systems constrained by single–source dependencies and static positioning. This study proposes the Wind–Rain Synergy System (WRSS), a dynamically positioned floating platform that simultaneously harvests wind energy and rainwater via predictive atmospheric analysis. Three integrated novelties define the system: (1) multi–source harvesting combining wind turbines with triboelectric rain collectors, (2) a predictive positioning algorithm using GPM IMERG (0.1° × 0.1°, half–hourly) and ERA5 data, and (3) autonomous dynamic positioning within a 2–km radius. Machine learning ensemble models trained on 26 years of Indonesian maritime meteorological data predict optimal harvesting positions 6–12 hours ahead. Ten–year simulations demonstrate 45–60% higher energy output than conventional fixed–position platforms, with up to 200 W/m² during atmospheric river events at wind speeds of 12–18 m/s and rainfall of 5–15 mm/h. Prediction accuracy reached 87%. Economic analysis shows 25–35% LCOE reduction and an 18–month shorter payback period versus traditional offshore wind installations. WRSS offers a viable pathway for adaptive multi–source offshore energy generation in tropical maritime regions.

ABSTRAK

Industri maritim menghadapi tekanan untuk mencapai emisi nol bersih pada 2050, dengan sistem energi terbarukan lepas pantai yang terbatas oleh ketergantungan sumber tunggal dan positioning statis. Penelitian ini menyajikan Wind–Rain Synergy System (WRSS), platform terapung berposisi dinamis yang memanen energi angin dan air hujan secara bersamaan melalui analisis atmosfer prediktif. Tiga komponen utama mendefinisikan novelti sistem: (1) pemanenan energi multi–sumber menggabungkan turbin angin dengan kolektor hujan triboelektrik, (2) algoritma positioning prediktif menggunakan data GPM IMERG (0,1° × 0,1°, setengah jam) dan ERA5, serta (3) positioning dinamis otonom dalam radius 2 km. Model ensemble machine learning dilatih pada 26 tahun data meteorologi maritim Indonesia untuk memprediksi posisi panen optimal 6–12 jam ke depan. Simulasi 10 tahun menunjukkan output energi 45–60% lebih tinggi dari platform posisi tetap konvensional, dengan hingga 200 W/m² saat peristiwa atmospheric river pada kecepatan angin 12–18 m/s dan curah hujan 5–15 mm/h. Akurasi prediksi mencapai 87%. Analisis ekonomi menunjukkan pengurangan LCOE 25–35% dan periode pengembalian modal 18 bulan lebih singkat. WRSS menawarkan jalur yang layak untuk pembangkitan energi lepas pantai adaptif multi–sumber di kawasan maritim tropis.

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