Search Results (582)

Accurate state of health (SOH) estimation of lithium-ion batteries (LIBs) is critical to ensuring the safety and reliability of battery management system (BMS). To achieve precise estimation, this study proposes a hybrid framework that integrates variational mode decomposition (VMD), kernel principal component analysis (KPCA), polar lights optimizer (PLO), and physics-informed neural network (PINN) for SOH estimation. First, multidimensional health features are extracted and decomposed by VMD into intrinsic mode functions (IMFs), which are then compressed into a one-dimensional principal component via KPCA, retaining over 95% of the original information. Subsequently, the PLO algorithm is used to adaptively optimize three key hyperparameters of the PINN-based model: the learning rate, the number of collocation points, and the regularization loss weight. Finally, the optimized PINN is deployed to predict the SOH of the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. Experimental results demonstrate that the proposed VMD-KPCA-PLO-PINN exhibits high prediction accuracy under both 7:3 and 5:5 training-to-testing data partitions. For example, under the 5:5 partition, the proposed model achieves an average $R^2$ of 0.983 and an average RMSE of 0.0085 on the tested CALCE cells. Full article

To address the high non-stationarity of COVID-19 pandemic time-series data and the severe error accumulation issue in long-horizon forecasting, a spatiotemporal two-branch multi-step forecasting model named CFC-FLCA is proposed. This model integrates closed-form continuous-time neural networks (CFC), a hybrid decomposition strategy combining variational mode decomposition (VMD) and jump plus AM-FM mode decomposition (JMD), and a cross-attention (CA) mechanism. First, the VMD-JMD hybrid mode decomposition method is applied to preprocess raw new case sequences. By leveraging the complementary advantages of the two decomposition algorithms, non-stationary sequences are adaptively decomposed into high-frequency noise components and low-to-mid-frequency trend-periodic components, eliminating random disturbance interference at the data source. On this basis, a time–frequency dual-branch feature extraction network is constructed. CFC provides ultra-long-range temporal dependency modeling capability; the time-domain branch adopts Legendre projection units (LPU) to extract robust temporal evolution features, while the frequency-domain branch employs frequency-enhanced units (FEU) to uncover latent periodic patterns that are difficult to capture using traditional time-domain methods. A cross-attention mechanism is introduced to dynamically learn the importance weights of time–frequency-domain features, enabling the adaptive deep integration of complementary information and effectively mitigating error accumulation in long-horizon forecasting. Multi-step forecasting experiments are conducted on real-world COVID-19 datasets from Belgium, the Czech Republic, and Ireland, with comprehensive comparisons against mainstream time series forecasting models. The experimental results demonstrate that the CFC-FLCA model outperforms all comparison models across all evaluation metrics for all prediction horizons. Full article

This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy environments. Monte Carlo analysis is used to generate a synthetic dataset with 200 runs per fault class by introducing component tolerances and realistic faults. A multi-stage pipeline is proposed; it begins with resampling the signals and normalizing them, and then noise is added at different levels: 5 dB, 10 dB and 20 dB. Feature fusion is performed by combining time-, frequency-, and statistical-domain features. Statistical-domain features are extracted by applying Variational Mode Decomposition (VMD) to split them into four IMF levels, followed by the application of Continuous Wavelet Transform (CWT) for time–frequency-domain analysis. Support Vector Machine (SVM), Random Forest, and Gradient Boosting are used as base-level classification models. A stacking ensemble model is developed which uses Random Forest, Gradient Boosting, and Extra Trees as base learners and Logistic Regression as the meta-learner. Full article

