Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening
Yaoyang Yi
TL;DR
This paper introduces a new hybrid model to accurately predict carbon prices in China's regional markets using advanced decomposition and machine learning techniques.
Contribution
The novel hybrid model combines quadratic decomposition, comprehensive feature screening, and optimized LSTM with attention for improved carbon price forecasting.
Findings
The proposed model reduces prediction errors by over 40% in metrics like MSE and RMSE compared to traditional models.
SHAP analysis reveals distinct drivers for carbon prices in Guangdong, Hubei, and Shanghai markets.
The model demonstrates higher accuracy and robustness in forecasting regional carbon prices in China.
Abstract
In light of global climate change and the objective of carbon neutrality, the carbon market has become an important tool for the international community to combat climate change. Nonetheless, due to the complexity and non-linear nature of the carbon price, its accurate prediction has always been a research difficulty. This work presents a hybrid model incorporating comprehensive feature screening, optimized quadratic decomposition, and the Optuna-Attention-LSTM prediction method, aiming to improve the accuracy and stability of carbon price prediction. First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). Then, the variational mode decomposition (VMD)…
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Taxonomy
TopicsEnergy Load and Power Forecasting · Energy, Environment, Economic Growth · Market Dynamics and Volatility