Multi Time Scale Wind Power Prediction Model Based on Improved ACA-LSTM Neural Network

Inhalt:
A multi time scale wind power load forecasting model is proposed to optimize the parameters of LSTM model by improving ant colony algorithm. After preprocessing and normalizing the original load, Long Short-Term Memory neural network is used to construct the training model, and then the LSTM neural network is optimized by ant colony algorithm. The optimized LSTM model is applied to short-term wind power load forecasting, and compared with the traditional prediction methods, so as to achieve more accurate prediction results.