… , kNN applications have been evaluated on the basis of the purpose of the study, input data
… ırradiance forecasting based on meteorological data”, Energies, vol. 10, 186, 2017. [22] M. …

… data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm
for … and the spatial distribution of 14 meteorological stations and one hydrometric station. …

… In this section, the meteorological data sets investigated in this study are introduced 2.1. …
KNN was actually designed for classification approaches, but it can also be used to solve …

… data-driven. This article investigates the applicability of the k-nearest neighbor (KNN) algorithm
for … and the spatial distribution of 14 meteorological stations and one hydrometric station. …

… Meanwhile, because the goal of meteorological data classification is to improve the
prediction accuracy of wind power, the three most relevant components were chosen after …

… implementation of the k-nearest neighbor algorithm (k-NN) classifier based on the meteorological
data inputs … An improved version of the K-means algorithm was proposed by Azimi and …

This paper proposes a multiple CNN architecture with multiple input features, combined with
multiple LSTM, along with densely connected convolutional layers, for temporal wind nature …

… of this study is to develop the novel hybrid approaches by integrating grey wolf, … meteorological
input structure. The effects of hybrid approaches and multi-tupled meteorological inputs …

… development of a novel approach to detect … based on detection of false alarms where the
literature study the faults, through the application of different classification algorithms: SVM, KNN…

… which using the meteorological data the relationship between … (KNN) using multiple
features metrological input data is … algorithm based MPPT control of PV systems under partial …