Gas concentration in coal mine return-air corners is affected by ventilation, mining disturbance and gas drainage conditions, and it shows strong nonstationarity, local fluctuation and dynamic multi-point correlations. To improve frequency information separation, monitoring point relationship modeling, and short-term prediction accuracy, a variational mode decomposition (VMD)-based dual-branch spatiotemporal graph method is proposed. Gas concentrations from four key monitoring points are used as inputs, and the return-air corner gas concentration is taken as the output. First, the raw series are decomposed by VMD and reconstructed into low- and high-frequency components. Then, two branches are built for different frequency components. The low-frequency branch combines adaptive graph learning, graph convolution and gated recurrent units to extract global variation features, while the high-frequency branch combines graph attention and gated recurrent units to capture local disturbance features. Finally, a feature-fusion module generates multi-step predictions, and a lightweight short-term warning strategy is developed based on the predicted values. The proposed model achieves MAE, RMSE and R² values of 0.0338, 0.0471 and 0.9499 in one-step prediction, respectively, and outperforms GRU, LSTM, GCN-GRU, GAT-GRU, VMD-GRU, Informer and STGCN under three-step and six-step conditions. Cross-dataset validation and inference time analysis indicate good adaptability and online prediction potential. Full article

Accurate carbon price forecasting is essential for emission trading risk management and low-carbon investment decisions. In existing decomposition-prediction frameworks, secondary decomposition targets are typically selected based on statistical complexity rather than domain-informed causality, and standard Long Short-Term Memory (LSTM)-Transformer architectures discard the cell state that encodes long-term temporal memory. These limitations are particularly pronounced where energy-driven causal structures and regime-switching volatility coexist. This study proposes Causal State-driven Network (CaStNet), an intelligent forecasting framework with two core innovations. A Policy-Causality-guided Residual Secondary Decomposition (PCRSD) module replaces entropy-based criteria with Granger causality to select intrinsic mode functions (IMFs) exhibiting significant energy-carbon causal linkages for targeted variational mode decomposition (VMD). A Cell-State-Driven Dual-function Attention (CSDA) mechanism repurposes the LSTM cell state for simultaneously injecting long-term memory into the Transformer and employing the cell-state differential velocity as a volatility proxy to adaptively regulate Top-k attention sparsity. The Artificial Lemming Algorithm (ALA) globally co-optimizes decomposition dimensions and attention boundaries. A Shapley Additive exPlanations (SHAP)–Local Interpretable Model-agnostic Explanations (LIME) interpretability analysis reveals horizon-dependent driver transitions from short-term autoregressive momentum to long-term energy fundamentals, uncovering threshold nonlinearities in energy-carbon transmission channels. Validation on the Shanghai market (2013–2025) achieves point-forecast RMSE = 0.8326 and R² = 0.9777, outperforming all twelve benchmark models. Cross-market testing on the Hubei market yields R² = 0.9487, and expanding-window five-fold cross-validation on the Shanghai dataset yields mean R² = 0.9704, jointly confirming generalization robustness. Full article

Accurate single-step prediction of stack NO_x concentration is essential for emission monitoring and ammonia-injection control in cement kiln SNCR–SCR hybrid denitrification systems. However, this task is challenging because industrial kiln data are affected by nonstationary emission fluctuations, nonlinear multivariable coupling, process-dependent time delays, and online deployment constraints. To address these process-specific challenges, this study develops a leakage-free dynamic soft-sensing framework for stack NO_x concentration prediction. In the proposed framework, variational mode decomposition (VMD) is used to characterize the multi-scale nonstationarity of the stack NO_x sequence under a sliding-window protocol. Trend-guided maximal information coefficient (MIC) analysis is then applied for nonlinear feature selection and delay compensation using only the training data, and the identified feature subset and delay parameters are fixed for validation and testing. A TCN-SE-LSTM model is constructed to extract temporal dependencies, recalibrate informative feature channels, and capture long-lag dynamic behavior. In addition, the Dual Adaptive Constrained Ivy Algorithm (DAC-IVY) is used only for offline hyperparameter optimization, so that the online stage requires only the trained prediction model. Experiments using 21,600 raw samples collected from an actual cement kiln Distributed Control System (DCS) show that the proposed framework achieves an RMSE of 0.2084 mg/Nm³ and an $R^2$ of 0.9844 on the test set, outperforming conventional baseline models. These results indicate that the proposed framework can provide an effective soft-sensing basis for subsequent denitrification control and operational optimization. Full article

