Bilin Shao

Against the backdrop of the global energy transition toward deep electrification, the natural gas industry faces challenges, including increased load forecasting uncertainty and frequent extreme weather impacts. To enhance natural gas load forecasting accuracy and support system resilience planning, this study constructs a forecasting model based on quadratic decomposition and hybrid deep learning, incorporating an electricity substitution coefficient to characterize the coupling substitution effect between electricity and natural gas. Under the basic scenario, the VMD-WPD-TCN-BiGRU model is proposed. It employs variational mode decomposition and wavelet packet denoising for secondary signal denoising, combined with a time-series convolutional network and bidirectional gated recurrent unit to extract temporal features. Experiments demonstrate that, compared to mainstream methods such as CNN, BiLSTM, SVM, and XGBoost, this model achieves statistically significant reductions in MSE (11.11–96.21%), MAE (0.89–76.50%), and MAPE (4.10–67.94%), significantly improving forecasting accuracy. In the deep electrification scenario, the introduction of the electricity substitution coefficient further optimizes peak load forecasting for system pressure days under extreme low temperatures, elevating the overall R² to 0.9905 in the deep electrification scenario. Research indicates that the proposed model not only effectively improves the accuracy of short-term natural gas load forecasting but also provides quantitative support for enterprises to plan peak-shaving facilities, optimize pipeline networks, and respond to extreme weather emergencies in data silo environments. This contributes to strengthening the adaptability and long-term resilience of natural gas systems during the energy transition, thereby supporting the sustainable development of energy infrastructure.

As a high-risk production unit, natural gas supply enterprises are increasingly recognizing the need to enhance production safety management. Traditional process warning methods, which rely on fixed alarm values, often fail to adequately account for dynamic changes in the production process. To address this issue, this study utilizes deep learning techniques to enhance the accuracy and reliability of natural gas load forecasting. By considering the benefits and feasibility of integrating multiple models, a VMD-CNN-LSTM-Self-Attention interval prediction method was innovatively proposed and developed. Empirical research was conducted using data from natural gas field station outgoing loads. The primary model constructed is a deep learning model for interval prediction of natural gas loads, which implements a graded alarm mechanism based on 85%, 90%, and 95% confidence intervals of real-time observations. This approach represents a novel strategy for enhancing enterprise safety production management. Experimental results demonstrate that the proposed method outperforms traditional warning models, reducing MAE, MAPE, MESE, and REMS by 1.13096 m³/h, 1.3504%, 7.6363 m³/h, 1.6743 m³/h, respectively, while improving R² by 0.04698. These findings are expected to offer valuable insights for enhancing safe production management in the natural gas industry and provide new perspectives for the industry’s digital and intelligent transformation.

The acceleration of urbanization has significantly exacerbated climate change due to excessive anthropogenic carbon emissions and air pollutants. Based on data from 281 prefecture-level cities in China between 2015 and 2021. The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. Extreme gradient boosting (XGBoost) multiscale geographically weighted regression models(MGWR) and SHAP theory from game theory were employed to deeply investigate the disparities in relevance, spatial heterogeneity, and multiscale fluctuations of carbon emissions and air pollution. The main results are summarized as follows: (1) Between 2015 and 2018, CO₂ emissions exhibited significant fluctuations, while SO₂ and PM_2.5 concentrations decreased markedly. (2) The XGBoost-SHAP model identified seven key driving factors, demonstrating high precision, the SHAP model is used to explain the model and reveal the influence of driving factors on carbon emissions. (3) The concentrations of CO₂, SO₂, and PM_2.5 were positively correlated, the influence of each factor exhibited significant spatiotemporal differences, with varying directions of fluctuation across different regions. Thus, the symbiotic relationship between carbon emissions and air pollutants can inform decision-making for regional planning and sustainable urban development.

Blockchain technology, as an emerging distributed ledger technology, has gradually shown great potential in the field of information traceability due to its characteristics of decentralization, non-tamperability, and transparency. Through blockchain technology, the production, circulation, and consumption of products can be recorded and verified to ensure the authenticity and credibility of the data. This article reviews the application of blockchain in information traceability, focusing on analyzing its practice and effects in food safety, drug traceability, supply chain management, and energy industry traceability. Through a systematic review of existing literature, this article summarizes the research status, main results, and technical challenges of blockchain information traceability, points out its significant advantages in improving transparency and trust, and also discusses the implementation complexity, limitations in terms of cost, and performance. Finally, this article proposes future development trends, including multi-chain integration and the combination of blockchain with other technologies. The research in this article provides important reference and guidance for the further development of blockchain technology in information traceability.

