Wind and solar power forecasting based on hybrid CNN-ABiLSTM, CNN-transformer-MLP models

Accurate prediction of solar and wind power output is crucial for effective integration into the electrical grid. Existing methods, including conventional approaches, machine learning (ML), and hybrid models, have limitations such as limited adaptability, narrow generalizability, and difficulty in forecasting multiple types of renewable energy respectively. To address these challenges, this study introduces two novel hybrid models: the CNN-ABiLSTM, which integrates Convolutional Neural Networks (CNN) with Attention-based Bidirectional Long Short-Term Memory (ABiLSTM), and the CNN-Transformer-MLP, which integrates CNN with Transformers and Multi-Layer Perceptrons (MLP). In both hybrid models, the CNN captures short-term patterns in solar and wind power data, while the ABiLSTM and Transformer-MLP models address the long-term patterns. CNN, BiLSTM, and Encoder-based Transformer were taken as baseline standalone models. The proposed hybrid models and standalone baseline models were trained on quarter-hour-based real-time data. The hybrid models outperform standalone baseline models in day, week, and month-ahead forecasting. The CNN-Transformer-MLP hybrid provides more accurate day and week-ahead solar and wind power predictions with lower mean absolute error (MAE), root mean square error (RMSE), and mean square error (MSE) values. For month-ahead forecasts, the CNN-ABiLSTM hybrid excels in wind power prediction, demonstrating its strength in long-term forecasting.