Wind speed prediction for site selection and reliable operation of wind power plants in coastal regions using machine learning algorithm variants

Abstract

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.