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

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.

Review of AI-Based Wind Prediction within Recent Three Years: 2021–2023

“…AI-based datadriven [31] models include various machine learning (ML) algorithms [32,33], such as support vector machine (SVM), extreme learning machines (ELM), and various traditional artificial neural networks (ANNs) represented by back propagation neural network (BPNN). In many studies [34][35][36], AI-based models have shown better predictive accuracy and robustness in complex nonlinear problems [37]. Li et al [38] successfully achieved accurate wind power prediction using the proposed enhanced crow search algorithm optimization-extreme learning machine model, keeping root mean square error (RMSE) and mean absolute percentage error (MAPE) values below 20% and 4%, respectively, effectively reducing the impact of large-scale wind power integration on the grid.…”