Development and Comparison of Windpower Prediction Models: A Case Study in Sri Lanka

Wind power is becoming a cornerstone of renewable energy, providing a sustainable solution for electricity generation worldwide. Accurate prediction models are essential for maximizing energy output, ensuring grid stability, and seamlessly integrating renewable energy into power systems. This research focus on the Musalpetti Wind Power (Pvt) Ltd wind farm in Kalpitiya, Sri Lanka, for developing the wind power generation models. The total generated power is the dependent variable, while the average wind speed (m/s) and average ambient temperature (°C) are the independent variables. By employing advanced predictive modelling techniques tailored to local climate conditions and turbine specifications, the study aims to perform accurate wind power prediction with R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0.940. The findings demonstrate the potential of these refined models to enhance operational efficiency and support Sri Lanka's renewable energy ambitions. Ultimately, this study highlights the importance of localized approaches in optimizing wind energy production and driving the global shift towards sustainable energy solutions.