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Cloud-edge collaborative and graph neural network based load forecasting method for electric vehicle charging stations DENG Bin, ZHANG Zongbao, ZHAO Wenmeng, LUO Xinhang, WU Qiuwei Journal of Shandong University(Engineering Science)    2025, 55 (5): 62-69.   DOI: 10.6040/j.issn.1672-3961.0.2024.219 Abstract （2682）      PDF（pc） （2415KB）（783）       Knowledge map    Save Aiming at the problems of privacy protection, computational efficiency, and predictive accuracy in existing forecasting methods for electric vehicle charging stations, a cloud-edge collaborative and graph neural network based load forecasting approach was proposed. A privacy preserving module based on embedding is developed in the cloud, which reconstructs the input data through embedding transformation to prevent potential privacy leakage risks. A method for generating representation with graph structure based on clustering is proposed to provide additional spatiotemporal information and achieve more accurate forecasting. Personalized graph neural network forecasting models are designed for clients based on cloud's graph structure representation, enabling collaborative training of electric vehicle charging stations in different regions while protecting privacy. Experimental results on the Perth dataset demonstrate that the model outperforms benchmark methods in predictive accuracy and that the cloud-edge collaborative framework proposed in this study significantly enhances the performance of graph neural network algorithms in the task of load forecasting for electric vehicle charging stations. Reference | Related Articles | Metrics

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Robust unit commitment model with multi-energy coupled system considering gas-heat network dynamics ZHANG Yumin, LI Jingrui, YANG Ming, JI Xingquan, SUN Donglei, XU Bo, WU Fucheng Journal of Shandong University(Engineering Science)    2025, 55 (5): 18-29.   DOI: 10.6040/j.issn.1672-3961.0.2024.163 Abstract （1945）      PDF（pc） （10507KB）（85）       Knowledge map    Save The inherent intermittency and uncertainty of renewable energy sources had a challenge to operation decisions of the system. To solve this problem, a robust unit commitment model with multi-energy coupled system considering gas-heat network dynamics was proposed. The mathematical expressions that characterize the dynamic characteristics of gas network and thermal network were established, which were incorporated into the robust unit commitment optimization model of multi-energy coupled system. A multi-dimensional uncertainty set from the perspectives of interval, time, and space to achieve flexible adjustment of wind power absorption boundaries was established. At the same time, concentrating solar power was used to replace the output of some thermal power units to further improve the utilization rate of renewable energy. The column-and-constraint generation algorithm was employed to transform the established min-max-min structure optimization model into a mixed-integer linear programming master-subproblem form for optimization, improving the solution speed of the model. The effectiveness of the proposed model and method was verified on 6-6-8 and 118-20-16 electricity-gas-heat systems, with results indicating that the dynamic characteristics of gas and heat networks can improve the economy of system operation and the utilization rate of renewable energy. Reference | Related Articles | Metrics

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Inverse analysis on the softening curve of steel slag fine aggregate concrete XUE Gang, ZHANG Yifan, LIU Jiangsen, DONG Wei Journal of Shandong University(Engineering Science)    2025, 55 (6): 120-128.   DOI: 10.6040/j.issn.1672-3961.0.2024.249 Abstract （1687）      PDF（pc） （11966KB）（37）       Knowledge map    Save In order to study the influence of steel slag fine aggregate volume fraction on the softening characteristics of concrete, the steel slag fine aggregate with stability meeting the specification limit requirements was selected to prepare concrete for wedge splitting test, and the inverse analysis program was established by ISIGHT integrated MATLAB and ABAQUS, and the trilinear softening curve of steel slag fine aggregate concrete was deduced, and the accuracy of finite element analysis of the softening curve was verified based on the wedge splitting tensile test results. The results showed that steel slag could significantly improve the tensile strength of concrete and accelerate the stress reduction of concrete after cracking. When the steel slag volume fraction was 20%, the slope of the second segment of the trilinear softening curve decreased the most. The cracking displacement of concrete at the end of the softening curve decreased with the increase of steel slag volume fraction, and the ductility of concrete deteriorated. The softening curve of steel slag fine aggregate concrete provided a theoretical basis for studying its softening performance. Reference | Related Articles | Metrics

