TY - JOUR
T1 - A novel deep learning carbon price short-term prediction model with dual-stage attention mechanism
AU - Wang, Yanfeng
AU - Qin, Ling
AU - Wang, Qingrui
AU - Chen, Yingqi
AU - Yang, Qing
AU - Xing, Lu
AU - Ba, Shusong
N1 - Funding information: We gratefully acknowledge the financial support from National Social Science Foundation of China (No: 72293601),the Science and Technology Program of China Southern Power Grid Co., Ltd. (No. YNKJXM20222173), the Reserve Talents Program for Middle-aged and Young Leaders of Disciplines in Science and Technology of Yunnan Province, China (No. 202105AC160014).
PY - 2023/10/1
Y1 - 2023/10/1
N2 - Carbon price prediction can help participants keep abreast of carbon market dynamics and develop trading strategies. It is challenging for statistical models to accurately capture the nonlinear characteristics of the carbon pricing, and machine learning methods need sophisticated artificial feature engineering. To successfully address these drawbacks, our research suggests a carbon price forecasting model built on a deep learning architecture. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise decomposes historical price to obtain Intrinsic Mode Function and Principal Component Analysis reduces the dimensionality of each influential factor. Dual-Stage Attention-Based Recurrent Neural Network, a Seq2Seq model, made up of an encoder with feature attention and a decoder with temporal attention, is employed to predicted price of the Hubei Carbon Emissions Allowance. The dual-attention mechanism enables preprocessing to be done adaptively and more effectively than manual processing. As shown by statistical analysis and grey correlation analysis, Hubei Carbon Emissions Allowance has a high autocorrelation, and the carbon market, energy and industry, economy, and environment have high to low correlations on it. The accuracy metrics of this framework, Mean Absolute Error = 0.75, Mean Absolute Percentage Error = 1.59 and Root Mean Squared Error = 1.28, are lower than compared models.
AB - Carbon price prediction can help participants keep abreast of carbon market dynamics and develop trading strategies. It is challenging for statistical models to accurately capture the nonlinear characteristics of the carbon pricing, and machine learning methods need sophisticated artificial feature engineering. To successfully address these drawbacks, our research suggests a carbon price forecasting model built on a deep learning architecture. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise decomposes historical price to obtain Intrinsic Mode Function and Principal Component Analysis reduces the dimensionality of each influential factor. Dual-Stage Attention-Based Recurrent Neural Network, a Seq2Seq model, made up of an encoder with feature attention and a decoder with temporal attention, is employed to predicted price of the Hubei Carbon Emissions Allowance. The dual-attention mechanism enables preprocessing to be done adaptively and more effectively than manual processing. As shown by statistical analysis and grey correlation analysis, Hubei Carbon Emissions Allowance has a high autocorrelation, and the carbon market, energy and industry, economy, and environment have high to low correlations on it. The accuracy metrics of this framework, Mean Absolute Error = 0.75, Mean Absolute Percentage Error = 1.59 and Root Mean Squared Error = 1.28, are lower than compared models.
KW - Carbon price
KW - Deep learning
KW - Multivariate time series forecasting
KW - Principal component analysis
KW - Time series decomposition
UR - http://www.scopus.com/inward/record.url?scp=85162240090&partnerID=8YFLogxK
U2 - 10.1016/j.apenergy.2023.121380
DO - 10.1016/j.apenergy.2023.121380
M3 - Article
SN - 0306-2619
VL - 347
JO - Applied Energy
JF - Applied Energy
M1 - 121380
ER -