Band-limited Intrinsic Mode Functions Research Articles

Looseness monitoring of multiple M1 bolt joints using multivariate intrinsic multiscale entropy analysis and Lorentz signal-enhanced piezoelectric active sensing

Bolts are widely used in the fields of mechanical, civil, and aerospace engineering. The condition of bolt joints has a significant impact on the safe and reliable operation of the whole equipment. The failure of bolt joints monitoring leads to severe accidents or even casualties. This paper proposes a novel bolt joints monitoring method using multivariate intrinsic multiscale entropy (MIME) analysis and Lorentz signal-enhanced piezoelectric active sensing. Lorentz signal is used as excitation signal in piezoelectric active sensing to expose nonlinear dynamical characteristics of the bolt joints. Multivariate variational mode decomposition (MVMD) is employed to decompose multiple components of the collected Lorentz signal into multivariate band-limited intrinsic mode functions (BLIMFs). Afterward, improved multiscale sample entropy (IMSE) values of each channel’s BLIMFs are computed to measure its irregularity and complexity. IMSE values are taken as quantitative features, reflecting dynamical characteristics of bolt joints. Further, the constructed 3-layer feature matrices are adopted as the input of the convolutional neural network (CNN) to achieve accurate bolt joint monitoring. The multiple M1 bolt joints are used during the experiment to verify the effectiveness and superiority of the proposed approach. The results demonstrate the proposed novel approach is promising in bolt joints monitoring.

Improving seismic resolution based on enhanced multi-channel variational mode decomposition

The attenuation and dispersion of seismic waves change with frequency. So, it is unreasonable to compensate absorption and attenuation using the full-frequency band seismic data directly. In this study, we propose a seismic high-resolution processing workflow based on the enhanced multi-channel variational mode decomposition (EMVMD). Frist, we aim to adopt the multi-channel variational mode decomposition (MVMD) to decompose seismic data into several band-limited intrinsic mode functions (IMFs). Then, we explain the relationship between the IMFs and the different scale structures, and propose a strategy to determine the number of IMFs by introducing the contraction operator mapping (COM) and the scale-space representation (SSR). Note that we name the MVMD with a number selection strategy as the enhanced MVMD (EMVMD). Next, we adopt the adaptive spectrum-broadening method (ASBM) to separately improve the resolution of each decomposed IMF. Afterward, we can easily obtain the enhanced seismic data with a high resolution by summing the processed IMFs. Finally, we apply the proposed workflow (EMVMD-ASBM) to synthetic and field data to testify its validity and effectiveness for enhancing the resolution of the post-stack seismic data.

Denoising and Feature Extraction for Space Infrared Dim Target Recognition Utilizing Optimal VMD and Dual-Band Thermometry

Space target feature extraction and space infrared target recognition are important components of space situational awareness (SSA). However, owing to far imaging distance between the space target and infrared detector, the infrared signal of the target received by the detector is dim and easily contaminated by noise. To effectively improve the accuracy of feature extraction and recognition, it is essential to suppress the noise of the infrared signal. Hence, a novel denoising and extracting feature method combinating optimal variational mode decomposition (VMD) and dual-band thermometry (DBT) is proposed. It takes the mean weighted fuzzy-distribution entropy (FuzzDistEn) of the band-limited intrinsic mode functions (BLIMFs) as the optimization index of dragonfly algorithm (DA) to obtain the optimal parameters (K, α) of VMD. Then the VMD is utilized to decompose the noisy signal to obtain a series of BLIMFs and the Pearson correlation coefficient (PCC) is proposed to determine the effective modes to reconstructe the denoising signal. Finally, based on the denoising signal, the feature of temperature and emissivity-area product are calculated using the DBT. The simulation and experiment results show that the proposed method has better noise reduction performance compared with the other denoising methods, and the accuracy of feature extraction is improved at different noise equivalent irradiance. This provides more accurate feature of temerpature and emissivity-area product for space infrared dim target recognition.

