Keke Wang

The authors would like to make the following corrections to the published paper

：With resource shortage and global warming becoming increasingly serious, it is urgent to accelerate the green and low-carbon energy transition. Wind power as a kind of green, low carbon, zero cost renewable energy has been rapidly developed. Aiming at the problem of strong randomness and strong temporal correlation in wind power prediction (WPP). a new framework for WPP based on RF-WOA-VMD and BiGRU optimized by attention mechanism is proposed. Firstly, the random forest algorithm (RF) is adopted to screen the influencing factors of wind power, effectively reducing the data redundancy and improving the prediction efficiency. Secondly, the variational modal decomposition (VMD) algorithm optimized by Whale Algorithm (WOA) for WPP is adopted. which uses WOA to adaptively determine the optimal parameters [K, ] in VMD, adaptively decompose raw wind power series and reduces data noise. Furthermore, BiGRU algorithm optimized by attention mechanism is proposed for WPP. The Attention mechanism is introduced to assign different weights to the hidden states of BiGRU to emphasize the impact of key information. Eventually, five comparison models are constructed to verify the effectiveness of proposed approach. The results illustrate that proposed model further enhances the prediction accuracy, and the MAPE is reduced by 86.81%.

The green development of electric power is a key measure to alleviate the shortage of energy supply, adjust the energy structure, reduce environmental pollution and improve energy efficiency. Firstly, the situation and challenges of China’s power green development is analyzed. On this basis, the power green development models are categorized into two typical research objects, which are multi-energy synergy mode, represented by integrated energy systems, and multi-energy combination mode with clean energy participation. The key points of the green power development model with the consumption of new energy as the core are reviewed, and then China’s exploration of the power green development system and the latest research results are reviewed. Finally, the key scientific issues facing China’s power green development are summarized and put forward targeted countermeasures and suggestions.

The inherent intermittency and uncertainty of wind power have brought challenges in accurate wind power output forecasting, which also cause tricky problems in the integration of wind power to the grid. In this paper, a hybrid deep learning model bidirectional long short term memory-convolutional neural network (BiLSTM-CNN) is proposed for short-term wind power forecasting. First, the grey correlation analysis is utilized to select the inputs for forecasting model; Then, the proposed hybrid model extracts multi-dimension features of inputs to predict the wind power from the temporal-spatial perspective, where the Bi-LSTM model is utilized to mine the bidirectional temporal characteristics while the convolution and pooling operations of CNN are utilized to extract the spatial characteristics from multiple input time series. Lastly, a case study is conducted to verify the superiority of the proposed model. Other deep learning models (Bi-LSTM, LSTM, CNN, LSTM-CNN, CNN-BiLSTM, CNN-LSTM) are also simulated to conduct comparison from three aspects. The results show that the BiLSTM-CNN model has the best accuracy with the lowest RMSE of 2.5492, MSE of 6.4984, MAE of 1.7344 and highest R² of 0.9929. CNN has the fastest speed with an average computational time of 0.0741s. The hybrid model that mines the spatial feature based on the extracted temporal feature has a better performance than the model mines the temporal feature based on the extracted spatial feature.