A Sustainability-Focused Real-Time Dynamic Wind Speed Estimation Method for Turbine Performance Optimization

Abstract

To achieve the highest efficiency from the turbines used in wind power plants, the region where the plant will be located must meet the appropriate conditions. One of these conditions, and the most important, is that the wind potential be above the critical value for energy production and be continuous. Locations that meet these conditions contribute positively to energy production and produce high efficiency. Based on the interpreted data, temperature, wind direction, and wind speed data from three turbines located at altitudes of 432, 454, and 492 m in the Sebenoba area of Yaylada & gbreve;, Hatay, where wind potential is high, were collected at 10 min intervals between 1 January 2017, and 19 September 2018, yielding a total of 50,986 data points. Wind speed was estimated for this region using temperature, wind direction, and time information. Daily, monthly, and seasonal analyses were used to generate forecasts for the three altitudes. Wind speed was estimated using Decision Tree Regression and 10-Fold Cross Validation methods, and Root Mean Square Error (RMSE) values were found to be 0.64917, 0.66629, and 0.59954 for the three altitudes, respectively; the overall RMSE value was found to be 0.60188. RMSE values decreased in daily, monthly, and seasonal analyses, and an inverse relationship existed between wind speed and RMSE. Analysis of these results indicated that the forecast model was suitable. This study supports sustainability by enabling accurate wind speed forecasting for optimal turbine placement, improving energy efficiency, and promoting long-term environmentally and economically sustainable wind energy planning.