Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise Research Articles

Multiscale investigations on RDI-SPI teleconnections of Çoruh and Aras Basins, Türkiye using time dependent intrinsic correlation

This paper proposes a multiscale dynamic correlation framework based on time-dependent intrinsic correlation (TDIC) to investigate the teleconnections between reconnaissance drought index (RDI) and standardized precipitation index (SPI). The SPI and RDI indices were calculated at 3-, 6-, and 12-month time scales using data from six stations in the Çoruh and Aras (CA) basins in Turkey for the 1969–2020 period. The spatial variability of the evaluation of the drought class demonstrated that, with the exception of the northwestern region (Bayburt station), the correlation between RDI and SPI exceeded 97% and the difference in occurrence between drought classes is found to be marginal. In the multiscale analysis of the two indices, firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN) was employed to decompose the series. The modes of RDI and SPI at different process scales exhibited a strong positive linear correlation, however, the association in their long-term trends may not be of the same nature. The TDIC analysis captured the long-range correlations between the modes of RDI and SPI at diverse process scales, and the newly proposed average significant correlation (ASC) measure could quantify their strength. Within the basin, there exists strong interconnections between SPI and RDI, with the exception of the observed alterations in their nature and strength at short time spells in some inter-annual scales. The proposed TDIC based framework is a generic and robust alternative for multiscale investigation studies while ASC can quantify the strength of non-linear associations at distinct process scales.

A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction

When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy.

A hybrid deep learning-based short-term forecast model for ionospheric foF2 in East Asia region

The short-term forecast of the critical frequency of the ionospheric F2 layer (foF2) is particularly challenging due to its nonlinear and non-stationary characteristics. To improve the short-term forecast accuracy of foF2, we propose a short-term hybrid deep learning model that integrates the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and long short-term memory (LSTM) algorithms. The proposed model leverages the ICEEMDAN decomposition algorithm to expand foF2 time series data into multi-dimensional space and utilizes the LSTM algorithm to preserve long-term data information, capturing detailed changes in foF2 parameters and obtaining internal information about foF2. The 2014 and 2017 foF2 data from four observation stations located in Mohe, Beijing, Jeju, and Kokubunji in the mid-latitudes of East Asia were utilized for modeling and 1-hour forecast analysis. The forecast values of the proposed model align closely with the measured data, exhibiting minimal fluctuation in forecast error and effectively tracking the changing trend of foF2. The average root mean square error (RMSE) across the four stations in 2014 is 0.40 MHz, with a relative RMSE (RRMSE) of 7.11 % and a coefficient of determination (R2) of 0.98. The average RMSE in 2017 is 0.43 MHz, RRMSE is 7.68 %, and R2 is 0.92. The proposed model reveals significant improvements in the forecast accuracy of the comparison model compared with the CCIR, URSI, NN, and LSTM models. Especially compared with the CCIR and URSI models in the International Reference Ionosphere (IRI), demonstrating superior real-time tracking capability for foF2 and effectively addressing the challenge of poor short-term forecast accuracy observed in the IRI model.

A Novel Fault Feature Selection and Diagnosis Method for Rotating Machinery with SI-IR2CMSE and SSGMM-SR

Abstract In the field of fault diagnosis, machine learning is highly valued for its broad applicability and efficiency. Feature extraction and feature selection are key steps in the application of machine learning, and the performance of fault diagnosis methods relies heavily on the effective execution of these two steps. For this reason, this paper aims to enhance the performance of fault diagnosis methods by improving these two aspects. Firstly, to address the non-linearity and non-stationarity of rotating machinery vibration signals under variable operation conditions, this paper proposes an improved rapid refined composite multiscale sample entropy (IR2CMSE) feature extraction method. In addition, this paper decomposes the vibration signals with improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and extracts the sensitive intrinsic modal functions’ (IMFs') IR2CMSE values (SI-IR2CMSE) as the initial feature vector, which more accurately reveals the intrinsic time-scale characteristics of the vibration signals. Secondly, to address the problem of over-reliance on sample labels in most feature selection methods, this paper proposes a semi-supervised Gaussian mixing model with sparse regularization (SSGMM-SR) feature selection model. The model does not require complete fault labels and can automatically identify important features. Finally, validation with two rotating machinery fault datasets shows that the method proposed in this study exhibits high diagnostic accuracy and stability across multiple classifiers.

