Determining offshore wind installation times using machine learning and open data

09/25/2019
by   Bo Tranberg, et al.
0

The installation process of offshore wind turbines requires the use of expensive jack-up vessels. These vessels regularly report their position via the Automatic Identification System (AIS). This paper introduces a novel approach of applying machine learning to AIS data from jack-up vessels. We apply the new method to 13 offshore wind farms in Danish, German and British waters. For each of the wind farms we identify individual turbine locations, individual installation times, time in transit and time in harbor for the respective vessel. This is done in an automated way exclusively using AIS data with no prior knowledge of turbine locations, thus enabling a detailed description of the entire installation process.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/10/2021

Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform

Detection of thunderstorms is important to the wind hazard community to ...
research
06/26/2023

Robust Wind Turbine Blade Segmentation from RGB Images in the Wild

With the relentless growth of the wind industry, there is an imperious n...
research
01/21/2019

Predicting wind pressures around circular cylinders using machine learning techniques

Numerous studies have been carried out to measure wind pressures around ...
research
01/15/2019

A forgotten Theorem of Schoenberg on one-sided integral averages

Let f:R→R be a function for which we want to take local averages. Assumi...
research
11/23/2020

Use of Computer Applications for Determining the Best Possible Runway Orientation using Wind Rose Diagrams

As technology advances, there are more uses of computer applications in ...
research
05/01/2018

Realistic Multimedia Tools based on Physical Models: II. The Binary 3D Renderer (B3dR)

The present article reports on the second tool of a custom-built toolkit...
research
10/12/2015

Data structuring for the ontological modelling of wind energy systems

Small wind projects encounter difficulties to be efficiently deployed, p...

Please sign up or login with your details

Forgot password? Click here to reset