An Open-Circuit Faults Diagnosis Approach for Three-Phase Inverters Based on an Improved Variational Mode Decomposition, Correlation Coefficients, and Statistical Indicators

To ensure the reliability and robustness of the three-phase inverter, it is important to accurately detect the faults of insulated gate bipolar transistor (IGBT) switches. Signal processing is widely used for monitoring and fault diagnosis of the three-phase inverters. The current signals are usually noisy, which can lead to a loss of fault information. This article proposes a new approach for identifying and detecting the IGBTs open-circuit faults of a three-phase inverter. This approach is based on an improved version of the variational mode decomposition (IVMD) technique associated with correlation coefficients and statistical indicators (variance and mean value). First, the IVMD is applied to the three-phase current signals to obtain an elementary function called the band-limited intrinsic mode functions (BLIMFs). Second, the correlation coefficients between the original signals and theirs BLIMFs are used to select the relevant modes. The variances of the modes that have the highest correlation coefficients among those selected are calculated to detect the faulty phase and, therefore, the faulty leg. Finally, the mean value of the mode that has the lowest correlation coefficients among those selected of the faulty phase is calculated to localize the fault position in either the upper or the lower IGBTs of the faulty leg. Different experimental data are used to test the effectiveness of the proposed approach. The results obtained show the merits of the proposed approach for the diagnosis and detection of open-circuit IGBTs compared to the conventional method.

Application of nonlinear system identification for EEG modelling using VMD-based deep random vector functional link network

In this paper, the EEG signal is considered for the development of the model. As the signal is nonlinear and non-stationary, the model is designed accordingly which is similar to nonlinear dynamic system identification. Initially, the signal is decomposed by a robust variational mode decomposition method for which the basic noise components are eliminated. Later, a kurtosis index method is applied to choose the best band-limited intrinsic mode functions (BLIMFs) based on their clean coefficient the model is developed using a random vector functional link neural network (RVFLN) for identification. The use of deep RVFLN provides better results as compared to simple RVFLN as explained in the result section. For verification of the system's robustness, three different epileptic signals known as pre-ictal, inter-ictal and ictal are experienced in this piece of work.

Multiscale Coherence Attribute and Its Application on Seismic Discontinuity Description

Geologic structure characterization is a key step for seismic structure interpretation, such as fluvial channels, faults, and fractures. The coherence attribute is a widely used tool for describing seismic discontinuities, which is usually calculated based on the similarity and dissimilarity of the adjacent seismic traces. However, accurately extracting coherence attribute is a difficult task in field data applications because seismic signal is one of the typical nonstationary, non-Gaussian, and wideband signals. To describe seismic discontinuities at different scales, we propose a workflow to extract the multiscale coherence (MSC) attribute. We first decompose seismic data into several band-limited intrinsic mode functions (IMFs) with different dominant frequencies via the multichannel variational mode decomposition (MVMD). Afterward, we develop a Cauchy kernel correlation-based coherence algorithm to extract the coherence attributes at different scales based on the decomposed IMFs. Finally, we can compute the MSC attribute by utilizing the calculated coherence attributes. Field data applications demonstrate that the proposed MSC attribute characterizes seismic discontinuities, such as faults and fluvial channels, more accurately and more clearly than the traditional coherence attribute and the 1-D variational mode decomposition (VMD)-based coherence attribute.

Seismic Random Noise Separation and Attenuation Based on MVMD and MSSA

Seismic noise separation and attenuation is a fundamental topic in the seismic signal processing and geological interpretation. Several kinds of algorithms are proposed for separating and attenuating seismic random noises. However, the conventional methods often handle a seismic volume trace by trace, which ignores the lateral continuity of seismic data. Moreover, seismic signal is a typical broadband signal, which makes it difficult to separate and attenuate random noises contained in the whole frequency bands of the raw seismic data. Additionally, the tuning parameters for the denoising methods are also a difficult task to filter a broadband seismic signal. In this study, we propose a multi-channel scheme which is referred as the multi-channel variational mode decomposition (MVMD) based on multi-channel singular spectrum analysis (MSSA), to efficiently and effectively separate and attenuate seismic random noises. The proposed workflow first adopts the MVMD to decompose a 2-D seismic data into several band-limited intrinsic mode functions (IMFs) with different center frequencies and different bandwidths. Then, we leverage the MSSA for each decomposed IMF to separate and attenuate random noises. It is worth to be noted that we can select the tuning MSSA parameters for each IMF based on their different center frequencies and bandwidths. Finally, we restore the valid seismic data by summing all filtered IMFs. Through detailed comparisons with the traditional denoising methods, the results from the synthetic examples and field data quantitatively and qualitatively demonstrate the effectiveness and accuracy of the proposed workflow for separating and attenuating seismic random noises.

Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length K. Then, the Variational Mode Decomposition (VMD) technique decomposes a noisy sensor data into K number of Band Limited IMFs (BLIMFs). The BLIMFs are separated as noise, noise-dominant, and signal-dominant BLIMFs by calculating the MI between BLIMFs. Eventually, the noise BLIMFs are discarded from further processing, noise-dominant BLIMFs are denoised using GSTV, and the signal BLIMFs are added to reconstruct the output signal. The regularization parameter for GSTV is automatically selected based on the values of Dispersion Entropy of the noise-dominant BLIMFs. The effectiveness of the proposed denoising method is evaluated in terms of performance metrics such as Signal-to-Noise Ratio, Root Mean Square Error, and Correlation Coefficient, which are are compared to EMD variants, and the results demonstrated that the proposed approach is able to effectively denoise the synthetic Blocks, Bumps, Doppler, Heavy Sine, PD pulses and real PD signals.

FPGA implementation of epileptic seizure detection using semisupervised reduced deep convolutional neural network

In this article, an optimized variational mode decomposition (OVMD), reduced deep convolutional neural network (RDCNN), and multi-kernel random vector functional link network (MKRVFLN) are combined to recognize the epileptic seizure using electroencephalogram (EEG) signals. An improved particle swarm optimization based on the maximum value of log energy entropy is introduced to compute the optimized values of the number of band-limited intrinsic mode functions (BLIMFs) and the data-fidelity factor. The Kurtosis and correlation coefficient are used to extract the most efficient BLIMF from highly nonlinear and non-stationary multi-class epilepsy EEG signals. The RDCNN structure is designed to extract the most discriminative unsupervised features and feed into the novel supervised MKRVFLN classifier to train efficiently by reducing the cross-entropy loss for recognizing the seizure epochs efficaciously. The Bonn University, Germany, Neurology and Sleep centre, New Delhi single-channel EEG, and Boston Children’s Hospital multichannel scalp EEG (sEEG) datasets are considered to evaluate the overall efficiency of the proposed method. The proposed method has produced 100% classification accuracy (CA) for classification problem (CP) 1 to 4 present in database-A, as well as for all CP incorporated with database-B and also it has 99.88% CA for CP-5 of the Bonn University database using a ten-fold cross-validation strategy. Furthermore, the proposed method has also the sensitivity of 100%, specificity of 99.34%, and CA of 99.37% with a negligible false positive per hour (FPR/h) of 0.663% by adopting 50% training, 30% testing and 20% validation of total data present in Boston database and its overall performance outperforms as compared to other state-of-the-art methods. The less computational complexity, higher learning speed, accurate epileptic seizure recognition, remarkable classification accuracy, short event recognition time, and negligible FPR/h are the main advantages of the proposed OVMD-RDCNN-MKRVFLN method over RDCNN, OVMD-RDCNN, and OVMD-RDCNN-KRVFLN methods. The novel RDCNN-MKRVFLN digital architecture is designed and implemented on a high-speed field-programmable gate array (FPGA) hardware environment to design a computer-aided-diagnosis (CAD) system for online epileptic seizure diagnosis. The simplicity, feasibility, and practicability of the proposed method validate the effectiveness of automatic seizure recognition.

Variational mode decomposition-based seizure classification using Bayesian regularized shallow neural network

This work presents a new epileptic seizures epoch classification scheme. Variational mode decomposition (VMD), has been explored for non-recursively decomposing the electroencephalogram (EEG) signals into fourteen band limited intrinsic mode functions (IMFs). Data augmentation (DA), has been used for handling unbalanced classification problem. Normalized energy, fractal dimension, number of peaks, and prominence parameters were computed from the band-limited IMFs for the discrimination of seizure and non-seizure epochs. Bayesian regularized shallow neural network (BR-SNNs) and six other well-known classifiers were tested. Sensitivity, specificity, and accuracy have been used as performance metrics. This study includes two different epoch lengths of 1-second and 2-seconds. A total of 32 test cases for both, class balanced and unbalanced classification problems have been taken for the performance evaluation. The best performance obtained is 100% for all the three metrics from the test cases of database-2 and 3. For database-1, average sensitivity, specificity, and accuracy of 99.71, 99.75, and 99.73% have been achieved, respectively for the 1-second epoch. The presented work shows better performance results compared to many previously reported works.

