A CNN-LSTM based deep learning model with high accuracy and robustness for carbon price forecasting: A case of Shenzhen's carbon market in China

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 352, P. 120131 - 120131

Published: Jan. 23, 2024

Language: Английский

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Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence

Technological Forecasting and Social Change, Journal Year: 2024, Volume and Issue: 200, P. 123178 - 123178

Published: Jan. 3, 2024

Language: Английский

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Breaking through the limitation of carbon price forecasting: A novel hybrid model based on secondary decomposition and nonlinear integration

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 362, P. 121253 - 121253

Published: June 1, 2024

Language: Английский

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Pricing and valuation of carbon swap in uncertain finance market

Fuzzy Optimization and Decision Making, Journal Year: 2024, Volume and Issue: 23(3), P. 319 - 336

Published: June 17, 2024

Language: Английский

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Predicting energy prices based on a novel hybrid machine learning: Comprehensive study of multi-step price forecasting

Energy, Journal Year: 2024, Volume and Issue: 298, P. 131321 - 131321

Published: April 16, 2024

Language: Английский

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Attention improvement for data-driven analyzing fluorescence excitation-emission matrix spectra via interpretable attention mechanism

npj Clean Water, Journal Year: 2024, Volume and Issue: 7(1)

Published: Aug. 8, 2024

Analyzing three-dimensional excitation-emission matrix (3D-EEM) spectra through machine learning models has drawn increasing attention, whereas the reliability of these remains unclear due to their "black box" nature. In this study, convolutional neural network (CNN) for classifying numbers fluorescent components in 3D-EEM was interpreted by gradient-weighted class activation mapping (Grad-CAM), guided Grad-CAM, and structured attention graphs (SAGs). Results showed that original CNN classifier with high classification accuracy may make a based on misleading non-fluorescence area spectra. By removing Rayleigh scatterings integrating block module (CBAM) classifiers, correct trained CBAM greatly increased from 17.6% 57.2%. This work formulated strategies improving classifiers associated environmental fields would provide great help water determination both natural artificial environments.

Language: Английский

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A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124237 - 124237

Published: Jan. 29, 2025

Language: Английский

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A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 464 - 464

Published: Jan. 30, 2025

Carbon price forecasting and pricing are critical for stabilizing carbon markets, mitigating investment risks, fostering economic development. This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance forecasting. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method is employed decompose data into distinct modal components, each defined by specific frequency characteristics. Then, Lempel–Ziv complexity dispersion entropy algorithms applied analyze these facilitating identification of their unique attributes. The subsequently employs GARCH predicting high-frequency components a gated recurrent unit (GRU) neural network optimized grey wolf algorithm low-frequency components. Finally, GRU model utilized integrate predictive outcomes nonlinearly, ensuring comprehensive precise forecast. Empirical evidence demonstrates that this not only accurately captures diverse characteristics different but also significantly outperforms traditional benchmark in accuracy. By optimizing optimizer (GWO) algorithm, enhances both prediction stability adaptability, while nonlinear integration approach effectively mitigates error accumulation. innovative offers scientifically rigorous efficient tool forecasting, providing valuable insights policymakers market participants trading.

Language: Английский

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RVM+: An AI-Driven Vision Sensor Framework for High-Precision, Real-Time Video Portrait Segmentation with Enhanced Temporal Consistency and Optimized Model Design

Sensors, Journal Year: 2025, Volume and Issue: 25(5), P. 1278 - 1278

Published: Feb. 20, 2025

Video portrait segmentation is essential for intelligent sensing systems, including human-computer interaction, autonomous navigation, and augmented reality. However, dynamic video environments introduce significant challenges, such as temporal variations, occlusions, computational constraints. This study introduces RVM+, an enhanced framework based on the Robust Matting (RVM) architecture. By incorporating Convolutional Gated Recurrent Units (ConvGRU), RVM+ improves consistency captures intricate dynamics across frames. Additionally, a novel knowledge distillation strategy reduces demands while maintaining high accuracy, making ideal real-time applications in resource-constrained environments. Comprehensive evaluations challenging datasets show that outperforms state-of-the-art methods both accuracy consistency. Key performance indicators MIoU, SAD, dtSSD effectively verify robustness efficiency of model. The integration ensures streamlined effective design with negligible trade-offs, highlighting its suitability practical deployment. makes strides sensor technology, providing high-performance, efficient, scalable solution segmentation. offers potential fields reality, robotics, analysis, also advancing development AI-enabled vision sensors.

Language: Английский

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Carbon price prediction research based on CEEMDAN-VMD secondary decomposition and BiLSTM

Environmental Science and Pollution Research, Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Language: Английский

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