Publications

Floating-Lidar-System-Generalized Machine Learning model for turbulence intensity correction

April 2026

In this study we present the evolution of the Eolos’ original machine learning (ML) turbulence intensity (TI) correction model. In 2024, Eolos developed a ML model specifically designed to correct TI measurements for the FLS200 buoy, a widely used floating lidar system.

Taming Turbulence: A Comparison between Motion Compensation and Machine Learning for FLS measured TI

June 2025

Floating LiDAR Systems (FLS) are essential for delivering cost-effective and high-quality wind data in the offshore wind industry. While the 10-minute average Horizontal Wind Speed (HWS) measured by FLS compares well with traditional cup or sonic anemometers, significant discrepancies arise when assessing Turbulence Intensity (TI).

The influence of atmospheric stability on floating-lidar-observed turbulence intensity and its correction via machine learning

January 2025

The Floating Lidar Turbulence Intensity Debate: What Is the Industry Willing to Accept to Confront It?

July 2024

A Machine Learning Approach to Correct Turbulence Intensity measured by Floating Lidars

May 2024

In this work we introduce a supervised Machine Learning (ML) model to correct the Turbulence Intensity (TI) measured by Floating LiDAR Systems (FLS) in offshore environment. The model was developed using data from 46 EOLOS-FLS200 validation campaigns (≈4.6 years) carried out at three reference sites in the North Sea.

Multi-dimensional data-based correction algorithm for Turbulence Intensity measured by Floating Lidars

June 2023

Turbulence Intensity: Floating LiDAR measurements assimilation to traditional offshore met mast’s anemometry through an adjustment algorithm

April 2022

In this study a correction of the measured TI from floating lidars is applied for wind resource characterization. Two EOLOS FLS200, mounting a ZX300M continuous wave lidar, are currently measuring wind conditions in the New York bight as part of a wind resource assessment campaign by the New York State Energy Research and Development Authority (NYSERDA).