Modelling Wind Energy Potential in Different Regions with Different Methods

Abstract

Processing a lot of data is a very difficult and laborious task. In order to save time and ease the process, computational intelligence method is a very practical method for data processing. In the present study, the potential of wind energy in different regions of Turkey based on the hourly wind speed data in the years 2008-2017 were analysed statistically. Wind power density values have been examined mathematically and statistically and modelled using artificial intelligence methods. During the statistical analysis, maximum wind speed, average wind speed, wind power density, and standard deviation of wind speed have been determined. The cumulative Weibull function was used to determine wind power density and wind speed distribution on an annual basis using hourly wind speed data. Predictive models have been created by using machine learning algorithms which are computational intelligence method for the obtained wind power density values. Decision tree (DT) algorithm and multilayer perceptron (MLP) algorithm have been chosen as machine learning algorithms. Four different error analyses have been performed for DT and MLP estimates. In the machine algorithms used to estimate wind power values, the DT algorithm performed approximately 35% more accurate than the MLP algorithm. As a result, wind power densities for certain regions have been determined by using both mathematical model and computational intelligence methods.

Keywords

• Wind energy
• Weibull distribution
• Decision tree
• Multilayer perceptron

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