Deep learning model for solar and wind energy forecasting considering Northwest China as an example

The growing demand for renewable energy sources like wind and solar power requires accurate and reliable forecasting techniques for effective planning and operation. This study presents an attention-based spatial-temporal graph neural network–long short-term memory (ASTGNN-LSTM) model designed to predict wind speed and solar radiation using 20 years of meteorological data from five regions in Northwest China. The ASTGNN-LSTM model shows significant performance improvements over traditional methods, such as the historical average model, autoregressive integrated moving average model, and graph convolutional network with LSTM. After optimizing the hidden layers and learning rate, the relative errors for predicting wind speed and solar radiation are reduced to 27.15 % and 6.11 %, respectively. Sensitivity analysis reveals that location data have the most significant impact on predictions. These findings demonstrate that the ASTGNN-LSTM model effectively captures nonlinear relationships and can enhance renewable energy planning and management. • Time and space data-driven model forecasts of wind and solar resources. • ARIMA, HA, GCN-LSTM and ASTGNN-LSTM models performance were compared. • The ASTGNN-LSTM model has the highest prediction accuracy. • Location information strongly impacted the model accuracy. • MAPE of wind speed and solar irradiance were 26.16 % and 6.10 %.