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Keywords = CEEMDAN-Kmeans-VMD decomposition

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24 pages, 6464 KiB  
Article
A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization
by Yongfa Chen, Yingjie Zhu, Jie Wang and Meng Li
Mathematics 2025, 13(14), 2323; https://doi.org/10.3390/math13142323 - 21 Jul 2025
Viewed by 270
Abstract
Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original [...] Read more.
Accurate carbon price forecasting is essential for market stability, risk management, and policy-making. To address the nonlinear, non-stationary, and multiscale nature of carbon prices, this paper proposes a forecasting framework integrating secondary decomposition, two-stage feature selection, and dynamic ensemble learning. Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The IMFs are then grouped into low- and high-frequency components based on multiscale entropy (MSE) and K-Means clustering. To further alleviate mode mixing in the high-frequency components, an improved variational mode decomposition (VMD) optimized by particle swarm optimization (PSO) is applied for secondary decomposition. Secondly, a two-stage feature-selection method is employed, in which the partial autocorrelation function (PACF) is used to select relevant lagged features, while the maximal information coefficient (MIC) is applied to identify key variables from both historical and external data. Finally, this paper introduces a dynamic integration module based on sliding windows and sequential least squares programming (SLSQP), which can not only adaptively adjust the weights of four base learners but can also effectively leverage the complementary advantages of each model and track the dynamic trends of carbon prices. The empirical results of the carbon markets in Hubei and Guangdong indicate that the proposed method outperforms the benchmark model in terms of prediction accuracy and robustness, and the method has been tested by Diebold Mariano (DM). The main contributions are the improved feature-extraction process and the innovative use of a sliding window-based SLSQP method for dynamic ensemble weight optimization. Full article
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19 pages, 3425 KiB  
Article
Multi-Scale Decomposition and Hybrid Deep Learning CEEMDAN-VMD-CNN-BiLSTM Approach for Wind Power Forecasting
by Zhanhu Ning, Guoping Chen, Jiwu Wang and Wei Hu
Processes 2025, 13(7), 2046; https://doi.org/10.3390/pr13072046 - 27 Jun 2025
Viewed by 364
Abstract
To address the challenges posed by the volatility and uncertainty of wind power generation, this study presents a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), convolutional neural network (CNN), and bidirectional long short-term memory [...] Read more.
To address the challenges posed by the volatility and uncertainty of wind power generation, this study presents a hybrid model combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD), convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM) for wind power forecasting. The model first employs CEEMDAN to decompose the original wind power sequence into multiple scales, obtaining several intrinsic mode functions (IMFs). These IMFs are then classified using sample entropy and k-means clustering, with high-frequency IMFs further decomposed using VMD. Next, the decomposed signals are processed by a CNN to extract local spatiotemporal features, followed by a BiLSTM network that captures bidirectional temporal dependencies. Experimental results demonstrate the superiority of the proposed model over ARIMA, LSTM, CEEMDAN-LSTM, and VMD-CNN-LSTM models. The proposed model achieves a mean squared error (MSE) of 67.145, a root mean squared error (RMSE) of 8.192, a mean absolute error (MAE) of 6.020, and a coefficient of determination (R2) of 0.9840, indicating significant improvements in forecasting accuracy and reliability. This study offers a new solution for enhancing wind power forecasting precision, which is crucial for efficient grid operation and energy management. Full article
(This article belongs to the Special Issue Challenges and Advances of Process Control Systems)
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24 pages, 5616 KiB  
Article
A Method for Predicting Coal-Mine Methane Outburst Volumes and Detecting Anomalies Based on a Fusion Model of Second-Order Decomposition and ETO-TSMixer
by Qiangyu Zheng, Cunmiao Li, Bo Yang, Zhenguo Yan and Zhixin Qin
Sensors 2025, 25(11), 3314; https://doi.org/10.3390/s25113314 - 24 May 2025
Viewed by 514
Abstract
The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. This paper’s central argument is that existing prediction models are limited in several ways. These limitations [...] Read more.
