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

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.

Automated summary

In this paper, an intelligent approach using various types of Deep Neural Networks (DNNs) is proposed for the Short-Term Wind Power Prediction (STWPP) problem. The impact of prediction time horizon and temperature on prediction accuracy are analyzed. Three types of DNNs, including CNN, GRU, and H-MLP, are implemented and tested in the Deep Learning Power Prediction System (DLPPS). The system is trained on real wind farm data and achieves the best results with the GRU network. The system is advantageous in its high effectiveness prediction using minimal parameters. Wind power prediction in wind farms is crucial due to its increasing capacity as a promising source of renewable energy.