A Sustainable Model for Forecasting Carbon Emission Trading Prices

Carbon trading has garnered considerable attention as a pivotal policy instrument for advancing carbon peaking and carbon neutrality, which are essential components of sustainable development. The capacity to precisely anticipate the cost of carbon trading has significant implications for the optimal deployment of market mechanisms, the economic advancement of technological innovations in corporate emissions reduction, and the facilitation of international energy policy adjustments. To this end, this paper proposes a novel and sustainable trading price prediction tool that employs a four-step process: decomposition, reconstruction, prediction, and integration. This innovative approach first utilizes the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), then reconstructs the decomposition set using multi-scale entropy (MSE), and finally uses the Long Short-Term Memory neural network model (LSTM) enhanced by the Grey Wolf Optimizer (GWO) to predict the carbon emission trading price. The experimental results demonstrate that the tool achieves high accuracy for both the EU carbon price series and the carbon price series of China’s seven major carbon trading markets, with accuracy rates of 99.10% and 99.60% in Hubei and the EU carbon trading markets, respectively. This represents an improvement of approximately 3.1% over the ICEEMDAN-LSTM model and 0.91% over the ICEEMDAN-MSE-LSTM model, thereby contributing to more sustainable and efficient carbon trading practices.

Signal de-noising based on ICEEMDAN for delamination detection by phased array ultrasonic testing

Abstract Aiming at the problem of clutter noise of inter-layer reflections that occurs in multilayer heterogeneous medium such as hybrid fibre composites during ultrasonic inspection, a signal de-noising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed in this paper. In the method, the cross-correlation coefficient is utilized to identify the intrinsic mode function (IMF) that is related to raw ultrasonic signal. Particularly, the root mean square error (RMSE), as selection criterions, are employed conjunctively to sift the optimal IMF among selected decomposition signals. Hilbert transform is used to extract the envelope of the signal after removing the interlayer reflection noise. Experimental results show that, the proposed method can adaptively decompose the non-stationary ultrasonic signal into several IMFs with different spectrum so as to dealing with clutter noises. For delaminate detection by phased array ultrasonic testing for hybrid fibre composites, structural noises are significantly suppressed in the reconstructed signal that retains the distinct echo characteristics of a delaminated defect.

Study on the Fluctuating Load Characteristics of the Submerged Radial Gate in the High-Head Flood Discharge Outlet

The fluctuating pressure acting on the radial gate in the high-head flood discharge outlet is the main excitation source of flow-induced vibration. Therefore, this paper delves into the distribution characteristics of fluctuating pressure on the panel of the high-head submerged radial gate based on hydraulic model tests. Hydraulic tests were first conducted to obtain the distribution patterns of time-averaged pressure and the root mean square (RMS) of fluctuating pressure on the radial gate panel. Secondly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and HHT method was employed to identify the causes of the fluctuating pressure on the gate panel. Finally, the ICEEMDAN-SSA (the sparrow search algorithm)–LSTM (long short-term memory) method was utilized to achieve accurate prediction of the fluctuating pressure on the gate panel. The results show that the time-averaged pressure in the middle of the gate panel is higher than that at the top and near the bottom edge, which differs significantly from the static pressure distribution. The RMS of the fluctuating pressure near the bottom edge is higher than that in the middle and at the top. The fluctuating pressure acting on the gate panel in the time domain can be regarded as a stationary process. The fluctuating pressure on the gate panel is caused by the combined diffusion and random mixing of multi-scale vortices in the turbulent eddy structure. The ICEEMDAN-SSA-LSTM combined method significantly improves the prediction accuracy of fluctuating pressure on the gate panel compared to the LSTM and ICEEMDAN-LSTM methods.

Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.

The diagnosis of faults in wind turbine gearboxes based on signal processing represents a significant area of research within the field of wind power generation. This paper presents an intelligent fault diagnosis method based on ensemble-refined composite multiscale fluctuation-based reverse dispersion entropy (ERCMFRDE) for a wind turbine gearbox vibration signal that is nonstationary and nonlinear and for noise problems. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and stationary wavelet transform (SWT) are adopted for signal decomposition, noise reduction, and restructuring of gearbox signals. Secondly, we extend the single coarse-graining processing method of refined composite multiscale fluctuation-based reverse dispersion entropy (RCMFRDE) to the multiorder moment coarse-grained processing method, extracting mixed fault feature sets for denoised signals. Finally, the diagnostic results are obtained based on the least squares support vector machine (LSSVM). The dataset collected during the gearbox fault simulation on the experimental platform is employed as the research object, and the experiments are conducted using the method proposed in this paper. The experimental results demonstrate that the proposed method is an effective and reliable approach for accurately diagnosing gearbox faults, exhibiting high diagnostic accuracy and a robust performance.

