… We acknowledge that a full calendar year was not represented in the data set. From these
meteorological observations, we considered nine atmospheric variables as input features to …

… air temperature as inputs parameters. Persistence … meteorological parameters input to k-NN.
In this paper, conventional time series model ARIMA and K-NN machine learning algorithm …

… Colak, “A novel implementation of knn classifier based on multitupled meteorological
input data for wind power prediction,” Energy Convers Manag, 135 434–444 (2017). doi:10.1016/j.…

… In this study, the k-nearest neighbor (KNN) algorithm is used with k =2 and Euclidean distance
to calculate the value of the error rate, which in turn was used to determine the accuracy. …

… A novel implementation of kNN classifier based on multi-tupled meteorological input data
for wind power prediction. Energy Conversion Manage. 135, 434–444 (2017). …

… The regression NEPP based on meteorological features … weather forecasting. The
autoregressive power prediction … confirm that the proposed novel attention mechanism based on …

… weather forecasts, the issue of missing historical data from new wind farms, and the need to
achieve accurate power prediction under extreme weather … under extreme weather scenarios…

… [52] proposed a novel method based on LUBE for predicting wind power production … input
weather-forecasting data, keeping the relevant information content. In practice, the F weather-…

… In literature it is found that most commonly kNN [16], SVM … The NWP model is set up by
combining a weather research … weather data may be obtained from the world weather online …

… In this study, a novel nonlinear predictive control method using previewed wind speeds is
proposed to address the issue of maximum wind energy extraction for variable-speed wind …