Research on an Ultra-Short-Term Wind Power Forecasting Model Based on Multi-Scale Decomposition and Fusion Framework

An accurate prediction of carbon pricing is essential in carbon emission management, and also provides an important role for governments to formulate corresponding policies. However, due to the inherent complexity and dynamics of carbon price sequence, the effectiveness of different decomposition algorithms for carbon price remains to be tested. In addition, existing studies lack a systematic framework to explore the organic integration of external factors and secondary decomposition technology, and the feature processing of complex external factors still needs to be improved. In order to overcome the shortcomings of existing research, This paper presents a Variational Modal Decomposition(VMD) algorithm and a Complete Ensemble Empirical Mode Decomposition with Adaptive Second decomposition technology of Noise(CEEMDAN) decomposition algorithm, and extract the features of external factors by Extreme Gradient Boosting (XGBoost) algorithm. The HI-VMD-PE-CEEMDAN-XGBoost-Transformer model for predicting carbon price is constructed by the combined Transformer algorithm. Specifically, first, we use Hampel identifer(HI) to detect and rectify the anomalies in the original sequence. After applying Variational Mode Decomposition(VMD) decomposition algorithm, Permutation Entropy(PE) is utilized to reassemble the decomposed component. Quadratic Decomposition is performed by Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN) algorithm. Then, the XGBoost algorithm is employed to extract features of external factors and screen key factors as predictive input variables. Finally, Transformer, which has stronger capability of large-scale data parallel processing, is selected as the prediction model to achieve a more scientific and effective carbon price prediction. The empirical analysis results based on EU carbon market data verify the validity and superiority of the proposed model in different forecasting scenarios.

Multistep short-term wind power forecasting model based on secondary decomposition, the kernel principal component analysis, an enhanced arithmetic optimization algorithm, and error correction

The performance of tissue imaging is improved by tissue harmonic imaging (THI) to expand and enhance the range and level of clinical diagnosis for many diseases. The separation based on High-pass filtering (S_HPF) is a commonly used method for extracting harmonic components from ultrasonic echo signals. However, the cut-off frequency, order and algorithm to realize a high-pass filter have a great influence on the separation accuracy of harmonic signals. In present study, a novel separation method (S_CEEMDAN) based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is proposed for adaptively separating harmonic components from ultrasonic echo signals. First, we calculate the ensemble size of CEEMDAN adaptively based on the added noise level, and draw a sum of intrinsic mode functions (IMFs) from the ultrasonic echo signals by CEEMDAN. Then, the spectrum of each IMF is computed and evaluated, and the IMFs containing both fundamental and harmonic components are further decomposed by using the CEEMDAN algorithm. This separation process is end until all of IMFs have been divided into either fundamental or harmonic categories. Finally, the corresponding fundamental and harmonic echo signals are yielded by accumulating separately these two categories. In experiments, simulated ultrasonic echo signals with a center frequency of 3.5MHz are separated by the proposed S_CEEMDAN method, and the results are compared with those processed by S_HPF. The edge of the harmonic image by S_CEEMDAN marginally better defined than S_HPF. The indices for the harmonic signals separated by S_CEEMDAN and S_HPF, respectively, are as follows: the center frequencies of 6.66 MHz and 6.65 MHz, 3 dB bandwidths of 1.04 MHz and 1.05 MHz, 6 dB bandwidths of 1.59 MHz and 1.55 MHz, signal-to-noise ratios (SNRs) of 14.00 and 13.63, and image contrasts of 7.01 and 7.06. In conclusion, due to good adaptive characteristics, and lower reconstruction errors, the proposed S_CEEMD method is superior to S_HPF in the performance of spectral accuracy and harmonic imaging. This method could be potentially alternative to the current method for the ultrasonic harmonic separation.

