Optimizing wind power forecasting with RNN-LSTM models through grid search cross-validation

Abstract

Wind energy is a crucial renewable resource that supports sustainable development and reduces carbon emissions. However, accurate wind power forecasting is challenging due to the inherent variability in wind patterns. This paper addresses these challenges by developing and evaluating some machine learning (ML) and deep learning (DL) models to enhance wind power forecasting accuracy. Traditional ML models, including Random Forest, k-nearest Neighbors, Ridge Regression, LASSO, Support Vector Regression, and Elastic Net, are compared with advanced DL models, such as Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Stacked LSTM, Graph Convolutional Networks (GCN), Temporal Convolutional Networks (TCN), and the Informer network, which is well-suited for long-sequence forecasting and large, sparse datasets. Recognizing the complexities of wind power forecasting, such as the need for high-resolution meteorological data and the limitations of ML models like overfitting and computational complexity, a novel hybrid approach is proposed. This approach uses hybrid RNN-LSTM models optimized through GS-CV. The models were trained and validated on a SCADA dataset from a Turkish wind farm, comprising 50,530 instances. Data preprocessing included cleaning, encoding, and normalization, with 70 % of the dataset allocated for training and 30 % for validation. Model performance was evaluated using key metrics such as R², MSE, MAE, RMSE, and MedAE. The proposed hybrid RNN-LSTM Models achieved outstanding results, with the RNN-LSTM model attaining an R² of 99.99 %, significantly outperforming other models. These results demonstrate the effectiveness of the hybrid approach and the Informer network in improving wind power forecasting accuracy, contributing to grid stability, and facilitating the broader adoption of sustainable energy solutions. The proposed model also achieved superior comparable performance when compared to state-of-the-art methods.