Search Results (13)

Renewable power generation is unpredictable due to its intermittency, making grid-connected microgrids difficult to operate, control, and manage. Currently used prediction models for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with the carbon trading mechanism are inefficient as they cannot account for all eventualities and are not well studied. Therefore, a two-stage robust optimisation model based on Bidirectional Temporal Convolutional Networks (BiTCN) and Transformer prediction for electricity, heat, gas, and hydrogen multi-energy complementary microgrids with a carbon trading mechanism is proposed to solve this problem. First, BiTCN extracts implicit wind speed and wind power output sequences from historical data and feeds it into the Transformer model for point prediction using the attention mechanism. Ablation computation modelling is then performed. The proposed prediction model’s Mean Absolute Error (MAE) is found to be 1.3512, and its R² is 0.9683, proving its efficacy and reliability. Second, the proposed model is used to perform interval prediction in two typical scenarios: high wind power and low wind power. After constructing the robust optimisation model uncertainty set based on the prediction results, simulation experiments are performed on the proposed optimisation model. The simulation results suggest that the proposed optimisation model enhances renewable energy use, emissions reductions, microgrid operating costs, and system reliability. The study also reveals that the total system cost and carbon emission cost in the low wind scenario are 283% (2.83 times) and 314% (3.14 times) higher than in the high wind scenario; hence, a significant percentage of renewable energy is needed for microgrid stability. Full article

Aiming at improving the prediction accuracy of the gas dissolved in transformer oil which occurs with strong nonlinearity, this paper presents a method named CEEMDAN-PWOA-VMD-BIGRU for gas content prediction. First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to decompose the original gas sequence. To solve the problem of the strong nonlinear characteristic of the decomposed high-frequency components leads to a large error in prediction, this paper uses Variational Mode Decomposition (VMD) for secondary decomposition. Though VMD can decompose high-frequency modes well, the selection of the optimal decomposition number and the quadratic penalty factors often depends on subjective judgment, which may affect the accuracy of decomposition results. Therefore, Whale Optimization Algorithm (WOA) is applied to optimize the parameter setting of VMD. However, the search of WOA in the optimization process is random, which leads to the limitations of the optimization efficiency. To solve this problem, this paper further uses Proximal Policy Optimization (PPO) to improve WOA (PWOA). With the optimized parameters of PWOA, VMD obtains more accurate secondary decomposition results. Then, the trained Bidirectional Gated Recurrent Unit (BiGRU) model is used to predict each decomposed component, and finally these predicted components are reconstructed to obtain more accurate prediction results. The experimental results demonstrate that the mean absolute error (MAE) of the proposed model is reduced by 6.88%, 7.45%, and 5.69%, compared with the traditional algorithms of Long Short-term Memory network (LSTM), Gated Recurrent Unit (GRU), and Temporal Convolution Network (TCN), respectively. Full article

This study addresses the problems of multi-source data redundancy, insufficient feature capture timing, and delayed risk warning in the prediction of gas concentration in fully mechanized coal-mining operations by constructing a three-pronged technical approach that integrates feature dimensionality reduction, hybrid modeling, and intelligent early warning. First, sparse kernel principal component analysis (SKPCA) is used to accomplish the feature decoupling of multi-source monitoring data, and its optimal dimensionality reduction performance is verified using long-term and short-term neural networks (LSTMs). Second, an innovative TCN–Transformer hybrid architecture is proposed. The transient fluctuation characteristics of gas concentration are captured using causal dilation convolution, while a multi-head self-attention mechanism is used to analyze the cross-scale correlation of geological mining parameters. A flood optimization algorithm (FLA) is used to establish a hyperparameter collaborative optimization framework. Compared to TCN-LSTM, CNN-GRU, and other hybrid models, the hybrid model proposed in this study exhibits superior point prediction performance, with a maximum R² of 0.980988. Finally, a dynamic confidence interval is established using the locally weighted kernel density estimation (LWD-KDE) method with an optimized bandwidth, and an unsupervised early warning mechanism for the risk of gas concentration fluctuations in coal mines is constructed. The results provide a comprehensive approach to preventing and controlling gas disasters in fully mechanized mining operations. This research effectively promotes the transformation and upgrading of coal-mine-safety-monitoring systems to an active defense paradigm. Full article

