A Wind and Solar Power Prediction Method Based on Temporal Convolutional Network–Attention–Long Short-Term Memory Transfer Learning and Sensitive Meteorological Features

To address the issue of declining prediction accuracy caused by the lack of data in newly constructed wind and solar power stations, this paper introduces a transfer learning-based forecasting approach for wind and photovoltaic power. The method incorporates sensitive meteorological feature selection and utilizes a Temporal Convolutional Network–Attention–Long Short-Term Memory (TCN-ATT-LSTM) model. Spearman’s rank correlation, mutual information entropy, and Pearson correlation are employed to investigate the relationship between meteorological features and power output. Through evidence theory, meteorological features with a cumulative contribution exceeding 85% are selected as inputs for the wind and solar power forecasting model. The TCN-ATT-LSTM network is pre-trained to extract common knowledge, and transfer learning is applied to fine-tune (FT) the model through network parameter adjustments. This enables the adaptive model to be quickly constructed for target wind and solar power stations with limited data, improving the prediction accuracy. Finally, the effectiveness of the proposed method is validated through its application to data from a projected wind and solar power station planned for a region in northwestern China. The proposed method not only enhances forecasting accuracy for emerging wind and solar power stations with limited data but also has significant implications for the renewable energy industry.