Predicting Wind Turbine Performance Using Machine Learning Techniques

Abstract

Wind energy is a rapidly growing field, and the ability to accurately predict wind turbine performance is essential for optimizing wind energy production. Machine learning technology has been successfully applied to predict wind turbine performance using various models such as neural networks, decision trees, and support vector machines. However, traditional machine learning models such as neural networks require a significant amount of time to train and optimize, and their performance can be affected by overfitting and underfitting. To address these challenges, a proposed backpropagation algorithm is introduced to predict wind turbine performance using a neural network model. The proposed methodology can be used in real-world scenarios to predict wind turbine performance and optimize wind energy production, contributing to the transition towards sustainable and clean energy sources.