Accurate prediction of effluent total nitrogen (TN) is important for early exceedance warning and operational control in wastewater treatment plants. Existing decomposition-based models may overestimate performance when full-series decomposition is performed before data splitting, causing potential temporal information leakage. To address this issue, this study compares noncausal and strictly causal Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise combined with Long Short-Term Memory (ICEEMDAN-LSTM) and Variational Mode Decomposition–Long Short-Term Memory network (VMD-LSTM) settings, and proposes a Level–Trend Persistence-Residual LSTM (LT-PR-LSTM) for univariate effluent TN prediction. The model uses Persistence as the short-term state baseline, extracts level features from historical TN, and introduces first- and second-order differences to learn residual corrections relative to the current state. Multi-model comparison, ablation experiments, stability tests, SHapley Additive exPlanations (SHAP) interpretation, supplementary dataset validation, and efficiency analysis were conducted. Results show that noncausal decomposition inflates predictive performance. LT-PR-LSTM achieves the best main-test performance, with RMSE 1.1273, MAE 0.6082, MAPE 7.5455%, and $R^2$ 0.8512, reducing RMSE, MAE, and MAPE by 6.73%, 7.64%, and 8.56% compared with Persistence. SHAP identifies $TN(t-2h)$ as the dominant predictor, and the model requires only 0.5348 ms/sample, indicating potential for online TN early warning. Full article

This is of significant engineering importance for enhancing the operation stability and reliability of aeroengines. To ensure the precise identification of aerodynamic instability, it proposes a deep learning model for multi-source fusion based on cross-attention and bidirectional Long Short-Term Memory (CA_BiLSTM) network. From a high-speed multistage compressor, multi-dimensional feature extraction is performed in the time domain, frequency domain, and entropy value range. Based on dispersion entropy, feature cross-identification is constructed with a multi-level early warning method. In response to the nonlinear aerodynamic parameters, Variational Mode Decomposition (VMD) and Dung Beetle Optimizer (DBO) for global optimization are integrated to construct a VMD_DBO_LSTM-coupled prediction model for aerodynamic stability. To address the limitation of single-point detection, this paper proposes a dual-channel fusion model based on cross-attention mechanism. Through shared convolution and dynamic weighting mechanism, the CA_BiLSTM model can precisely characterize the nonlinear features of the complex flow. It can fully integrate the complementary information of inlet and outlet signals, achieving the collaborative signal characterization. Its anti-interference capability is significantly superior to that of the original single-point signal. Combined with the dispersion entropy threshold, it can detect instability 1580 r in advance, effectively overcoming the problems of information deficiency and incomplete representation caused by traditional single-point monitoring. Full article

To enable fine-grained electricity management on the user side under the dual-carbon strategy and address the inherent limitations of traditional non-intrusive load monitoring (NILM) methods in multi-load parallel operation scenarios, this paper proposes a novel synergistically optimized framework. The framework sequentially integrates three core modules to tackle the key challenges of load identification: SSA-VMD-based load quantity estimation, CNN-LSTM-Attention-based current separation, and GAF-ResNet18-based load recognition. First, the Sparrow Search Algorithm optimizes Variational Mode Decomposition parameters, combined with Pearson-PCA, to accurately estimate the number of operating loads in mixed-power signals without prior knowledge. Second, a hybrid CNN-LSTM-Attention model extracts deep spatial-temporal features from the aggregated current spectrogram, enabling high-fidelity separation and reconstruction of individual load current waveforms. Third, the separated current signals are transformed into Gramian Angular Field images and classified by a ResNet18 network for robust load identification. The framework’s efficacy is rigorously validated on both the public PLAID dataset and a self-constructed laboratory dataset, covering diverse dual-load and triple-load operating conditions. Results demonstrate that the method achieves R² coefficients exceeding 0.9 for current waveform reconstruction and maintains load recognition accuracy above 91% across all test cases, significantly improving identification performance under complex electricity consumption conditions. This high-performance load disaggregation provides critical data support for advanced grid applications, including demand response, load forecasting, and distribution network planning, thereby contributing to the intelligence and efficiency of future power systems. Full article

