Reliable and efficient power modeling from meteorological wind data is vital for optimal
implementation and monitoring of wind energy, and it is important for understanding turbine …

The purpose of this paper is to synthesise the existing literature on study of macroeconomic
variables on the economic growth of BRICS countries in a systematic manner, where GDP …

Time series classification is an important topic in data mining. Time series classification
methods have been studied by many researchers. A feature-weighted classification method …

Wind energy, one of the greatest progressing renewable energy sources, becomes more
significant for sustainable development and environmental protection. Its intermittent nature …

Wind mapping has played a significant role in the selection of wind harvesting areas and
engineering objectives. This research aims to find the best clustering method to cluster the …

Graph neural networks (GNN) recently have been successfully applied in wind power
prediction by employing geo-information to construct the graphs that serve as the GNN …

The accurate calculation of extreme design wind speeds for offshore wind turbines is crucial.
Under changing environmental conditions, extreme wind speeds also exhibit non-stationary …

Long time series forecasting (LTSF), which involves modeling relationships within long time
series to predict future values, has extensive applications in domains such as weather …

This article presents the Recurrent Neural Network (RNN) and its Attention mechanism to
develop forecasting models for renewable energy applications. In this study, wind speed …

Wind energy plays an essential role in the generation process of sustainable energy, with a
bright future. Therefore, predicting wind speed fluctuations and their output power plays a …