Abstract

The most critical concerns during offshore wind turbine installation are wind conditions. Wind can cause safety issues and long periods of waiting, resulting in project delays and massive costs. The offshore wind energy industry relies on weather forecast reports, but the reports are frequently found to be inconsistent, particularly in wind speed.The decision to perform installation work is then difficult [1-15]. The current paper proposes a solution to analyse the similarity degree of the two reports, as well as predict the next opening of the weather window and its periodicity, by utilising the concept of time lag in Cross-Correlation and Pearson Correlation Coefficient. Not only must the reports be aligned, but wind types must also be precisely judged. The paper presents a new point of view .The most critical concerns during offshore wind turbine installation are wind conditions. Wind can cause safety issues and long periods of waiting, resulting in project delays and massive costs. The offshore wind energy industry relies on weather forecast reports, but the reports are frequently found to be inconsistent, particularly in wind speed. The decision to perform installation work is then difficult. The current paper proposes a solution to analyse the similarity degree of the two reports, as well as predict the next opening of the weather window and its periodicity, by utilising the concept of time lag in Cross-Correlation and Pearson Correlation Coefficient. Not only must the reports be aligned, but wind types must also be precisely judged. The paper presents a new point of view.