Spatiotemporal wind speed forecasting using conditional local convolution and multidimensional meteorology features

Wind speed prediction is crucial for precisely wind power forecasting and reduced maintenance costs. Highland regions, which possess a considerable wind potential, present complex meteorological conditions, making wind speed prediction challenging. Traditional weather forecasting relies on complex statistical methods and extensive prior knowledge. While recent deep learning models have improved prediction accuracy, they often assume uniform influence weight structure, limiting model effectiveness. This study introduces an enhanced Conditional Local Convolution Recurrent Network (CLCRN) model to improve spatiotemporal wind speed forecasting using multidimensional meteorological inputs such as temperature, pressure, and dew point, alongside wind components. This model addresses uniform influence model weight issue by redesigning convolution kernels to better capture local meteorological features and integrating multiple influencing factors. Our model consistently achieves lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values across various prediction intervals (3, 6, 9, and 12 h) compared to other models, supported by the meteorological station data from 2019 to 2021. Furthermore, the spatial distribution of the local convolution weights aligns with local wind velocity patterns in Inner Mongolia, enhancing model interpretability. These results demonstrate potential for practical applications in renewable energy planning and wind dynamics simulation.