Multifuse multilayer multikernel RVFLN+ of process modes decomposition and approximate entropy data from iEEG/sEEG signals for epileptic seizure recognition

In this paper, the extracted features using variational mode decomposition (VMD) and approximate entropy (ApEn) privileged information of the input EEG signals are combined with multilayer multikernel random vector functional link network plus (MMRVFLN+) classifier to recognize the epileptic seizure epochs efficaciously. In our experiment Bonn University single-channel intracranial electroencephalogram (iEEG) and Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) multichannel scalp EEG (sEEG) recordings are considered to evaluate the efficacy of the proposed method. The VMD is applied on chaotic, non-stationary, nonlinear, and complex EEG signal to decompose it into three band-limited intrinsic mode functions (BLIMFs). The Hilbert transform (HT) is applied on BLIMFs to extract informative spectral and temporal features. The ApEn is computed from the raw EEG signals as the privileged information and given to the multi-hidden layer structure to obtain the most discriminative compressed form. The scatter plots show the distinct nature of compressed privileged ApEn information among the seizure pattern classes. The linear as well as nonlinear mapping, local and global kernel function, high-learning speed, less computationally complex MMRVFLN+ classifier is proposed to recognize the seizure events accurately by importing the efficacious features with ApEn as the input. The advanced signal processing algorithm i.e., Hilbert Huang transform (HHT) with ApEn and MMRVFLN+ are combined to compare the performance with the proposed VMDHTApEn-MMRVFLN+ method. The proposed method has remarkable recognition ability, superior classification accuracy, and excellent overall performance as compared to other methods. The digital architecture of the multifuse MMRVFLN+ is developed and implemented on a high-speed reconfigurable FPGA hardware platform to validate the effectiveness of the proposed method. The superior classification accuracy, the negligible false positive rate per hour (FPR/h), simplicity, feasibility, robustness, and practicability of the proposed method validate its ability to recognize the epileptic seizure epochs automatically.

Adaptive Complex Variational Mode Decomposition for Micro-Motion Signal Processing Applications.

In order to suppress the strong clutter component and separate the effective fretting component from narrow-band radar echo, a method based on complex variational mode decomposition (CVMD) is proposed in this paper. The CVMD is extended from variational mode decomposition (VMD), which is a recently introduced technique for adaptive signal decomposition, limited to only dealing with the real signal. Thus, the VMD is extended from the real domain to the complex domain firstly. Then, the optimal effective order of singular value is obtained by singular value decomposition (SVD) to solve the problem of under-decomposition or over-decomposition caused by unreasonable choice of decomposition layer, it is more accurate than detrended fluctuation analysis (DFA) and empirical mode decomposition (EMD). Finally, the strongly correlated modes and weakly correlated modes are judged by calculating the Mahalanobis distance between the band-limited intrinsic mode functions (BLIMFs) and the original signal, which is more robust than the correlation judgment methods such as computing cross-correlation, Euclidean distance, Bhattachryya distance and Hausdorff distance. After the weak correlation modes are eliminated, the signal is reconstructed locally, and the separation of the micro-motion signal is realized. The experimental results show that the proposed method can filter out the strong clutter component and the fuselage component from radar echo more effectively than the local mean decomposition (LMD), empirical mode decomposition and moving target indicator (MTI) filter.

Ground-Roll Separation and Attenuation Using Curvelet-Based Multichannel Variational Mode Decomposition

Ground roll, a source-generated surface wave, is a main type of coherent noise in a land seismic survey. Low-frequency and high-amplitude ground roll often overlays valid reflection events, resulting in obscuring seismic reflections. Ground-roll attenuation is an essential step for seismic data processing, which is based on the accurate separation of the ground roll and reflections without damaging their morphological characteristics. In this study, we effectively separate and suppress ground roll in shot gathers through a proposed workflow. We first identify the major components of the ground roll adopting the multichannel variational mode decomposition (MVMD), which shows significant improvements compared to the conventional single-channel VMD. Ground roll can be identified on the decomposed band-limited intrinsic mode functions (IMFs). Moreover, we propose an adaptive criterion to determine the number of decomposed IMFs. Due to the narrowly concentrated frequency components with the multichannel continuity constraint from MVMD, ground roll is mainly contained in low-frequency IMFs, which benefits the accurate ground-roll suppression. Next, we separate ground roll and reflections on the selected low-frequency IMFs through a curvelet based block-coordinate relaxation method. Afterward, we can obtain a filtered gather by removing the separated ground roll from the original shot gather. Finally, we apply the proposed workflow to synthetic and field gathers to testify its validity and effectiveness for simultaneously attenuating ground roll and preserving valid seismic reflector information.

