Open AccessPrediction of Chinese energy structure based on Convolutional Neural Network‐Long Short‐Term Memory (CNN‐LSTM)
Author(s) -
Li Yan,
He Yaoyu,
Zhang Meizhen
Publication year - 2020
Publication title -
energy science and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.638
H-Index - 29
ISSN - 2050-0505
DOI - 10.1002/ese3.698
Subject(s) - convolutional neural network , computer science , convolution (computer science) , energy consumption , artificial intelligence , deep learning , energy (signal processing) , consumption (sociology) , coal , term (time) , artificial neural network , recurrent neural network , china , pattern recognition (psychology) , statistics , mathematics , engineering , electrical engineering , social science , physics , quantum mechanics , sociology , law , political science , waste management
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.