The ability to predict the volume of methane outbursts in coal mines is critical for the prevention of methane outburst accidents and the assurance of coal-mine safety. This paper’s central argument is that existing prediction models are limited in several ways. These limitations include the complexity of the models and their poor ability to generalize. The paper proposes a methane outburst volume-prediction and early-warning method. This method is based on a secondary decomposition and improved TSMixer model. First, data smoothing is achieved through an STL decomposition–adaptive Savitzky–Golay filtering–reconstruction framework to reduce temporal complexity. Second, a CEEMDAN-Kmeans-VMD secondary decomposition strategy is adopted to integrate intrinsic mode functions (IMFs) using K-means clustering. Variational mode decomposition (VMD) parameters are optimized via a novel exponential triangular optimization (ETO) algorithm to extract multi-scale features. Additionally, a refined TSMixer model is proposed, integrating reversible instance normalization (RevIn) to bolster the model’s generalizability and employing ETO to fine-tune model hyperparameters. This approach enables multi-component joint modeling, thereby averting error accumulation. The experimental results demonstrate that the enhanced model attains RMSE, MAE, and R2 values of 0.0151, 0.0117, and 0.9878 on the test set, respectively, thereby exhibiting a substantial improvement in performance when compared to the reference models. Furthermore, we propose an anomaly detection framework based on STL decomposition and dual lonely forests. This framework improves sensitivity to sudden feature changes and detection robustness through a weighted fusion strategy of global trends and residual anomalies. This method provides efficient and reliable dynamic early-warning technology support for coal-mine gas disaster prevention and control, demonstrating significant engineering application value. Full article
(This article belongs to the Section Industrial Sensors)
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18 pages, 3099 KiB  
Article
Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model
by Na Fang, Zhengguang Liu and Shilei Fan
Energies 2025, 18(6), 1465; https://doi.org/10.3390/en18061465 - 17 Mar 2025
Viewed by 596
Abstract
In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature [...] Read more.
In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–gated recurrent unit (GRU) prediction model. With the goal of extracting feature information that existed in temporal series data, CEEMDAN and VMD decomposition are used to divide the raw wind data into several intrinsic modal function components. Furthermore, to reduce computational burden and enhance convergence speed, these intrinsic mode function (IMF) components are integrated and rebuilt via the results of sample entropy and K-means. Lastly, to ensure the completeness of the prediction outcomes, the final prediction results are synthesized through the superposition of all IMF components. The simulation results indicate that the proposed model is superior to other models in accuracy and robustness. Full article
(This article belongs to the Section F: Electrical Engineering)
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18 pages, 1412 KiB  
Article
Photovoltaic Power Prediction Technology Based on Multi-Source Feature Fusion
by Xia Zhou, Xize Zhang, Jianfeng Dai and Tengfei Zhang
Symmetry 2025, 17(3), 414; https://doi.org/10.3390/sym17030414 - 10 Mar 2025
Viewed by 662
Abstract
With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on [...] Read more.
With the increase in photovoltaic installed capacity year by year, accurate photovoltaic power prediction is of great significance for photovoltaic grid-connected operation and scheduling planning. In order to improve the prediction accuracy, this paper proposes a photovoltaic power prediction combination model based on Pearson Correlation Coefficient (PCC), Complete Ensemble Empirical Mode Decomposition (CEEMDAN), K-means clustering, Variational Mode Decomposition (VMD), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM). By making full use of the symmetric structure of the BiLSTM algorithm, one part is used to process the data sequence in order, and the other part is used to process the data sequence in reverse order. It captures the characteristics of sequence data by simultaneously processing a ‘symmetric’ information. Firstly, the historical photovoltaic data are preprocessed, and the correlation analysis of meteorological factors is carried out by PCC, and the high correlation factors are extracted to obtain the multivariate time series feature matrix of meteorological factors. Then, the historical photovoltaic power data are decomposed into multiple intrinsic modes and a residual component at one time by CEEMDAN. The high-frequency components are clustered by K-means combined with sample entropy, and the high-frequency components are decomposed and refined by VMD to form a multi-scale characteristic mode matrix. Finally, the obtained features are input into the CNN–BiLSTM model for the final photovoltaic power prediction results. After experimental verification, compared with the traditional single-mode decomposition algorithm (such as CEEMDAN–BiLSTM, VMD–BiLSTM), the combined prediction method proposed reduces MAE by more than 0.016 and RMSE by more than 0.017, which shows excellent accuracy and stability. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Data Analysis)
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22 pages, 4438 KiB  
Article
Combined Prediction of PM10 Concentration at Smart Construction Sites Based on Quadratic Mode Decomposition and Deep Learning
by Ming Li, Xin Li, Kaikai Kang and Qiang Li
Sustainability 2025, 17(2), 616; https://doi.org/10.3390/su17020616 - 15 Jan 2025
Viewed by 1051
Abstract
The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction sites are influenced by the interaction of construction intensity and environmental [...] Read more.