Variational mode decomposition based differentiation of fatigue conditions in muscles using surface electromyography signals

Surface electromyography (sEMG) signals are stochastic, multicomponent and non-stationary, and therefore their interpretation is challenging. In this study, an attempt has been made to develop an automated muscle fatigue detection system using variational mode decomposition (VMD) features of sEMG signals and random forest classifier. The sEMG signals are acquired from 103 healthy volunteers during isometric (45 subjects) and dynamic (58 subjects) muscle fatiguing contractions and preprocessed. The band-limited intrinsic mode functions (BLIMFs) are extracted from non-fatigue and fatigue segments of the signals using the VMD algorithm. Hjorth features, such as activity, mobility and complexity are extracted from each BLIMF and are given to the random forest classifier. The performance of these features is evaluated using leave-one-subject-out cross-validation. The results show that the complexity feature performs better than others and it has resulted in an accuracy of 83% in dynamic contractions and 80% in isometric contractions. The performance is increased by about 8% in a dynamic condition when the most significant complexity features (p < 0.001) are used and by about 12% for isometric when the authors use all significant features. Therefore, the proposed approach could be used to detect fatigue conditions in various neuromuscular activities and real-time monitoring in the workplace.

Epilepsy seizure detection using kurtosis based VMD’s parameters selection and bandwidth features

This paper presents an automated seizure detection method based on variational mode decomposition (VMD). In VMD, the number of decomposed modes K and the penalty coefficient α are selected empirically based on experience and observation which impacts its adaptability. To overcome this difficulty, we have proposed a novel method based on kurtosis to select K and α automatically for VMD decomposition of EEG signals. Primarily, the five sets of EEG data obtained from Bonn University database are decomposed into bandlimited intrinsic mode functions (BIMFs) using VMD with parameters K and α selected using the proposed kurtosis method, then amplitude modulation bandwidth (AMBω), frequency modulation bandwidth (FMBω) and spectral features of VMD decomposed BIMFs are evaluated. The significant features are obtained using Kruskal-Wallis test and fed to four different classifiers including Decision Tree (DT), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF) for five clinically relevant classification cases. Experimental results show that our presented work deals efficiently with all the five classification cases and accuracies greater than or equal to 98.7% have been achieved using RF classifier. Finally, in comparison with related works, our proposed seizure detection scheme performs better with higher accuracies in all the five cases.

Application of an improved variational mode decomposition algorithm in leakage location detection of water supply pipeline

In the research of pipeline leak location, signal decomposition can be used to extract effective leak signal components. Variational mode decomposition (VMD) can decompose a complex non-stationary signals into several simple band-limited intrinsic mode functions (BLIMFs). However, the number of modes K and the quadratic penalty item α need to be preset before using VMD, which brings limitations to VMD. Therefore, an improved adaptive VMD (IAVMD) is proposed in this paper. This method can adaptively determine the quadratic penalty item α for each BLIMF according to the frequency characteristics of the signal. Meanwhile, the energy loss coefficient and the correlation of adjacent BLIMFs is regarded as an evaluation index to determine the best number of modes K. Finally, this method is used to locate the leak location of water pipelines, which can significantly improve the positioning accuracy.

Enhancing earthquake signal based on variational mode decomposition and S-G filter

The precise estimation of associated parameters for microseismic and earthquake signals is a challenging task due to the presence of background noise. Important parameters to analyze earthquake signals such as peak ground acceleration, velocity, displacement, and P, S-wave arrival time are affected by noise. In this study, we propose a seismic data denoising algorithm by combining variational mode decomposition (VMD) and Savitzky-Golay (SG) filter. The method first employs a VMD technique that disintegrates the original signal into band-limited intrinsic mode functions. The modes that are contaminated with high-frequency noise are selected and smoothed by the SG filter. An important advantage of SG filters is their ability to retain the shape of data with high frequency. To observe the effect of noise, PPHASEPICKER is applied to the signal provided by the proposed denoising method. As the fundamental constituent of the earthquake accelerogram is displacement, we performed an experiment to expose the effect of noise and different denoising techniques on the displacement component. The results of synthetic data and real data from the Nepal 2015 earthquake show the enhancement of signal-to-noise ratio while preserving the significant features of the displacement component and onset time arrival accuracy, in comparison with some existing techniques.

