Short-term wind power forecasting model based on multi-feature extraction and CNN-LSTM

Abstract

Improving the accuracy of short-term wind power forecasting is critical to wind power consumption. This paper establishes a short-term wind power prediction model based on the multi-feature extraction and deep learning network CNN-LSTM. Muti-features are extracted from original data to improve the accuracy of training. In addition, clustering algorithm is used to classify training data and train the models corresponding to those classes. CNN-LSTM prediction models are established for each cluster and compared with ARIMA, RNN, CNN and LSTM models.