To further improve prediction accuracy, a VMD-VPPSO-LSTM model is proposed in this study, which combines Variational Mode Decomposition (VMD) for signal decomposition, Velocity-Pause Particle Swarm Optimization (VPPSO) for parameter optimization, and Long Short-Term Memory (LSTM) for runoff prediction. The model was evaluated at Huangtaiqiao station in the Xiaoqing River Basin, Dawenkou station in the Dawen River Basin, and Tangnaihai station in the source region of the Yellow River Basin. The proposed model achieved the best overall performance among all comparison models, with Nash–Sutcliffe Efficiency (NSE) values of 0.970, 0.962, and 0.994 and Root Mean Square Error (RMSE) values of 1.357, 0.989, and 46.804 at the three stations, respectively. Compared with VMD-LSTM, VPPSO further reduced the RMSE at all stations and maintained training-test NSE gaps below 0.006, indicating strong generalization performance. The model also achieved the lowest Peak Percent Standard Deviation (PPSD) values for high-flow events, reaching 9.03%, 14.42%, and 3.88% at the three stations, respectively. These results demonstrate that VMD-VPPSO-LSTM is a reliable and effective model for daily runoff prediction. Full article

Accurate forecasting of offshore wind power is essential for the stability of power systems, yet it remains challenging due to the strong non-stationarity and complex multivariate coupling of meteorological data. To address the tendency of error accumulation in medium- and long-term predictions, this paper proposes a novel framework, termed ISSAVMD-TCN-SOFTS, which integrates adaptive signal decomposition with lightweight deep temporal modeling. Specifically, an improved sparrow search algorithm, enhanced by Lévy flight and sine–cosine modulation mechanisms, is introduced to adaptively optimize the parameters of variational mode decomposition (VMD). This optimization ensures the robust decomposition of highly non-stationary power series. Furthermore, the framework combines the capability of temporal convolutional networks (TCN) to extract multiscale local temporal features with the efficiency of the STAR module in SOFTS for modeling global channel dependencies. Experiments on multi-site, multi-horizon SCADA data from real offshore wind farms show that the proposed model reduces MAE and RMSE by 10–45% compared with mainstream linear models, recurrent neural networks, and Transformer-based models, and maintains high stability over extended forecasting horizons. The results confirm that the integration of adaptive decomposition and collaborative temporal-channel modeling provides an effective solution for the accurate and stable forecasting of offshore wind power. Full article

Accurate short-term photovoltaic (PV) power forecasting is important for grid dispatch and PV integration. However, PV power under complex weather conditions has strong fluctuation, non-stationarity, and multi-frequency coupling. These features make accurate forecasting difficult. This paper proposes a short-term PV power forecasting model named WOA-MS-TFformer-BiTCN. The model first constructs similar-day samples through daily feature extraction, Gaussian mixture clustering, and physical consistency correction. Then, the whale optimization algorithm (WOA) optimizes the key parameters of variational mode decomposition (VMD) and the forecasting network. VMD decomposes the original power sequence into modes with different frequency features. The multi-scale frequency-domain perception (MS) module extracts multi-scale frequency-domain features from these modes. TFformer captures global temporal relationships, while BiTCN models local dynamic changes. Experiments are conducted using PV data from Gansu, China. The Alice Springs PV dataset is used for cross-regional validation. The results show that the proposed model achieves the lowest MAE, RMSE and the highest R² in all 16 season-weather cases, corresponding to four seasons and four weather types, for the 15 min-ahead task. Its average MAE, RMSE and the highest R² are 0.5439, 0.7910, and 0.99898, respectively. The model also performs best on rainy samples from the Alice Springs dataset. In addition, prediction intervals based on validation-set residual quantiles provide uncertainty information for point forecasts. The results show that the proposed method improves the accuracy and stability of short-term PV power forecasting under complex weather conditions. Full article

