<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.</p>
</abstract>

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Department of Electronics and Computer Science

Department of Intelligent Computer Systems

Department of Automatic Control and Robotics, Center of Excellence in Artificial Intelligence

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural...

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Online veröffentlicht: 23. Juni 2023

Seitenbereich: 197 - 210

Eingereicht: 26. Mai 2023

Akzeptiert: 27. Mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0015

Schlüsselwörter
Renewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks

© 2023 Tacjana Niksa-Rynkiewicz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Intelligent Approach to Short-Term Wind Power Prediction Using Deep Neural Networks

Online veröffentlicht: 23. Juni 2023

Seitenbereich: 197 - 210

Eingereicht: 26. Mai 2023

Akzeptiert: 27. Mai 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0015

SchlüsselwörterRenewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks

© 2023 Tacjana Niksa-Rynkiewicz et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Renewable Energy, Wind Energy, Wind Power, Wind Turbine, Short-Term Wind Power Prediction, Deep Learning, Convolutional Neural Networks, Gated Recurrent Unit, Hierarchical Multilayer Perceptron, Deep Neural Networks