The electrical energy supply relies on the satisfactory operation of insulators. The ultrasound recorded from insulators in different conditions has a time series output, which can be used to classify faulty insulators. The random convolutional kernel transform (Rocket) algorithms use convolutional filters to extract various features from the time series data. This paper proposes a combination of Rocket algorithms, machine learning classifiers, and empirical mode decomposition (EMD) methods, such as complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), empirical wavelet transform (EWT), and variational mode decomposition (VMD). The results show that the EMD methods, combined with MiniRocket, significantly improve the accuracy of logistic regression in insulator fault diagnosis. The proposed strategy achieves an accuracy of 0.992 using CEEMDAN, 0.995 with EWT, and 0.980 with VMD. These results highlight the potential of incorporating EMD methods in insulator failure detection models to enhance the safety and dependability of power systems.

A hybrid forecasting model of carbon emissions with optimized VMD and error correction

Accurate power load forecasting plays an important role in smart grid analysis. To improve the accuracy of forecasting through the three-level “decomposition–optimization–prediction” innovation, this study proposes a prediction model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the improved sparrow search algorithm (ISSA), a convolutional neural network (CNN), and bidirectional long short-term memory (BiLSTM). A series of simpler intrinsic mode functions (IMFs) with different frequency characteristics can be decomposed by CEEMDAN from data, then each IMF is reconstructed based on calculating the sample entropy of each IMF. The ISSA introduces three significant enhancements over the standard sparrow search algorithm (SSA), including that the initial distribution of the population is determined by the optimal point set, the position of the discoverer is updated by the golden sine strategy, and the random walk of the population is enhanced by the Lévy flight strategy. By the optimization of the ISSA to the parameters of the CNN-BiLSTM model, integrating the prediction results of the reconstructed IMFs in the sub-models can obtain the final prediction result of the data. Through the performance indexes of the designed prediction model, the application case results show that the proposed combined prediction model has a smaller prediction error and higher prediction accuracy than the eight comparison models.

A method of planetary gear fault diagnosis based on the fuzzy entropy of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multi-layer perceptron (MLP) neural network is proposed. The vibration signal is decomposed into multiple intrinsic mode functions (IMFs) by CEEMDAN, and the fuzzy entropy that combines the fuzzy function and sample entropy is proposed and used to extract the feature information contained in each IMF. The fuzzy entropies of each IMF are defined as the input of the MLP neural network, and the planetary gear status can be recognized by the output. The experiments prove the proposed method is effective.

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

In this study, different kinds of hybrid Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithms with forecasting models including Random Forest (RF), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) neural networks, are developed to estimate the mean daily wind speed at the height of 2 m in Ağrı city (WS st12 ), Turkey. In these hybrid models, different layer networks of single and integrated LSTM and GRU models include general single LSTM, general single GRU, simple coupled LSTM-GRU, and novel coupled LSTM with GRU through Addition layer (i.e., LSTM + GRU model) structures are applied. The most effective parameters on the WS st12 , from a list of on-site potential meteorological parameters and wind speed values in its adjacent cities of Ağrı province from Jan 2015–Dec 2019 through the Pearson correlation coefficient method, are determined. In the hybrid CEEMDAN and DNNs-based models, State activation functions (SAF), numbers of hidden neurons (NHN), dropout rates (P-rate), and network structural architect (NSA) as the meta-parameters are tuned for lessening the impact of overfitting/underfitting dilemmas and improving modeling performance. According to the comparison plots, performance evaluation measures, and total learnable parameter (TLP), the novel developed hybrid CEEMDAN-RF-(LSTM + GRU) model is confirmed as the best approach with an R 2 of 0.86 while, in the optimal scenario using the RF model, R 2 was 0.47. Graphical Abstract Based on the graphical snapshot, this study focuses on estimating daily mean wind speed at a 2-meter height in Ağrı, Turkey, using hybrid data-driven models. The research integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm with advanced forecasting techniques, including Random Forest (RF), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) neural networks. The modeling framework explores various configurations, such as standalone LSTM and GRU, coupled LSTM-GRU structures, and a novel LSTM + GRU model using an Addition layer to enhance predictive accuracy.

