Determining offshore wind installation times using machine learning and open data
This provides a cost-effective solution for the offshore wind industry by enabling detailed process analysis without prior knowledge, though it is incremental in applying existing ML techniques to a new domain.
The paper tackled the problem of determining offshore wind turbine installation times by applying machine learning to AIS data from jack-up vessels, achieving automated identification of turbine locations and installation times for 13 wind farms in Danish, German, and British waters.
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.