The rapid expansion of international maritime transportation has led to rising greenhouse gas emissions, exacerbating climate change and environmental sustainability concerns. According to the International Maritime Organization, carbon dioxide (CO₂) emissions from vessels are projected to increase by over 17% by 2050. Traditional emission estimation methods are prone to inaccuracies due to uncertainties in emission factors, and inconsistencies in fuel consumption data. This study proposes deep learning-based CO₂ emission prediction models leveraging engine operation data. Unlike previous approaches that primarily relied on fuel consumption, this model incorporates multiple parameters capturing the relationship between combustion characteristics and emissions to enhance predictive accuracy. We developed and evaluated individual models—convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN)—as well as hybrid model (TCN–LSTM). The hybrid model achieved the highest predictive performance, with a coefficient of determination of 0.9726, outperforming other models across multiple quantitative metrics. These findings demonstrate the potential of deep learning for vessel emission assessment, providing a scientific basis for carbon management strategies and policy development in the international shipping industry. This study thus holds major academic and industrial value, advancing the field of deep learning-based emission prediction and extending its applicability to diverse operational scenarios. Full article

Achieving accurate and efficient ship-type recognition is crucial for the development and management of modern maritime traffic systems. To overcome the limitations of existing methods that rely solely on AIS time series data or navigation trajectory images as single-modal approaches, this study introduces TrackAISNet, a multimodal ship classification model that seamlessly integrates ship trajectory images with AIS time series data for improved performance. The model employs a parallel structure, utilizing a lightweight neural network to extract features from trajectory images, and a specially designed TCN-GA (Temporal Convolutional Network with Global Attention) to capture the temporal dependencies and long-range relationships in the AIS time series data. The extracted image features and temporal features are then fused, and the combined features are fed into a classification network for final classification. We conducted experiments on a self-constructed dataset of variable-length AIS time series data comprising four types of ships. The results show that the proposed model achieved an accuracy of 81.38%, recall of 81.11%, precision of 80.95%, and an F1 score of 81.38%, outperforming the benchmark single-modal algorithms. Additionally, on a publicly available dataset containing three types of fishing vessel operations, the model demonstrated improvements in accuracy, recall, and F1 scores by 5.5%, 4.88%, and 5.88%, respectively. Full article

Effective production prediction is vital for optimizing energy resource management, designing efficient extraction strategies, minimizing operational risks, and informing strategic investment decisions within the energy sector. This paper introduces a Dual-Stage Attention Temporal Convolutional Network (DA-TCN) model to enhance the accuracy and efficiency of gas production forecasting, particularly for wells in tight sandstone reservoirs. The DA-TCN architecture integrates feature and temporal attention mechanisms within the Temporal Convolutional Network (TCN) framework, improving the model’s ability to capture complex temporal dependencies and emphasize significant features, resulting in robust forecasting performance across multiple time horizons. Application of the DA-TCN model to gas production data from two wells in Block T of the Sulige gas field in China demonstrated a 19% improvement in RMSE and a 21% improvement in MAPE compared to traditional TCN methods for long-term forecasts. These findings confirm that dual-stage attention not only increases predictive accuracy but also enhances forecast stability over short-, medium-, and long-term horizons. By enabling more reliable production forecasting, the DA-TCN model reduces operational uncertainties, optimizes resource allocation, and supports cost-effective management of unconventional gas resources. Leveraging existing knowledge, this scalable and data-efficient approach represents a significant advancement in gas production forecasting, delivering tangible economic benefits for the energy industry. Full article

In the process of oil and natural gas exploration and development, density logging curves play a crucial role, providing essential evidence for identifying lithology, calculating reservoir parameters, and analyzing fluid properties. Due to factors such as instrument failure and wellbore enlargement, logging data for some well segments may become distorted or missing during the actual logging process. To address this issue, this paper proposes a density logging curve reconstruction model that integrates the multi-head self-attention mechanism (MSA) with temporal convolutional networks (TCN) and bidirectional gated recurrent units (BiGRU). This model uses the distance correlation coefficient to determine curves with a strong correlation to density as a model input parameter and incorporates stratigraphic lithology indicators as physical constraints to enhance the model’s reconstruction accuracy and stability. This method was applied to reconstruct density logging curves in the X depression area, compared with several traditional reconstruction methods, and verified through core calibration experiments. The results show that the reconstruction method proposed in this paper exhibits high accuracy and generalizability. Full article

To tackle the complex challenges inherent in gas turbine fault diagnosis, this study uses powerful machine learning (ML) tools. For this purpose, an advanced Temporal Convolutional Network (TCN)–Autoencoder model was presented to detect anomalies in vibration data. By synergizing TCN capabilities and Multi-Head Attention (MHA) mechanisms, this model introduces a new approach that performs anomaly detection with high accuracy. To train and test the proposed model, a bespoke dataset of CA 202 accelerometers installed in the Kirkuk power plant was used. The proposed model not only outperforms traditional GRU–Autoencoder, LSTM–Autoencoder, and VAE models in terms of anomaly detection accuracy, but also shows the Mean Squared Error (MSE = 1.447), Root Mean Squared Error (RMSE = 1.193), and Mean Absolute Error (MAE = 0.712). These results confirm the effectiveness of the TCN–Autoencoder model in increasing predictive maintenance and operational efficiency in power plants. Full article

