DATA IN THE BREEZE: CASE STUDIES ON WIND POWER POTENTIAL USING MAXIMUM LIKELIHOOD AND MODIFIED METHODS

Abstract

Wind power is an important renewable energy source, and estimating its potential is necessary for efficient utilization. In this paper, we discuss various methods for estimating wind power potential, with a focus on the use of the Weibull distribution. We present five approaches for estimating the parameters of the Weibull distribution, including the Maximum likelihood method, the Modified maximum likelihood method, Error of approximation, Method of Moment, and the Energy pattern factor method. A sample wind speed dataset is used to compare the accuracy of each method. We also highlight the importance of wind power forecasting in grid-connected wind generating plants and discuss various modern methodologies for weather forecasting. Lastly, we present a case study for the Energy pattern factor method, demonstrating its applicability in wind energy production.