Accurate photovoltaic (PV) power prediction technology plays a crucial role in effectively addressing the challenges posed by the integration of large-scale PV systems into the grid. In this article, we propose a novel PV power combination prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in conjunction with a hybrid neural network. To mitigate the influence of strong fluctuations in PV power on prediction outcomes, we employ CEEMDAN to decompose the PV data into several subsequences. Subsequently, sample entropy (SE) is used to quantify the complexity of each subsequence. Subsequences with similar SE values are then restructured to reduce computational load. Moreover, to overcome the limitations of a single neural network in capturing historical data features of PV power, a hybrid sequential convolutional neural network-gate recurrent unit (CNN-GRU) neural network is proposed. The effectiveness of our proposed model is validated through case studies involving PV power stations in two regions. To provide a comprehensive assessment, we conduct comparative validation by building and evaluating alternative models, including long-short term memory (LSTM), GRU, CEEMDAN-LSTM, CEEMDAN-GRU, and CNN-GRU. The results unequivocally demonstrate that the model presented in this article exhibits exceptional prediction performance, characterized by high accuracy and robust generalization.

Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error.

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters.

A new carbon price prediction model

Aging is a complex phenomenon that can be studied by analyzing age-related alterations in the execution of various tasks. This work deals with the processing of navigation data acquired from a bike simulator in two populations: young healthy subjects and older adults with loss of autonomy. Our goal is to analyze the influence of age in the ability to ride a bike on a virtual straight line with the perspective to be able to identify diseases in a geriatric population. For this purpose we process time series defined as the deviation from the center of the virtual path, in two straight lines: for each time series we quantify the irregularity - through sample entropy (SampEn) - of intrinsic mode functions obtained from complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). The results show that our approach is able to reveal different learning and adaptation skills in the two populations. Our work could be useful to help the geriatricians in their diagnoses.

Electroencephalogram (EEG) signal-based emotion classification is vital in the ever-growing human-computer interface (HCI) applications. However, the chaotic, non-stationary, and person-dependent nature of EEG signals often limits such practical applications. These challenges reduce the ability of the state-of-the-art approaches to effectively distinguish between different emotional states from EEG data, resulting in sub-optimal emotion recognition performance. This work presented a time-frequency (T-F) analysis of EEG signals to localize different EEG frequency rhythms responsible for emotion-related information in the EEG signals. In particular, this work investigated two T-F analysis domains for multichannel EEG signals based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and local mean decomposition (LMD) to extract entropy features that capture specific emotion-relevant traits from the specific EEG channels that are highly responsive to emotions. The CEEMDAN and LMD decompose the EEG signals into different EEG frequency rhythms called mode functions, intrinsic mode functions (IMFs), and product functions (PFs), respectively. Further, various types of entropy feature of these two categories of mode functions, such as approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn), and bubble entropy (BuEn), are computed for extracting emotion-relevant distinguishing features. Entropy features help quantify EEG’s non-linear behavior and eventually help classify EEG-based emotions precisely. Emotion classification has been achieved using a grid search cross-validation (GSCV) optimized XGboost classifier. Thorough experimentations are conducted to validate the efficacy of the proposed approach on publically accessible datasets named Database for Emotion Analysis of Physiological Signals (DEAP), SJTU emotion EEG dataset (SEED), and SEED-IV. The efficacy of the proposed emotion recognition approach is measured in terms of widely used performance metrics such as accuracy, confusion metrics, receiver operating characteristics (RoC), and area under the curve (AuC). The highest average accuracy is attained using the proposed LMD-domain BuEn features, i.e., 97.8%, 98.6%, and 95.7% using SEED, SEED-IV, and DEAP databases, respectively, outperforming the recent state-of-the-art emotion recognition algorithms.