A new hybrid optimization ensemble learning approach for carbon price forecasting

Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is us...

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Veröffentlicht in:Applied Mathematical Modelling Jg. 97; S. 182
Hauptverfasser: Sun, Shaolong, Jin, Feng, Li, Hongtao, Li, Yongwu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Elsevier BV 01.09.2021
Schlagworte:
Algorithms
Carbon
Decomposition
Emission analysis
Emissions control
Ensemble learning
Entropy
Environmental protection
Forecasting
Matching
Mathematical models
Neural networks
Optimization
Optimization algorithms
Prediction models
ISSN:1088-8691, 0307-904X
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Zusammenfassung:Accurate carbon price forecast plays a vital role in energy conservation, emission reduction and environmental protection. In previous studies, more attention was focused on the prediction accuracy and stability, while the problem of disharmony between the prediction model and the data pattern is usually ignored. Considering the matching utility with deeper understanding of data and model, this paper proposes a novel approach to forecast carbon price, which combines the data preprocessing mechanism, decomposition technology, forecast module with selection and matching strategy and ensemble model based on an original hybrid optimization algorithm. According to a comprehensive evaluation index in consideration of several evaluation perspectives, the optimal parameter structures of the three forecast models are selected in this framework. Then, the data components decomposed by variational mode decomposition are reconstructed into three novel range entropy series with different levels of complexity by range entropy. As a result, the matching relation between the three forecasting models and the three range entropy series is correspondingly established. Additionally, a feedback neural network optimized by hybrid optimization algorithm, which persists more superiorities of reasonable weight assignment than the usual ensemble method, is initially used to synthesize three forecasting results of range entropy series. The carbon price data from four different trading markets in China is used to test the novel approach and the experimental results indicate that it does enhance the performance of carbon price forecasting, and provide a convincing tool for the operation and investment of the carbon markets.
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ISSN:1088-8691
0307-904X
DOI:10.1016/j.apm.2021.03.020