The integration of wind energy into power systems relies on forecasting technologies to address operational challenges caused by its volatility and intermittency. This paper proposes a computing architecture for ultra-short-term wind power forecasting. The methodology integrates an adaptive dual-stage signal processing technique with an optimized deep learning model. To manage the non-stationarity of meteorological variables, the Pearson and Maximal Information Coefficient (MIC) analyses are employed for feature selection. The ICEEMDAN algorithm is then used for initial decomposition, followed by sample entropy and K-Means clustering to assess component complexity. Variational Mode Decomposition (VMD) is applied only to the high-frequency component to further separate stochastic fluctuations while preserving relatively stable trend components. A Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) network is constructed to forecast the resulting multi-scale components. To reduce reliance on manual empirical tuning, the Crested Porcupine Optimizer (CPO) is used to fine-tune key network hyperparameters. Evaluations using operational wind-farm data indicate that the developed hybrid method captures the temporal dynamics of wind power and yields lower prediction errors than the tested benchmark models. This research provides a data-driven computing framework for renewable-energy forecasting and related operational analysis. Full article

To solve the low-precision prediction problem of noisy non-stationary goaf subsidence sequences, this study aims to establish a high-accuracy hybrid prediction model for mining surface deformation monitoring. The Global Navigation Satellite System (GNSS) monitoring data of surface subsidence in goaf areas exhibits non-stationary and noisy characteristics, which limits the accuracy of traditional prediction models. In this paper, a hybrid prediction model, namely the Improved Radial Movement Optimization–Variational Mode Decomposition–Gated Recurrent Unit (IRMO-VMD-GRU) model, is proposed. The IRMO algorithm is employed to globally optimize the key parameters of VMD, achieving adaptive and stable decomposition of the settlement sequences. The obtained Intrinsic Mode Function (IMF) sub-sequences are input into the GRU network for independent training and prediction, followed by superposition and reconstruction. The model is validated using the GNSS monitoring data from three monitoring points at a coal mine in Shaanxi Province, China. The results show that the proposed model outperforms the comparison models in all four evaluation indicators, namely Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), with all R² values exceeding 0.8. The model demonstrates superior fitting performance, correlation, and generalization ability, which provides important practical technical support for goaf subsidence early warning, geological disaster prevention and engineering safety management in mining areas. Full article

Accurate short-term load forecasting is essential for reliable power system operation under increasingly nonlinear, volatile, and multi-scale load patterns. This study proposes a VMD–BayesXGB–BiLSTM hybrid forecasting framework that integrates time-series-cross-validation-based variational mode decomposition (VMD), Bayesian-optimized XGBoost (BayesXGB), and BiLSTM residual correction. First, abnormal values in the raw load and explanatory variables are detected using the $3\sigma$ criterion and corrected by cubic spline interpolation. Then, VMD parameters are selected only within the training sequence, and leakage-free VMD features are generated from historical input windows, avoiding the use of future information. BayesXGB is employed as the primary forecasting model to capture nonlinear relationships between historical load, VMD-derived multi-scale features, and external variables. Finally, a stacked BiLSTM module learns temporal patterns from historical BayesXGB predictions and residuals, and the predicted residual correction is added to the preliminary forecast. Experiments on an Australian electricity load dataset show that the proposed model achieves an RMSE of 122.1003, an MAE of 90.7386, a MAPE of 1.0269%, and an R² of 0.9921, outperforming all compared baseline models while maintaining sub-millisecond inference per sample. Full article