Regional wind power forecasting based on CNN-BiLSTM-reliefF method

Dehghan, Fariba, Parsa Moghaddam, Mohsen and Imani, Maryam (2024) Regional wind power forecasting based on CNN-BiLSTM-reliefF method. In 2024 11th Iranian Conference on Renewable Energy and Distribution Generation (ICREDG). IEEE.. (doi:10.1109/ICREDG61679.2024.10607820).

Abstract

The noisy and stochastic nature of wind power production introduces many challenges to the grid. Wind power forecasting methods can effectively predict these uncertainties and ensure the stable operation of power systems. Due to the superior performance of deep learning approaches in predicting nonlinear data, a hybrid method based on two-dimensional convolutional neural network (Conv2D), bidirectional long short-term memory (BiLSTM), and ReliefF algorithm is presented in this study. First, wind power data are preprocessed. Then, the processed data are entered into each Conv2D and BiLSTM network for temporal-spatial feature extraction. Finally, the features that are extracted from these two networks are combined by assigning weights to them through the ReliefF algorithm for final wind power prediction. The forecasting performance of the presented method is tested on a real aggregated dataset of Belgium for 15 minutes ahead regional wind power forecasting. According to the results, the proposed model has higher accuracy than long short-term memory, gated recurrent unit (GRU), BiLSTM, and Conv2D networks.

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Contributors

Author: Fariba Dehghan

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