Abstract Accurate power load forecasting is crucial for the sustainable operation of smart grids. However, the complexity and uncertainty of load, along with the large-scale and high-dimensional ...energy information, present challenges in handling intricate dynamic features and long-term dependencies. This paper proposes a computational approach to address these challenges in short-term power load forecasting and energy information management, with the goal of accurately predicting future load demand. The study introduces a hybrid method that combines multiple deep learning models, the Gated Recurrent Unit (GRU) is employed to capture long-term dependencies in time series data, while the Temporal Convolutional Network (TCN) efficiently learns patterns and features in load data. Additionally, the attention mechanism is incorporated to automatically focus on the input components most relevant to the load prediction task, further enhancing model performance. According to the experimental evaluation conducted on four public datasets, including GEFCom2014, the proposed algorithm outperforms the baseline models on various metrics such as prediction accuracy, efficiency, and stability. Notably, on the GEFCom2014 dataset, FLOP is reduced by over 48.8%, inference time is shortened by more than 46.7%, and MAPE is improved by 39%. The proposed method significantly enhances the reliability, stability, and cost-effectiveness of smart grids, which facilitates risk assessment optimization and operational planning under the context of information management for smart grid systems.
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Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the ...performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistically combines the power of the Savitzky-Golay (SG) filter, the TCN, as well as the LSTM. ST-LSTM employs a three-phase end-to-end methodology serving time series prediction. It first eliminates noise in raw data using the SG filter, then extracts short-term features from sequences applying the TCN, and then captures the long-term dependence in the data exploiting the LSTM. Experimental results over real-world datasets demonstrate that the proposed ST-LSTM outperforms state-of-the-art algorithms in terms of prediction accuracy.
Due to the complex working conditions and harsh environment, wind turbines often encounter abnormalities, resulting in great operation and maintenance difficulties. As nacelle vibration signals ...reveal the structure's dynamic characteristics and the interaction between components, vibration anomaly detection has a strong potential. However, vibration anomaly detection is challenging due to the complex characteristics and non-stationarity of high dynamic nacelle vibration signals. To solve this problem, this paper proposed a semi-supervised vibration anomaly detection approach for wind turbines, combining deep learning and one-class classification. Firstly, a healthy behavior model (HBM) for predicting wind turbine nacelle vibration based on the temporal convolutional network (TCN) is developed. To use all available information, a Hilbert spectrum fusion technology (HSFT) was proposed to enhance model performance. Then, based on the support vector data description (SVDD) algorithm, we established a one-class classifier of vibration prediction residual and realized the vibration anomaly detection. The proposed approach can be trained on the healthy dataset to provide accurate detection of different abnormal types. The effectiveness of the proposed anomaly detection approach was verified on simulated and actual monitoring datasets.
•Semi-supervised vibration anomaly detection approach for wind turbine.•Temporal convolutional network-based healthy behavior model.•One class classification model for adjustable anomaly detection performance.•Variation of abnormal monitoring results correlated with abnormalities degree.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid ...stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA.
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The ongoing COVID-19 pandemic has created an unprecedented predicament for global supply chains (SCs). Shipments of essential and life-saving products, ranging from pharmaceuticals, agriculture, and ...healthcare, to manufacturing, have been significantly impacted or delayed, making the global SCs vulnerable. A better understanding of the shipment risks can substantially reduce that nervousness. Thenceforth, this paper proposes a few Deep Learning (DL) approaches to mitigate shipment risks by predicting ”if a shipment can be exported from one source to another”, despite the restrictions imposed by the COVID-19 pandemic. The proposed DL methodologies have four main stages: data capturing, de-noising or pre-processing, feature extraction, and classification. The feature extraction stage depends on two main variants of DL models. The first variant involves three recurrent neural networks (RNN) structures (i.e., long short-term memory (LSTM), Bidirectional long short-term memory (BiLSTM), and gated recurrent unit (GRU)), and the second variant is the temporal convolutional network (TCN). In terms of the classification stage, six different classifiers are applied to test the entire methodology. These classifiers are SoftMax, random trees (RT), random forest (RF), k-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM). The performance of the proposed DL models is evaluated based on an online dataset (taken as a case study). The numerical results show that one of the proposed models (i.e., TCN) is about 100% accurate in predicting the risk of shipment to a particular destination under COVID-19 restrictions. Unarguably, the aftermath of this work will help the decision-makers to predict supply chain risks proactively to increase the resiliency of the SCs.
•Proposes a wide range of DL approaches to predict supply chain risks.•Introducing a temporal convolutional network (TCN) by combining RNN and CNN models.•Identification of the most promising DL approach in terms of performance.•Performance demonstration of the selected classifiers on the advanced DL networks.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A new combination forecasting model is proposed to improve the ultra-short-term forecast accuracy of traction load.•The design concept of the combined model proved to be reasonable.•This model can ...get the changing trend of the predicted load in different time scales.
