The challenge of predicting wind speeds to facilitate site selection and the consistent operation of wind power plants in coastal regions is a global concern. The output of wind turbines is subject to fluctuations corresponding to changes in wind speed. The unpredictable characteristics of wind patterns introduce vulnerabilities to wind power facilities in wind power plants. To address this unpredictability, an effective strategy involves forecasting wind speeds at specific locations during wind power plant operations. While previous research has explored various machine learning algorithms to tackle these issues, satisfactory results have not been achieved, and Bangladesh faces challenges in this regard, especially in low-wind speed areas. This study aims to identify the most accurate machine learning-based algorithm to forecast the short-term wind speed of two areas (Kutubdia and Cox's Bazar) located on the eastern coast of Bangladesh. Wind speed data for a span of 21.5 years, ranging from January 2001 to June 2022, were sourced from two outlets: the Bangladesh Meteorological Department and the website of NASA. Wind speed has been forecasted using 14 different regression-based machine learning models with a comprehensive overview. The results of the experiment highlight the exceptional predictive performance of a boosting-based ensemble method known as categorical boosting, especially in the context of forecasting wind speed data obtained from NASA. Based on the testing data, the evaluation yields remarkable results, with coefficients of determination measuring 0.8621 and 0.8758 for wind speed in Kutubdia and Cox's Bazar, respectively. The study underscores the critical importance of prioritizing optimal turbine site selection in the context of wind power facilities in Bangladesh. This approach can yield benefits for stakeholders, including engineers and project owners associated with wind projects.

中文翻译：

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使用机器学习算法变体预测沿海地区风力发电厂的选址和可靠运行的风速

预测风速以促进沿海地区风力发电厂选址和持续运行的挑战是全球关注的问题。风力涡轮机的输出会随着风速的变化而波动。风型的不可预测特性给风力发电厂的风力发电设施带来了脆弱性。为了解决这种不可预测性，有效的策略包括预测风力发电厂运营期间特定位置的风速。虽然之前的研究探索了各种机器学习算法来解决这些问题，但尚未取得令人满意的结果，孟加拉国在这方面面临挑战，特别是在低风速地区。本研究旨在确定最准确的基于机器学习的算法来预测位于孟加拉国东海岸的两个地区（库图卜迪亚和考克斯巴扎尔）的短期风速。 2001年1月至2022年6月21.5年的风速数据来自两个来源：孟加拉国气象部门和美国宇航局网站。使用 14 种不同的基于回归的机器学习模型对风速进行了全面概述。实验结果凸显了基于增强的集成方法（称为分类增强）的卓越预测性能，特别是在预测从 NASA 获得的风速数据的情况下。根据测试数据，评估结果显着，库图卜迪亚和科克斯巴扎尔的风速决定系数分别为0.8621和0.8758。该研究强调了在孟加拉国风力发电设施中优先选择最佳涡轮机选址的至关重要性。这种方法可以为利益相关者带来好处，包括与风电项目相关的工程师和项目业主。