Research on the Construction of Short-term Prediction Model of Wind Farm Output Power Based on Deep Learning Model

With the shift in the global energy structure, wind energy has become increasingly prominent in the energy market. However, wind power generation poses great challenges to grid dispatching due to its intermittency and randomness, so accurate wind power output prediction is of great significance to improve grid stability and reduce operating costs. Based on deep learning technology, this paper constructs a model for short-term power forecasting of wind farms, aiming to improve the accuracy of wind power forecasting. The model in this paper has been improved in data preprocessing, feature extraction and algorithm optimization. We used the actual operation data of a wind farm in a coastal area. The data covered three years of historical wind speed, wind direction and power output information, totaling more than 500,000 data records. The model adopts a long-term and short-term memory network architecture and combines self-attention mechanism to perform multi-step prediction. Finally, the average absolute error on the test set is reduced to 0.045, prediction accuracy 15% traditional statistical. This study shows that the application of deep learning in wind power forecasting has significant advantages and provides an important reference for the construction of smart grids in the future.