Ultra-short-term photovoltaic power prediction based on similar day clustering and temporal convolutional network with bidirectional long short-term memory model: A case study using DKASC data

Due to its strong dependence on weather conditions, photovoltaic (PV) power is highly intermittent in nature. In light of this, this paper introduces a PV power prediction model based on similar-day clustering and temporal convolutional network (TCN) with bidirectional long short-term memory (BiLSTM) model. Firstly, in the data preprocessing stage, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is used to decompose and denoise the original PV power data to obtain subsequences of different frequencies. Subsequently, the sample entropy (SE) method is incorporated to reconstruct the subsequences and generate low-frequency and high-frequency subsequences with more pronounced temporal features. In addition, a self-organizing map (SOM) with the K-means algorithm is used to categorize historical days into four weather types: sunny, cloudy, rainy, and intermittent. Through grey relational analysis (GRA), meteorological factors significantly affecting PV power prediction are identified to construct a multidimensional feature set. During the model prediction phase, historical PV power data and related meteorological impact factors are input into the TCN-BiLSTM model to perform multi-data-driven PV power prediction. Finally, the predicted results of each subsequence are linearly combined to obtain the ultimate PV power prediction. The proposed model is compared with convolutional neural network (CNN)-LSTM, LSTM, and TCN models, achieving a reduction in the root mean square error (RMSE) of 0.129 kW, 0.238 kW, and 0.257 kW, respectively. This demonstrates that the proposed model exhibits superior overall performance in time series modeling and information capture, enhancing the understanding of seasonal, periodic, and irregular patterns in PV power generation data.

ICG signal denoising based on ICEEMDAN and PSO-VMD methods.

Impedance cardiography (ICG) plays a crucial role in clinically evaluating cardiac systolic and diastolic functions, along with various other cardiac parameters. However, its accuracy heavily depends on precisely identifying feature points reflecting cardiac function. Moreover, traditional signal processing techniques used to mitigate random noise and breathing artifacts may inadvertently distort the amplitude and temporal characteristics of ICG signals. To address this issue, this study investigates a noise and artifact elimination method based on Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and Particle Swarm Optimization-based Variational Mode Decomposition Algorithm (PSO-VMD). The goal is to preserve the amplitude and temporal features of ICG signals to ensure accurate feature point extraction and computation of associated cardiac parameters. Comparative analysis with signal processing methods employing various wavelet families and Ensemble Empirical Mode Decomposition (EEMD) in ICG signal processing applications reveals that the proposed method achieves superior signal-to-noise ratio (SNR) and lower root-mean-square error (RMSE), while demonstrating enhanced correlation and waveform consistency with the original signal.

An Adaptive Noise Reduction Method for High Temperature and Low Voltage Electromagnetic Detection Signals Based on SVMD Combined with ICEEMDAN.

In view of the low signal-to-noise ratio (SNR) of shear wave electromagnetic acoustic transducers (EMAT) in the detection of high-temperature equipment, the use of low excitation voltage (LEV) further deteriorates the detection results, resulting in the echo signal containing defects being drowned in noise. For the extraction of the EMAT signal, an adaptive noise reduction method is proposed. Firstly, the minimum envelope entropy is taken as the fitness function for the Harris Hawks Optimizer (HHO), and the optimal successive variational mode decomposition (SVMD) balance parameter is searched by HHO adaptive iteration to decompose LEV EMAT signals at high temperatures. Then the filter is carried out according to the excitation center frequency and correlation coefficient threshold function. Then, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used to decompose the filtered signal and combine the kurtosis factor to select the appropriate intrinsic mode functions. Finally, the signal is extracted by the Hilbert transform. In order to verify the effectiveness of the method, it is applied to the low-voltage detection of 40Cr from 25 °C to 700 °C. The results show that the method not only suppresses the background noise and clutter noise but also significantly improves the SNR of EMAT signals, and most importantly, it is able to detect and extract the 2 mm small defects from the echo signals. It has great application prospects and value in the LEV detection of high-temperature equipment.

