A Comparative Analysis of KNN and Light GBM Algorithms for Wind Energy Forecasting

Forecasting of wind energy is an important task for the efficient and reliable integration of renewable sources into the power grid. In this paper, we explore the use of two popular machine learning algorithms, Light Gradient Boosting Machine (Light GBM) and K-Nearest Neighbors (KNN), for wind energy forecasting. We discuss the pre-processing of data, selection of hyper-parameters, training, and evaluation of models, and the use of metrics such as mean squared error (MSE) and root mean squared error (RMSE) to evaluate the performance of the models. We conclude that both KNN and Light GBM algorithms can be used for wind energy forecasting and the selection of algorithm depends on the specific requirements of the application and the size of the dataset and more accuracy obtained for light GBM algorithm.