Harmonic Detection Algorithm for Traction Power Supply System Based on ICEEMDAN and Teager Energy Operator by XIE Zeen

“…In view that the conventional harmonic detection algorithms cannot support analysis on nonlinear and non-stationary harmonics in the traction power supply systems, this paper proposes a harmonic detection algorithm based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and Teager energy operator (TEO). …”
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Collaborative Forecasting of Multiple Energy Loads in Integrated Energy Systems Based on Feature Extraction and Deep Learning by Zhe Wang, Jiali Duan, Fengzhang Luo, Xiaoyu Qiu

“…This paper proposes a collaborative load forecasting method based on feature extraction and deep learning. First, the complete ensemble empirical mode decomposition with adaptive noise algorithm decomposes load data, and a dynamic time warping-based k-medoids clustering algorithm reconstructs subsequences aligned with system load components. …”
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Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis by Houxin Ji, Ke Huang, Chaoquan Mo

“…Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method.…”
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Diagnosis of Commutation Failure in a High- Voltage Direct Current Transmission System Based on Fuzzy Entropy Feature Vectors and a PCNN-GRU by Cao Ruirui, Yang Taigang, Li Guohui, Chen Shilong

“…To enhance the diagnostic accuracy of commutation failure in weak receiving-end high-voltage direct current (HVDC) transmission systems, this study proposes a novel diagnostic model that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based fuzzy entropy and a Parallel Convolutional Gated Recurrent Unit Neural Network (PCNN-GRU). …”
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Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition by Ahmed Elbeltagi, Okan Mert Katipoğlu, Veysi Kartal, Ali Danandeh Mehr, Sabri Berhail, Elsayed Ahmed Elsadek

“…Six techniques, namely, Empirical Mode Decomposition, Variational Mode Decomposition, Ensemble Empirical Mode Decomposition, Local Mean Decomposition, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, and Empirical Wavelet Transform were used to evaluate signal decomposition efficiency in ETo prediction. …”
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Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Research on optimal selection of runoff prediction models based on coupled machine learning methods by Xing Wei, Mengen Chen, Yulin Zhou, Jianhua Zou, Libo Ran, Ruibo Shi

“…Time-Varying Filter-based Empirical Mode Decomposition (TVF-EMD), Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), and Variational Mode Decomposition (VMD). …”
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Coal Price Forecasting Using CEEMDAN Decomposition and IFOA-Optimized LSTM Model by Zhuang Liu, Xiaotuan Li

“…Abstract This study introduces a novel hybrid forecasting model for coking coal prices, integrating complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and long short-term memory (LSTM) neural networks, enhanced by an improved fruit fly optimization algorithm (IFOA). …”
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A Hybrid Model for Carbon Price Forecasting Based on Secondary Decomposition and Weight Optimization by Yongfa Chen, Yingjie Zhu, Jie Wang, Meng Li

“…Firstly, the original price series is decomposed into intrinsic mode functions (IMFs), using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). …”
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Regional Short‐Term Wind Power Prediction Based on CEEMDAN‐FTC Feature Mapping and EC‐TCN‐BiLSTM Deep Learning by Guoyuan Qin, Xiaosheng Peng, Zimin Yang

“…To improve the accuracy of regional short‐term WPP, a method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fine‐to‐coarse (FTC) feature mapping, and error compensation‐temporal convolutional network‐bidirectional Long short‐term memory network (EC‐TCN‐BiLSTM) is proposed in this paper. …”
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Photovoltaic Power Generation Forecasting Based on Secondary Data Decomposition and Hybrid Deep Learning Model by Liwei Zhang, Lisang Liu, Wenwei Chen, Zhihui Lin, Dongwei He, Jian Chen

“…This paper proposes a learning model named CECSVB-LSTM, which integrates several advanced techniques: a bidirectional long short-term memory (BILSTM) network, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), variational mode decomposition (VMD), and the Sparrow Search Algorithm (CSSSA) incorporating circle chaos mapping and the Sine Cosine Algorithm. …”
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Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction by Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang

“…Abstract To address the limitations of existing water quality prediction models in handling non-stationary data and capturing multi-scale features, this study proposes a hybrid model integrating Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Variational Mode Decomposition (VMD), Long Short-Term Memory Network (LSTM), and Frequency-Enhanced Channel Attention (FECA). …”
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Adaptive Multiscale Noise Control Enhanced Stochastic Resonance Method Based on Modified EEMD with Its Application in Bearing Fault Diagnosis by Jimeng Li, Jinfeng Zhang

“…Therefore, an adaptive multiscale noise control enhanced stochastic resonance (SR) method based on modified ensemble empirical mode decomposition (EEMD) for mechanical fault diagnosis is proposed in the paper. …”
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Research on monthly runoff prediction model considering secondary decomposition of multiple fitness functions and deep learning by Zhongfeng Zhao, Xueni Wang, Hua Jin, Jie Zheng

“…Initially, K-means clustering is employed, leveraging sample entropy (SE) to synthesize high-frequency sequences from the high-frequency components obtained via the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). …”
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Advanced Noise Reduction for In-Cylinder Combustion Pressure Data Using ICEEMDAN and Optimal Wavelet Selection by Van-Trung Nguyen, Minh-Tien Nguyen

“…This study introduces a robust approach for denoising pressure signals by integrating Improved Complete Ensemble Empirical Mode Decomposition (ICEEMDAN), Continuous Mean Square Error (CMSE) analysis, optimal wavelet selection, and wavelet thresholding techniques. …”
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Forecasting regional carbon prices in china with a hybrid model based on quadratic decomposition and comprehensive feature screening. by Yaoyang Yi

“…First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the carbon price time series once, extract high-frequency and low-frequency components, and denoise the high-frequency components using stacked denoising autoencoder (SDAE). …”
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Research on Electric Vehicle Charging Load Prediction Methods Combining Signal Noise Reduction and Time Series Modeling by Liyun Liu, Xiaomei Xu, Jinsong Zhang, Dong Li

“…This study introduces a hybrid deep learning model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Convolutional Neural Networks (CNN), Bi-directional Gated Recurrent Units (BiGRU), and Attention Mechanism (AM) to address the volatility in charging load patterns. …”
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Research on a hybrid deep learning model based on two-stage decomposition and an improved whale optimization algorithm for air quality index prediction by Hangyu Zhou, Yongquan Yan

“…The two-stage decomposition method first uses Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to initially decompose the AQI sequences, followed by Singular Spectrum Analysis (SSA) applied to the subsequence with the highest Weighted Permutation Entropy (WPE), and all subsequences are then reconstructed. …”
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PCL-RC: a parallel cloud resource load prediction model based on feature optimization by Guoxiu Zhang, Xinyi He, Xiaofeng Wang

“…To address the problem of nonlinear load data feature extraction, a feature extraction optimization method that is based on combining an improved random forest method and complete ensemble empirical modal decomposition with adaptive noise is proposed to realize regular decomposition and feature extraction from fluctuating data. …”
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Monthly Runoff Prediction Based on STL-CEEMDAN-LSTM Model by WANG Hai, SHEN Yanqing, QI Shansheng, PAN Hongzhong, HUO Jianzhen, WANG Zhance

“…The complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was then applied to decompose the residual terms to obtain intrinsic mode functions (IMFs) of different frequency components. …”
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