Air quality prediction based on improved quantum long short-term memory neural networks ★ ★ This work was supported by National Natural Science Foundation of China (No.61772295), Key Projects of Chongqing Natural Science Foundation Innovation Development Joint Fund (CSTB2023NSCQ-LZX0139), Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province (Grant No. SKLACSS–202208) and Technology Research Program of Chongqing

Accurate prediction of urban air quality is of vital importance in preventing urban air pollution and improving the quality of life of urban residents. In order to achieve more accurate prediction of air quality, this study proposes a novel hybrid quantum neural network prediction model that combines an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a quantum long short-term memory network (QLSTM) optimized by the particle swarm optimization (PSO) algorithm.This study focuses on using the QLSTM model to mine the time-real fluctuations and historical dependence of air quality data and applying the PSO algorithm to optimize the hyper-parameters of the quantum model to improve the prediction accuracy; then, ICEEMDAN is introduced to disassemble the original air quality data series into multiple pattern components containing different information, which effectively reduces the complexity of the data; the feasibility and validity of the proposed methodology are verified through comparison experiments with other prediction modeling methods. The results show that the proposed QLSTM prediction method incorporating ICEEMDAN and PSO optimization has the highest prediction accuracy in terms of prediction accuracy, which contributes a novel and quantum-specific technical approach to the field of air quality prediction.

A new fault location method for high‐voltage transmission lines based on ICEEMDAN‐MSA‐ConvGRU model

AbstractGiven the complex form of distribution line faults, the accuracy of fault location using traditional artificial intelligence networks needs to be further improved. Here, a combined fault location method is proposed for a 110 kV distribution line based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), mantis search algorithm (MSA), and convolutional gate recurrent unit (ConvGRU). Firstly, the study used the ICEEMDAN algorithm to decompose the signals and discard the high‐frequency signals with low correlation so as to achieve the purpose of noise cancellation. Then, the study used the root mean square error (RMSE) of the ConvGRU model training as the adaptation value, optimized the internal parameters of the model using the MSA algorithm, and obtained a combined fault locating model. By using the proposed model, the effects of the fault form and transition impedance changes on the location accuracy were analysed, and the location accuracy was compared with other artificial intelligence methods. The location accuracy index showed that the proposed model had a better convergence speed of training error than the traditional model. Also, the RMSE of the localization results was reduced by 50%, with a higher fault location accuracy.

A hybrid data-driven method based on data preprocessing to predict the remaining useful life of lithium-ion batteries

Abstract The estimation of remaining useful life (RUL) for lithium-ion batteries is an essential part for battery management system (BMS). A hybrid method is presented which is combining principal component analysis (PCA), improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sparrow search algorithm (SSA), Elman neural network (Elman-NN), and gaussian process regression (GPR) to forecast battery RUL. Firstly, in the data preprocessing stage, the PCA+ICEEMDAN algorithm is creatively proposed to extract features of capacity decay and fluctuation. The PCA method is used to reduce the dimensionality of the extracted indirect health indicators (HIs), and then the ICEEMDAN algorithm is introduced to decompose the fused HI sequence and actual capacity data into residuals and multiple Intrinsic mode functions (IMFs). Secondly, in the prediction stage, feature data is corresponded one to-one with the mixed model. The prediction models of SSA-Elman algorithm and GPR algorithm are established, with the SSA-Elman algorithm predicting the capacity decay trend and the GPR algorithm quantifying the uncertainty caused by the capacity regeneration phenomenon. The final prediction results are obtained by superimposing the two sets of prediction data, and the prediction error and RUL are calculated. The effectiveness of the proposed hybrid approach is validated by RUL prediction experiments on three kinds of batteries. The comparative experimental results indicate that the mean absolute error (MAE) and root mean square error (RMSE) of the presented prediction model for lithium-ion battery capacity are less than 0.7% and 1.0%.

River dust-induced air pollution in a changing climate: A study of Taiwan's Choshui and Kaoping Rivers

This study investigates river dust episodes along the Choshui and Kaoping Rivers in Taiwan, focusing on their spatiotemporal distribution and correlation with hydrometeorological factors (temperature, precipitation, relative humidity, and wind speed). Using the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) algorithm and time-dependent intrinsic correlation (TDIC) analysis, we identified significant annual and diurnal correlations between PM10 concentrations and these factors. The analysis revealed that wind speed at Lunbei station had a positive annual correlation with PM10, while other factors exhibited significant negative correlations. Seasonal variations in PM10 correlations with temperature, relative humidity, and wind speed were observed, aligning with the prevailing seasons of river dust episodes. Wind motion analysis highlighted diurnal associations with land-sea breezes and annual correlations with the winter monsoon. Specifically, the Choshui River's dust events coincided with the northeast monsoon, whereas the Kaoping River's events occurred during the northwest and southwest monsoons. The study also uncovered that downstream stations (Lunbei and Daliao) were more prone to severe dust events than upstream stations (Douliu and Pingtung). These findings enhance our understanding of the dynamics and environmental impacts of river dust episodes, providing valuable insights for air quality management and health risk mitigation.