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

The fluctuation and uncertainty of the electricity load bring challenges to load forecasting. Traditional point forecasting struggles to avoid errors, and pure interval forecasting may cause the problem of too wide an interval. In this paper, we combine point forecasting and interval forecasting and propose a point-interval forecasting model for electricity load based on regular fluctuation component extraction. Firstly, the variational modal decomposition is combined with the sample entropy to decompose the original load series into a strong regular fluctuation component and a weak regular fluctuation component. Then, the gate recurrent unit neural network is used for point forecasting of the strong regular fluctuation component, and the support vector quantile regression model is used for interval forecasting of the weak regular fluctuation component, and the results are accumulated to obtain the final forecasting intervals. Finally, experiments were conducted using electricity load data from two regional electricity grids in Shaanxi Province, China. The results show that combining the idea of point interval, point forecasting, and interval forecasting for components with different fluctuation regularity can effectively reduce the forecasting interval width while having high accuracy. The proposed model has higher forecasting accuracy and smaller mean interval width at various confidence levels compared to the commonly used models.

With the spread of cloud storage technology, checking the integrity of data stored in the cloud effectively is increasingly becoming a concern. Following the introduction of the first remote data integrity audit schemes, different audit schemes with various characteristics have been proposed. However, most of the existing solutions have problems such as additional storage overhead and additional certificate burden. This paper proposes a certificateless remote data integrity auditing scheme which takes into account the storage burden and data privacy issues while ensuring the correctness of the data audit results. In addition, the certificateless design concept enables the scheme proposed in this paper to avoid a series of burdens brought by certificates. The scheme designed in this paper provides a data access control function whereby only users who hold a valid token generated by the data owner can access the target data from the cloud. Finally, this paper provides a detailed security proof to ensure the rationality of the results. A theoretical analysis and subsequent experimental verification show that the proposed scheme is both effective and feasible.

Accurate short-term load forecasting can ensure the safe operation of the grid. Decomposing load data into smooth components by decomposition algorithms is a common approach to address data volatility. However, each component of the decomposition must be modeled separately for prediction, which leads to overly complex models. To solve this problem, a VMD-WSLSTM load prediction model based on Shapley values is proposed in this paper. First, the Shapley value is used to select the optimal set of special features, and then the VMD decomposition method is used to decompose the original load into several smooth components. Finally, WSLSTM is used to predict each component. Unlike the traditional LSTM model, WSLSTM can simplify the prediction model and extract common features among the components by sharing the parameters among the components. In order to verify the effectiveness of the proposed model, several control groups were used for experiments. The results show that the proposed method has higher prediction accuracy and training speed compared with traditional prediction methods.

Due to their characteristics of high energy storage density and a nearly constant melting temperature, phase change materials (PCMs) could be inserted into the roof of green buildings in order to reduce the energy consumption and ameliorate the room thermal comfort. In this paper, an enthalpy based multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) was developed to calculate the transient phase change conjugate heat transfer with solar radiation inside the green building’s PCM roof in the hot summer and cold winter areas of China. The effect of the PCM melting temperature on the variation of the roof internal temperature was investigated and the energy saving characteristic of the PCM roof under an intermittent energy utilization condition was also analyzed by comparing with the performance of the roof filled with sensible insulating materials (SIMs). Then, the life cycle incremental costs and incremental benefits of a PCM roof and SIM roof were studied by using the comprehensive incremental benefit model so that the green building roof could be economically evaluated. The results indicate that a temperature rise inside the roof during summer cooling time could be delayed due to the latent heat of the PCMs. It was also found that the melting temperature and the thickness of the PCM layer should be chosen appropriately for enhancing the energy saving amount of a PCM roof. Based on this, the PCM roof could have a better energy saving capability than the SIM roof. During the winter heating time, as the environment temperature and the room temperature are both below the PCM melting temperature, the PCM roof does not have a latent heat characteristic so that it performs like a SIM roof. Furthermore, due to the high price of PCMs, the incremental cost of green building is increased, which makes the PCM roof have a negative comprehensive incremental benefit. Under this circumstance, developing PCMs with a low price and stable chemical properties is a key scientific bottleneck for a wider application of PCM roofs in the architecture engineering field.

The amount of encoded data replication in an erasure-coded clustered storage system has a great impact on the bandwidth consumption and network latency, mostly during data reconstruction. Aimed at the reasons that lead to the excess data transmission between racks, a rack aware data block placement method is proposed. In order to ensure rack-level fault tolerance and reduce the frequency and amount of the cross-rack data transmission during data reconstruction, the method deploys partial data block concentration to store the data blocks of a file in fewer racks. Theoretical analysis and simulation results show that our proposed strategy greatly reduces the frequency and data volume of the cross-rack transmission during data reconstruction. At the same time, it has better performance than the typical random distribution method in terms of network usage and data reconstruction efficiency.