Research on mountainous wind farm prediction based on deep learning

The wind fields in mountain areas are highly complex due to tremendous factors, including the topographic conditions present. The complexity above presents significant challenges for the construction of wind farms, spanning the initial site selection process to the subsequent wind power forecast. Computational fluid dynamics (CFD) represents an effective predictive tool that can play a significant part in the simulation and analysis of mountain wind fields. The objective of this study is to propose a deep learning-based prediction method for mountain wind farms, with the aim of improving the accuracy of wind resource assessment. A long short-term memory network (LSTM) was employed as the principal deep learning model for processing time series data pertaining to the wind field, aiming to capture the dynamic changes in wind speed and direction. In the specific method, a substantial corpus of meteorological data was initially assembled, encompassing wind speed, wind direction, temperature, and pressure. Following preprocessing and feature engineering, this data was employed to train the LSTM model. The LSTM network can effectively capture long-term dependencies in time series data, thereby enabling the prediction of wind speed and direction changes in wind farms. By optimizing the model structure and parameter settings, we have improved the accuracy and stability of the prediction. The experimental results demonstrate that the LSTM model exhibits enhanced performance in predicting wind farm data in mountainous regions. Compared with traditional physical models and statistical methods, the deep learning model has significant advantages in the accuracy and stability of wind field prediction.