# Statistical distributions for assessment of potential offshore wind speed in Indonesia
> Syafaruddin

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_105050544210
Jurnal / Konferensi: Clean Energy Science and Technology
Tahun terbit: 2026
DOI: https://doi.org/10.18686/cest837
ISSN: 29724910
Kuartil SJR: Q2
Citations: 0

## Authors
- Syafaruddin

## Abstract
Despite Indonesia’s vast offshore wind potential of 155 GW, current utilization remains low at 0.15 GW. This study evaluates wind speed distributions and cumulative energy production at three strategic locations: Jeneponto, Banda Aceh, and Lombok. Using Weibull, Nakagami, and Rayleigh probability density functions, the research assesses model accuracy through R2 and RMSE metrics. The results show that the parameter values of k and c are 2.9239 and 8.1043, respectively; m and Ω are 2.3117 and 59.4929, respectively, and σ is 5.454 at Jeneponto. In comparison, Banda Aceh has the parameter values of k and c are 2.5520 and 5.3175, respectively; m and Ω are 1.6970 and 26.2341, respectively, and σ is 3.622. Meanwhile, Lombok provides the parameter values of k and c are 2.9755 and 6.7810, respectively; m and Ω are 2.2311 and 41.5548, respectively, and σ is 4.558. Results indicate that the Weibull distribution provides the most accurate fit for Jeneponto (R2 = 0.997, RMSE = 0.0177) and Lombok (R2 = 0.996), while the Rayleigh distribution performs best in Banda Aceh (R2 = 0.993). Comparative analysis of cumulative energy production confirms the Weibull distribution as the superior model for Indonesian offshore wind assessment, yielding the lowest error deviations (0.028–0.087). These findings provide a robust statistical framework for optimizing future offshore wind farm developments in the region. In addition, it helps provide the empirical foundation necessary to integrate systematic offshore wind observations into Indonesia’s long-term energy transition roadmap.

## Keywords
- Weibull distribution
- Offshore wind power
- Rayleigh distribution
- Wind speed
- Wind power
- Cumulative distribution function
- Environmental science
- Submarine pipeline
- Empirical distribution function
- Meteorology
- Mean squared error
- Statistics
- Probability distribution
- Probability density function
- Distribution (mathematics)
- Production (economics)
- Renewable energy
- Standard deviation
- Statistical analysis
- Significant wave height
- Statistical model
- Energy (signal processing)
- Shape parameter
- Gamma distribution
- Mathematics

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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