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Carbon Trading Price Prediction Research Based on HPSO-LSTM
- 1 Wuhan University of Technology, Wuhan City, Hubei Province, China
- 2 Wuhan University of Technology, Wuhan City, Hubei Province, China
* Author to whom correspondence should be addressed.
Abstract
Accurate carbon price forecasting plays a key role in promoting emission reductions and advancing the low-carbon economy. Given the strong nonlinear nature of carbon prices and the subjective challenge in tuning hyperparameters for traditional LSTM networks, this study introduces a prediction framework combining a Hybrid Particle Swarm Optimization (HPSO) algorithm with an LSTM neural network. Using China’s national carbon market data, both univariate and multivariate time series predictions are conducted. Results demonstrate that the HPSO algorithm efficiently tunes LSTM hyperparameters, enhancing performance compared to multilayer perceptron (MLP) models. Moreover, incorporating multiple variables yields superior predictive outcomes over using historical prices alone.
Keywords
Carbon Price Prediction Model, LSTM Neural Network, Hybrid Particle Swarm Optimization (HPSO) Algorithm
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Cite this article
Chen,D.;Shi,C. (2025). Carbon Trading Price Prediction Research Based on HPSO-LSTM. Applied and Computational Engineering,163,7-14.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 3rd International Conference on Software Engineering and Machine Learning
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