Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm
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ML can be used to analyze prices within cap-and-trade markets, for example by predicting prices via supervised learning or analyzing the main drivers of prices via hierarchical clustering.
“ML can be used to analyze prices within these markets, for example by predicting prices via supervised learning [769,834,890,892] or analyzing the main drivers of prices via hierarchical clustering [891].”
From Tackling climate change with machine learning (2023) · cites this paper as [769]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Sun, W., & Zhang, C.
- Affiliations
- North China Electric Power University (CN)
- Corresponding author
- Zhang, Chongchong
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 231
- Pages
- 1354-1371
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2890866454
- Cited by
- 181
- References
- 28
- Citations by year
- 2026: 14 · 2025: 16 · 2024: 28 · 2023: 28 · 2022: 34 · 2021: 33 · 2020: 16 · 2019: 12
- FWCI
- 12.17
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Applied Energy (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Keywords
- carbon price forecasting, extreme learning machine, hybrid forecasting model, emissions trading, EU Emissions Trading System, carbon futures, non-stationary time series, feature selection
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