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This Project contains Data Analysis for my Master's thesis on "Predicting Wind Turbine Power Output using Mathematical Models".
The global pursuit of renewable energy sources has accelerated wind energy development as a sustainable solution for electricity generation. Accurate estimation of wind turbine power output is crucial for optimizing energy production and evaluating the economic viability of wind projects. Power curves, which describe the relationship between wind speed and turbine power output, are fundamental in assessing wind turbine performance.
This project aims to enhance the accuracy of power curve modelling and evaluate the economic feasibility of wind energy projects by proposing a cubic power curve model and integrating a cost model.
The cubic power curve model accommodates the non-linear nature of the wind speed and power output relationship, providing improved predictions compared to traditional linear models. By incorporating cubic functions, the model captures variations in wind speed and enhances the accuracy of power curve estimation.
The 5PL (Five-Parameter Logistic) model is a mathematical model commonly used in various fields, including biology and pharmacology, to describe sigmoidal or "S"-shaped curves. For the wind speed and power output, the 5PL model captures the non-linear relationship between these variables.
In addition to power curve modelling, this research incorporates a cost model to assess the financial viability of wind energy projects. The cost model considers capital costs, operation and maintenance expenses, and revenue generation to evaluate the project economics. By analyzing these cost factors, stakeholders can make informed decisions regarding the implementation and profitability of wind energy initiatives.
The primary objective of this analysis is to validate mathematical power prediction models: the 5PL and the cubic power curve model for predicting wind turbine power output using historical wind data, including wind speed measurements and corresponding power output, will be utilized. The cubic power curve model will be calibrated and validated using this dataset, ensuring its accuracy and reliability.