A Denoising Method for Fiber Optic Gyroscope Based on Variational Mode Decomposition and Beetle Swarm Antenna Search Algorithm.

Fiber optic gyroscope (FOG) is one of the important components of Inertial Navigation Systems (INS). In order to improve the accuracy of the INS, it is necessary to suppress the random error of the FOG signal. In this paper, a variational mode decomposition (VMD) denoising method based on beetle swarm antenna search (BSAS) algorithm is proposed to reduce the noise in FOG signal. Firstly, the BSAS algorithm is introduced in detail. Then, the permutation entropy of the band-limited intrinsic mode functions (BLIMFs) is taken as the optimization index, and two key parameters of VMD algorithm, including decomposition mode number K and quadratic penalty factor , are optimized by using the BSAS algorithm. Next, a new method based on Hausdorff distance (HD) between the probability density function (PDF) of all BLIMFs and that of the original signal is proposed in this paper to determine the relevant modes. Finally, the selected BLIMF components are reconstructed to get the denoised signal. In addition, the simulation results show that the proposed scheme is better than the existing schemes in terms of noise reduction performance. Two experiments further demonstrate the priority of the proposed scheme in the FOG noise reduction compared with other schemes.

A coarse-to-fine denoising method for dynamic calibration signals of pressure sensor based on adaptive mode decompositions

Abstract Dynamic calibration is an essential way to characterize the measurement performance of pressure sensors under dynamic environment. However, it is difficult to guarantee the reliability of calibration results because the calibration signal is inevitably contaminated by noises. In this paper, a coarse-to-fine denoising method is proposed to improve the quality of calibration signals based on adaptive mode decompositions. Firstly, variational mode decomposition (VMD) is used to decompose the calibration signal into several band-limited intrinsic mode functions (BLIMFs). The optimal mode number is estimated based on their center frequency spacing and mutual information of BLIMFs. The coarsely denoised signal is then reconstructed by the relevant BLIMFs based on the ringing energy loss ratio indicator. Subsequently, the coarsely denoised signal is decomposed as a series of IMFs with empirical mode decomposition (EMD). By introducing a clustering indicator named as comprehensive weighted correlative degree, the ringing and trend IMFs are extracted for obtaining the finally denoised result. The performance of the proposed method is validated by both simulated and actual dynamic calibration signals. Results show that the SNR of denoised result with the proposed method is 33.91, which is obviously larger than that obtained by EMD (SNR = 25.03) and VMD (SNR = 22.67) for simulated signal. Furthermore, comparative experiments also demonstrate the superiority of the proposed method over the existing approaches in both denoising ability and signal integrity.

A novel ensemble method for hourly residential electricity consumption forecasting by imaging time series

In this paper, a novel ensemble method is proposed to forecast the hourly consumption of residential electricity. Firstly, variational mode decomposition (VMD) is applied to decompose weather conditions (relative humidity and temperature, etc.), residential building data (manually operated appliances relevant to residents’ lifestyle, dishwasher, heating heat-pump, and television, etc.), and electricity price into several band-limited intrinsic mode functions (BLIMFs). Then the incremental kernel principal component analysis (IKPCA) is applied to extract the incremental kernel principal components (IKPCs) from the BLIMFs. Next, IKPCs are encoded as images by the Gramian Angular Fields (GAFs). Secondly, a novel ensemble method based on conditional generative adversarial networks (CGANs), is applied to simulate the variability in people’s electrical behavior and weather forecast errors. Moreover, the elitist search strategy of the multi-population genetic algorithm (MPGA) is introduced to realize the communication among each sub-CGAN. And then all sub-CGANs are integrated by the Huffman coding (HC). Thirdly, an improved dragonfly algorithm (IDA) is developed to optimize the weights of HC. The experimental results show that the forecasting results of the proposed ensemble method are obviously better than those of other standard and state-of-the-art methods tested in this paper.