Prediction of Chinese energy structure based on Convolutional Neural Network‐Long Short‐Term Memory (CNN‐LSTM)

With the improvement of environmental protection awareness and transformation of industrial structure, China's energy consumption structure has changed constantly. Although the proportion of coal consumption is gradually decreasing, the consumption has not seen a significant decline; it even increased in 2018. Many researchers have focused on changes in one specific type of energy such as coal or solar. However, the overall energy structure was ignored. In this study, an integrated convolutional neural network (CNN) with Long Short‐term Memory (LSTM) was used to predict the energy structure of China. Historical data were considered as pixels for convolution by layers of the CNN and then encoded by LSTM layers. To verify the effectiveness of the method, CNN‐LSTM was compared with six previous methods using data from 1965 to 2018. The results showed that CNN‐LSTM was superior in predicting energy structure. Finally, the energy structure of China in the next decade was predicted. The prediction results demonstrated that although the share of clean energy consumption will continue to increase, coal consumption will not decline. In this paper, we fused Convolutional Neural Network (CNN) with Long Short‐term Memory (LSTM) to predict the energy structure of China. The historical data were considered as pixels to be convoluted by layers of CNN and then were encoded by LSTM layers.