Classification and Evaluation of Wind Energy Resources in Offshore Wind Farms Based on Cluster Analysis

As an important clean energy, the classification and evaluation of offshore wind power resources have attracted increasing attention. However, the current evaluation methods for offshore wind farms are diverse, and lacking in systematicness and accuracy. This paper aims to systematically classify and evaluate offshore wind farms through cluster analysis. First, historical meteorological data of a certain sea area, including wind direction, temperature, and other information, are collected, and then the K-means clustering algorithm is used to divide wind energy into multiple categories. On this basis, the data of various types of wind energy resources are further calculated to obtain detailed characteristic data of different wind energy resource categories. The test finds that cluster analysis divides wind energy resources into three categories, with the highest wind speeds of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.0 \mathrm{m} / \mathrm{s}, 8.2 \mathrm{m} / \mathrm{s}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$8.6 \mathrm{m} / \mathrm{s}$</tex>, and the highest wind energy densities of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$160 \mathrm{W} / \mathrm{m}^{2}, 264 \mathrm{W} / \mathrm{m}^{2}$</tex>, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$310 \mathrm{W} / \mathrm{m}^{2}$</tex>, respectively. Cluster analysis effectively classifies the wind energy resources of offshore wind farms and promotes the sustainable development of offshore wind power.