The accurate prediction of PM10 concentrations at smart construction sites is crucial for improving urban air quality, protecting public health, and advancing sustainable development in the construction industry. PM10 concentrations at construction sites are influenced by the interaction of construction intensity and environmental meteorological factors, resulting in nonlinear and volatile data. To improve prediction accuracy, this paper presents a two-stage mode decomposition method that integrates Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Variational Mode Decomposition (VMD). This method is combined with a Bidirectional Long Short-Term Memory (BiLSTM) neural network, optimized using the Sparrow Search Algorithm (SSA), to establish a hybrid model for forecasting PM10 concentrations at construction sites. Initially, CEEMDAN decomposes the original sequence into several Intrinsic Mode Functions (IMFs). The sample entropy of each component is then calculated, and K-means clustering is used to group them. VMD is applied to further decompose the high-frequency components obtained after clustering. SSA is then employed to optimize the parameters of the BiLSTM network, which models all the components with the optimized predictive model. The predicted values of all components are aggregated to generate the final forecast. Real-time monitoring data from Construction Site A in Nanjing are used for case study validation. The empirical results demonstrate that the proposed hybrid prediction model outperforms comparison models on all evaluation metrics, offering a scientific foundation for sustainable and automated dust reduction decision-making at smart construction sites, thereby facilitating the shift toward greener, smarter, and more digitized construction practices. Full article
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18 pages, 3041 KiB  
Article
A Deep Learning PM2.5 Hybrid Prediction Model Based on Clustering–Secondary Decomposition Strategy
by Tao Zeng, Ruru Liu, Yahui Liu, Jinli Shi, Tao Luo, Yunyun Xi, Shuo Zhao, Chunpeng Chen, Guangrui Pan, Yuming Zhou and Liping Xu
Electronics 2024, 13(21), 4242; https://doi.org/10.3390/electronics13214242 - 29 Oct 2024
Cited by 1 | Viewed by 1064
Abstract
Accurate prediction of PM2.5 concentration is important for pollution control, public health, and ecological protection. However, due to the nonlinear nature of PM2.5 data, the accuracy of existing methods suffers and performs poorly in both short-term and long-term predictions. In this [...] Read more.
Accurate prediction of PM2.5 concentration is important for pollution control, public health, and ecological protection. However, due to the nonlinear nature of PM2.5 data, the accuracy of existing methods suffers and performs poorly in both short-term and long-term predictions. In this study, a deep learning hybrid prediction model based on clustering and quadratic decomposition is proposed. The model utilizes the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to decompose the PM2.5 sequences into multiple intrinsic modal function components (IMFs), and clusters and re-fuses the subsequences with similar complexity by permutation entropy (PE) and K-means clustering. For the fused high-frequency sequences, a secondary decomposition is performed using the whale optimization algorithm (WOA) optimized variational modal decomposition (VMD). Finally, the nonlinear and temporal features are captured for prediction using the long- and short-term memory neural network (LSTM). Experiments show that this proposed model exhibits good stability and generalization ability. It does not only make accurate predictions in the short term, but also captures the trends in the long-term prediction. There is a significant performance improvement over the baseline models. Further comparisons with existing models outperform the current state-of-the-art models. Full article
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