International Journal of Research and Innovation in Applied Science (IJRIAS)
The Gaussian-Enhanced Rayleigh Distribution (GERD): A Hybrid Model for Wind Speed and Power Output Estimation in Tokyo
Published May 25, 2026 • Vol. 11, Issue 5, pp. 409–417Open Access
DOI: 10.51584/IJRIAS.2026.11050035
Abstract
In this paper, we came up with the Gaussian-Enhanced Rayleigh Distribution (GERD), a mix of Rayleigh and Gaussian parts, to see if it could do a better job with wind speed data. For testing, we used monthly records from Tokyo between 2000 and 2020. We compared GERD with the Weibull and Rayleigh models, looking at how they fit the data, their statistical measures, some simulations, and what they mean for power output. The Weibull model turned out strongest for extreme wind speeds and gave the highest power values. Rayleigh came out too low. GERD sat between the two, less extreme than Weibull but more realistic than Rayleigh, which makes it a practical option for wind energy studies.
Keywords: Wind Speed Modeling, Rayleigh Distribution, Weibull Distribution, Gaussian-Enhanced Rayleigh Distribution (GERD)
| Journal | International Journal of Research and Innovation in Applied Science (IJRIAS) |
|---|---|
| ISSN | 2454-6194 |
| Volume / Issue | Volume 11, Issue 5 |
| Pages | 409–417 |
| Publication date | May 25, 2026 |
| DOI | 10.51584/IJRIAS.2026.11050035 |
| Publisher | RSIS International |
| License | Open Access |
How to cite this article
Flowery Francis, & Jeena Joseph (2026). The Gaussian-Enhanced Rayleigh Distribution (GERD): A Hybrid Model for Wind Speed and Power Output Estimation in Tokyo. International Journal of Research and Innovation in Applied Science (IJRIAS), 11(5), 409-417. https://doi.org/10.51584/IJRIAS.2026.11050035
BibTeX
@article{Flowery2026,
title = {The Gaussian-Enhanced Rayleigh Distribution (GERD): A Hybrid Model for Wind Speed and Power Output Estimation in Tokyo},
author = {Flowery Francis and Jeena Joseph},
journal = {International Journal of Research and Innovation in Applied Science (IJRIAS)},
volume = {11},
number = {5},
pages = {409--417},
year = {2026},
doi = {10.51584/IJRIAS.2026.11050035},
publisher = {RSIS International}
}