Wind speed prediction is critical for wind energy conversion systems since it greatly influences the issues such as scheduling of a power system and dynamic control of the wind turbine. In this Paper, we present a new approach using two artificial intelligent methods including Adaptive Neuro-F...
Therefore, to improve the prediction accuracy at a target location, this study proposes a multiple-point model based on data from multiple locations for short-term wind speed prediction. The model, which utilizes wind speed measurements from neighboring locations and combines the extreme learning ...
The section presents an overview of wind speed prediction models,data preprocessingtechniques, and the physical locations ofdata sourcesused in wind speed forecasting. The prediction models include physical, statistical, and artificial intelligence models, and their combinations. Data processing techniques in...
In response to these challenges, we propose a novel neighborhood preserving cross-dataset data augmentation framework for high-horizon wind speed prediction. The proposed method addresses data variability and dynamic behaviors through three key components: (i) the uniform manifold ap...
The inherent randomness and intermittent nature of wind speed fluctuations pose significant challenges in accurately predicting future wind speeds. To address this complexity, a wind speed prediction model based on a multiscale temporal-preserving embedding broad learning system (MTPE-BLS) is proposed. ...
This paper aims to develop a wind speed prediction model by utilizing deep learning and neural networks. The analysis of weather data using a neural network architecture has been completed. The Lon...
wind speed collected from internet sources. Initially, WT is applied on the dataset to split the input data into approximation band and detailed band.Fig. 6shows the approximation band up to three levels.Fig. 7indicates the detailed band for three levels. The prediction of training data and ...
The experimental results confirm that the mean absolute percentage error (MAPE) of the proposed wind speed prediction model on the dataset covering the four seasons-spring, summer, autumn, and winter-is superior to that of other benchmark models, with values of 0.4412%, 2.0187%, 1.2146% and ...
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A new algorithm based on multi-objective formulation is applied to design the prediction intervals for wind power. • Data pre-process strategy based on feature extraction is built to reduce the complexity and determine the input forms. • The wind speed prediction intervals are estimated through...