Integrated scheduling of multiple heterogeneous equipment and maintenance operations during discharging process in a container terminal under uncertainty

The integrated scheduling problem of container-handling equipment has been gaining prestige in recent years instead of decomposing the problem into a succession of subordinate sub-problems. This is due to decreasing operational costs, waiting time, completion time, and increasing efficiency. This study introduces an unprecedented integrated scheduling problem in a container terminal, wherein a broad range of heterogeneous container-handling equipment are considered, including gantry cranes, straddle carriers, transtainers, trailers, and outbound trucks. In this regard, an optimization model is developed for planning the discharging of heterogeneous containers, including storage and clearance, in three distinctive operational yards, including quay, storage, and clearance. Additionally, a wide range of realistic concerns are reflected, including maintenance operations, transport efficiency, and non-interference constraints. Furthermore, since maintenance operations’ times undeniably impact scheduling programming and involve various uncertainties, a new two-stage data-driven approach is offered to estimate reliable maintenance times. In the first stage, a hybrid machine learning approach is devised to estimate maintenance times, including the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the Takagi–Sugeno–Kang (TSK) fuzzy system, and the minibatch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA). In the second stage, since estimated maintenance operation times have uncertainty, the distributionally robust optimization (DRO) method with φ-divergence is utilized to tackle this challenge. Finally, a case study and various simulation experiments are investigated to illustrate the validation and applicability of the suggested framework.

Traffic flow prediction for highway vehicle detectors through decomposition and machine learning

ABSTRACT Traffic flow prediction is of significant importance in traffic planning. Currently, traffic flow data are primarily collected through loop detectors. However, factors such as road conditions can affect the accuracy of these data. To address this issue, this paper proposes a traffic flow prediction method based on decomposition and machine learning. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method decomposes the sequence into multiple intrinsic mode functions (IMFs). The complexity of each IMF is calculated using the sample entropy (SE), and then the IMFs are reconstructed. Parameters of the variational mode decomposition (VMD) are optimized using the whale optimization algorithm (WOA) for the secondary decomposition, and predictions are made using gated recurrent units (GRU). Finally, the prediction results are reconstructed to obtain the final prediction values. In the case study section, experiments are conducted using datasets from three detectors to explore different decomposition forms and methods.

Research on Dual-Grating Spacing Calibration Method Based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Combined with Hilbert Transform

The paper proposes a method for the calibration of spacing in dual-grating based on Multiple Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with Hilbert Transform (HT), referred to as Multiple ICEEMDAN-HT. This method addresses the potential impact of nonlinear factors on phase extraction accuracy, consequently on ranging precision in the homodyne interference of the dual-grating. Building upon the ICEEMDAN algorithm, the signal undergoes iterative decomposition and reconstruction using the sample entropy criterion. The intrinsic mode functions (IMFs) obtained from multiple iterations are then reconstructed to obtain the complete signal. Through a simulation and comparison with other signal decomposition methods, the repeatability and completeness of signal reconstruction by Multiple ICEEMDAN are verified. Finally, an actual dual-grating ranging system is utilized to calibrate the spacing of the planar grating. Experimental results demonstrate that the calibration relative error of the Multiple ICEEMDAN-HT phase unwrapping method can be reduced to as low as 0.07%, effectively enhancing the signal robustness and spacing calibration precision.

Traffic Flow Prediction by an Ensemble Framework with Data Denoising and Functional Principal Components Analysis

Accurate traffic flow data are important for congestion identification and real-time traffic control. Raw traffic flow data may be disturbed by different noises during the acquisition process, which leads to the degradation of model prediction performance. To address this problems, this paper proposes a data denoising method based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) combined with wavelet threshold denoising (WTD) to suppress potential outliers in the data, and then introduces functional principal component analysis to accomplish the traffic forecasting task. We use the proposed framework for real traffic flow prediction and validate the prediction performance of the proposed framework using root mean square error and mean absolute error. In the prediction performance comparison, five denoising methods are considered: empirical mode decomposition (EMD), ensemble empirical mode decomposition, complete ensemble empirical mode decomposition with adaptive noise, ICEEMDAN, and WTD. The prediction results show that the model combined with the denoising method outperforms the model without the denoising method. ICEEMDAN combined with WTD’s denoising method improves prediction performance more than other methods. Furthermore, the WTD method with dmey type achieves higher accuracy than other types.

ICEEMDAN-Informer-GWO: a hybrid model for accurate wind speed prediction.

Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5minutes, 30minutes, and 1hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.