Machine Learning-Based Prediction of Wind Power Using MATLAB

Omar Ratib Khazaleh; Firas Ratib Alawneh; Ali Ratib Alkhuzaie

Omar Ratib Khazaleh
Firas Ratib Alawneh
Ali Ratib Alkhuzaie

Keywords: Machine Learning, Wind Power Prediction, Neural Networks, Renewable Energy, SCADA Systems, Feedforward Neural Networks (FNNs), Nonlinear Autoregressive Networks with External Inputs (NARX), Back-Propagation Neural Networks (BPNN), Radial Basis Function Networks (RBF), and Wind Turbine Performance.

Abstract

The increasing pressure to increase the use of renewable energy in the face of climate change has pushed the sustainable energy agenda forward with wind power at the forefront due to the fact that it has a large greenhouse gas emission reduction curve. However, due to the variation in wind speed, its penetration challenges undermine the predictability of power systems, which is essential for integrating volatile renewable energy sources into grids. In this paper, the focus is on the application of machine learning (ML) algorithms in improving the availability of reliable wind power forecasts as applied to wind power management and integration into power systems. In this context, this paper presents the post hoc analysis through different neural network configurations such as feedforward neural networks (FNNs), nonlinear autoregressive networks with external inputs (NARX), back-propagation neural networks, and radial base function (RBF) networks. These models were trained and validated for their ability to predict wind energy using data obtained from the Fujairah wind turbines in Jordan, which were obtained through a SCADA system from November 2013 to August 2024. The accuracy evaluation of the constructed models shows that the proposed FNN model, due to its ease and efficiency in determining the number of layers and neurons, showed the smallest RMSE and MAE compared to other models, thus confirming the FNN model as the most accurate and reliable model for wind energy prediction. On the other hand, the RBF network, which this study observed its ability to resolve nonlinear data, provided less positive results, indicating that the algorithm will require further optimization and further combination with the specific nonlinearity of wind energy data. From this study, we can highlight the importance of machine learning techniques when it comes to wind energy prediction, which is very important for wind energy management and exploitation. The knowledge derived from this research enhances the theoretical understanding of previous work in this area, but also provides compelling recommendations for energy suppliers seeking to enhance the effectiveness of their business operations and integrate more sustainable supply methods.

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