Wind power interval prediction based on optimized deep learning network

In order to implement the "dual carbon" goal and sustainable development strategy, this paper proposes a method for interval prediction of wind power through Bayesian-optimized bidirectional long short-term memory network. By combining the existing deep learning algorithms, the CEEMDAN-BO-QRBiLSTM model was constructed. The empirical analysis results show that the newly established model is feasible and has higher accuracy and better effect than other existing models.