The ultra-short-term prediction of traction load is a key aspect of electric power control in electrified railroads. In order to provide more accurate traction load prediction data for the control process, this paper designs a combined prediction method combining Discrete Wavelet Transform(DWT), Temporal Convolutional Network (TCN) and Support Vector Regression based on Particle Swarm Optimization(PSO_SVR) for the characteristics of traction load with strong random fluctuation, large jump amplitude and frequent no-load. This method will use DWT model to decompose the traction load with strong random fluctuation and difficult to predict into sub-series with single fluctuation frequency and easy to predict; then, according to the frequency difference of different series, TCN model is selected to predict medium and low frequency series. The SVR model is selected to predict the high frequency sequences. Finally, the prediction results of each sub-sequence are summed to obtain the final prediction. The combined prediction model designed in this paper is used to predict the traction load of traction power supply stations. The experimental results show that the prediction method proposed in this paper has higher prediction accuracy than the existing prediction methods in the current prediction field.
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Due to the serious problem of the world’s aging population and the harm caused by unintentional falls to elderly individuals, the question of how to precisely identify falls has steadily attracted ...the public’s interest. In this article, a novel deep learning (DL) model, a combination model of a temporal convolutional network and gated recurrent unit (TCN-GRU) architecture, is proposed to obtain high-level features for classification. We evaluate its relative performance against two widely used machine learning (ML) based classifiers and six DL architectures using two popular open-source datasets collected using inertial sensors. Our algorithm results show that the proposed method outperformed other algorithms in nearly all four performance metrics we examined, for the datasets we tested. For the MobiAct dataset and Mosi-F dataset (which is a mixture of the MobiAct dataset with the Sisfall dataset), the prediction accuracy reached 99.5% and 97.6%, and the F1_Score reached 98.9% and 97.6%, respectively, demonstrating satisfactory performance. Moreover, the proposed algorithm had higher detection accuracy despite a small data volume, and it correctly detected all types of fall events from ten primary daily activity groups.
•A novel model is proposed to capture the different elderly activities accurately.•A new enriching fall-detection classifier with less motion information is proposed.•The method is better with superior classification accuracy and better convergence.
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Acoustic and articulatory signals are naturally coupled and complementary. The challenge of acquiring articulatory data and the nonlinear ill-posedness of acoustic–articulatory conversions have ...resulted in previous studies on speech emotion recognition (SER) primarily relying on unidirectional acoustic–articulatory conversions. However, these studies have ignored the potential benefits of bi-directional acoustic–articulatory conversion. Addressing the problem of nonlinear ill-posedness and effectively extracting and utilizing these two modal features in SER remain open research questions. To bridge this gap, this study proposes a Bi-A2CEmo framework that simultaneously addresses the bi-directional acoustic–articulatory conversion for SER. This framework comprises three components: a Bi-MGAN that addresses the nonlinear ill-posedness problem, KCLNet that enhances the emotional attributes of the mapped features, and ResTCN-FDA that fully exploits the emotional attributes of the features. Another challenge is the absence of a parallel acoustic–articulatory emotion database. To overcome this issue, this study utilizes electromagnetic articulography (EMA) to create a multi-modal acoustic–articulatory emotion database for Mandarin Chinese called STEM-E2VA. A comparative analysis is then conducted between the proposed method and state-of-the-art models to evaluate the effectiveness of the framework. Bi-A2CEmo achieves an accuracy of 89.04% in SER, which is an improvement of 5.27% compared with the actual acoustic and articulatory features recorded by the EMA. The results for the STEM-E2VA dataset show that Bi-MGAN achieves a higher accuracy in mapping and inversion than conventional conversion networks. Visualization of the mapped features before and after enhancement reveals that KCLNet reduces the intra-class spacing while increasing the inter-class spacing of the features. ResTCN-FDA demonstrates high recognition accuracy on three publicly available datasets. The experimental results show that the proposed bi-directional acoustic–articulatory conversion framework can significantly improve the SER performance.
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The novelty of this research is as follows:(1)A new transportable cross-domain adaptation method is developed.(2)A residual self-attention is proposed to fully consider the contextual degradation ...information.(3)The contrastive loss is introduced to improve the complex transformation invariance of MK-MMD mapping.
Many data-driven models normally assume that the training and test data are independent and identically distributed to predict the remaining useful life (RUL) of industrial machines. However, different failure models caused by variable failure behaviors may lead to a domain shift. Meanwhile, conventional methods lack comprehensive attention to temporal information, resulting in a limitation. To handle the aforementioned challenges, a transferable cross-domain approach for RUL estimation is proposed. The hidden features are extracted adaptively by a temporal convolution network in which residual self-attention is able to fully consider the contextual degradation information. Furthermore, a new cross-domain adaption architecture with the contrastive loss and multi-kernel maximum mean discrepancy is designed to learn the domain invariant features. The effectiveness and superiority of the proposed method are proved by the case study on IEEE PHM challenge 2012 bearing dataset and the comparison with other methods.
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