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction. Full article

Trajectory prediction (TP) is a vital operation in air traffic control systems for flight monitoring and tracking. The approach phase of general aviation (GA) aircraft is more of a visual approach, which is related to the safety of the flight and whether to go around. Therefore, it is important to accurately predict the flight trajectory of the approach phase. Based on the historical flight trajectories of GA aircraft, a TP model is proposed with deep learning after feature extraction in this study, and the hybrid model combines a time convolution network and an improved transformer model. First, feature extraction of the spatiotemporal dimension is performed on the preprocessed flight data by using TCN; then, the extracted features are executed by adopting the Informer model for TP. The performance of the novel architecture is verified by experiments based on real flight trajectory data. The results show that the proposed TCN-Informer architecture performs better according to various evaluation metrics, which means that the prediction accuracies of the hybrid model are better than those of the typical prediction models widely used today. Moreover, it has been verified that the proposed method can provide valuable suggestions for decision-making regarding whether to go around during the approach. Full article

A multi-strategy improved sand cat swarm algorithm with PTCN-BIGRU is proposed to solve the problem of predicting the risk level of gas-containing coal fracture. Combined with kernel entropy component analysis to downscale the gas-containing coal fracture risk level predictors, TCN is used for feature extraction by parallel convolution operation, and BiGRU is used to further obtain the contextual links of the features. A parameterized exponential linear unit based on the standard TCN is used to improve the linear unit and to enhance the generalization capability of the model. Combined with the sand cat swarm optimization algorithm to determine the optimal BIGRU network parameters, Singer chaos mapping, chaos decreasing factor, and adaptive t-distribution are used to improve the SCSO for optimal risk level prediction accuracy. The results show that the prediction accuracy of the ISCSO-PTCN-BiGRU model is 93.33%, which is better than other models, and it is proved that this paper can effectively improve the prediction accuracy of gas-containing coal fracture risk level. This research adds a theoretical support for the prevention of gas protrusion accidents and a guarantee for the safety of underground production in coal mines. Full article

Sulfur dioxide (SO₂) can cause detrimental impacts on the ecosystem. It is well known that coal-fired power plants play a dominant role in SO₂ emissions, and consequently industrial flue gas desulfurization (IFGD) systems are widely used in coal-fired power plants. To remove SO₂ effectively such that ultra-low emission standard can be satisfied, IFGD modeling has become urgently necessary. IFGD is a chemical process with long-term dependencies between time steps, and it typically exhibits strong non-linear behavior. Furthermore, the process is rendered non-stationary due to frequent changes in boiler loads. The above-mentioned properties make IFGD process modeling a truly formidable problem, since the chosen model should have the capability of learning long-term dependencies, non-linear dynamics and non-stationary processes simultaneously. Previous research in this area fails to take all the above points into account at a time, and this calls for a novel modeling approach so that satisfactory modeling performance can be achieved. In this work, a novel bivariate empirical mode decomposition (BEMD)-based temporal convolutional network (TCN) approach is proposed. In our approach, BEMD is employed to generate relatively stationary processes, while TCN, which possesses long-term memory ability and uses dilated causal convolutions, serves to model each subprocess. Our method was validated using the operating data from the desulfurization system of a coal-fired power station in China. Simulation results show that our approach yields desirable performance, which demonstrates its effectiveness in the IFGD dynamic modeling problem. Full article

Natural gas is often said to be the most environmentally friendly fossil fuel. Its usage has increased significantly in recent years. Meanwhile, accurate forecasting of natural gas spot prices has become critical to energy management, economic growth, and environmental protection. This work offers a novel model based on the temporal convolutional network (TCN) and dynamic learning rate for predicting natural gas spot prices over the following two weekdays. The residual block structure of TCN provides good prediction accuracy, and the dilated causal convolutions minimize the amount of computation. The dynamic learning rate setting was adopted to enhance the model’s prediction accuracy and robustness. Compared with three existing models, i.e., the one-dimensional convolutional neural network (1D-CNN), gate recurrent unit (GRU), and long short-term memory (LSTM), the proposed model can achieve better performance over other models with mean absolute percentage error (MAPE), mean absolute error (MAE), and root mean squared error (RMSE) scores of 4.965%, 0.216, and 0.687, respectively. These attractive advantages make the proposed model a promising candidate for long-term stability in natural gas spot price forecasting. Full article