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Study on associations between serum per- and polyfluoroalkyl substances levels and blood pressure in residents of Jinan ZHANG Haoyu, XU Fei, LIU Yi, HOU Chengxi, DING Lei Journal of Shandong University(Engineering Science)    2025, 55 (4): 160-172.   DOI: 10.6040/j.issn.1672-3961.0.2024.297 Abstract （834）      PDF（pc） （5752KB）（46）       Knowledge map    Save Human was exposed to per- and polyfluoroalkyl substances(PFASs), which were implicated to be associated with elevated prevalence of hypertension. To evaluate the relationships between individual PFAS and PFAS mixture with blood pressure levels and hypertension risk, 18 PFASs in fasting serum samples collected from 326 individuals in Jinan, China were analyzed with an ultrahigh performance liquid chromatography system coupled with an Orbitrap mass spectrometer. Multivariable linear regression and logistic regression models were utilized to analyze the associations between individual PFAS and systolic blood pressure, diastolic blood pressure, and the risk of hypertension, respectively. To evaluate the joint effects of PFAS mixture, quantile g-computation and Bayesian kernel machine regression models were applied. All the models indicated a positive association between perfluorodecanoic acid mass concentration and diastolic blood pressure, a negative association between perfluorododecanoic acid mass concentration and diastolic blood pressure, and a positive association between perfluoroundecanoic acid mass concentration and risk of hypertension. According to a series of results from this study, it was concluded that both diastolic blood pressure and the risk of hypertension increased with the percentile of PFAS mixture mass concentration among the study population. Reference | Related Articles | Metrics

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Prediction model of reinforced concrete resistivity based on finite element simulation JIA Liujian, HU Jie, BIAN Leixiang, XU Zhan, SHI Haotian, WANG Chong, LIU Hailong Journal of Shandong University(Engineering Science)    2026, 56 (2): 76-81.   DOI: 10.6040/j.issn.1672-3961.0.2024.300 Abstract （424）      PDF（pc） （4505KB）（23）       Knowledge map    Save In order to accurately analyze the information of underground artificial structures from transient electromagnetic signals and improve the accuracy of inversion imaging, it was necessary to clearly set the initial value of underground artificial medium resistivity to extract its response. Based on the finite element analysis, the resistivity law of reinforced concrete obtained by the four-electrode method was studied, and the influence of reinforced concrete thickness and steel bar spacing on resistivity was revealed. The influence of electrode spacing on the test results was analyzed, and the optimal electrode arrangement method and prediction function model were proposed. The reinforced concrete under different working conditions was prepared and tested. The experimental data were compared with the predicted values and the mean square error was analyzed to verify the advantages of the model. Reference | Related Articles | Metrics

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Short-term wind power prediction model based on spatial-temporal graph convolutional network with dual-graph structure ZHENG Zheming, KONG Lingling, HE Yin Journal of Shandong University(Engineering Science)    2026, 56 (2): 130-138.   DOI: 10.6040/j.issn.1672-3961.0.2025.030 Abstract （399）      PDF（pc） （3999KB）（34）       Knowledge map    Save To address the limitations of traditional wind power prediction methods that ignored the interaction of spatial-temporal features, a spatial-temporal graph convolutional network with attention(STGCN-A)was proposed. A correlation matrix was constructed by the maximal information coefficient to form a statistical correlation-based spatial graph, while an Euclidean distance-based geographic proximity spatial graph was built to achieve dual-graph modeling among wind turbines. Spatial features were extracted through a graph convolutional network(GCN), and temporal dependencies were captured by a gated recurrent unit(GRU). An attention mechanism(AM)was introduced to dynamically weight different time steps, enhancing the representation of critical information in spatial-temporal features. Comparative experiments on real wind power datasets demonstrated that the proposed model outperformed traditional methods in terms of root mean square error(ERMS), mean absolute error(EMA), and coefficient of determination(R2). The results indicated higher prediction accuracy and strong potential for practical applications. Reference | Related Articles | Metrics

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Comparison on manual and automatic monitoring of greenhouse gases from fixed pollution sources in sewage treatment plants and waste incineration power plants LIU Tiedong, LIN Na, XIE Tingting, LENG Yaling, YAO Tingting, MA Zhitong, ZHAO Hongxia Journal of Shandong University(Engineering Science)    2026, 56 (2): 181-188.   DOI: 10.6040/j.issn.1672-3961.0.2024.298 Abstract （395）      PDF（pc） （1836KB）（45）       Knowledge map    Save In order to solve the monitoring difficulties of greenhouse gas emissions in industries such as sewage treatment plants and municipal waste incineration power plants, break the dependence of monitoring equipment performance indicators on foreign countries, and establish a traceability system for N2O and CH4 monitoring equipment, by manual monitoring and portable device, this article analyzed the differences in mass concentration of N2O and CH4 emissions through two methods: manual monitoring and portable device measurements. Results showed that, at three different locations in a typical wastewater treatment plant scenario, the differences in average mass concentrations were 8.66, 12.20, 0.75 mg/m3 for N2O, respectively, and 45.74, 64.30, 214.82 mg/m3 for CH4 respectively. The two measurement methods showed significant discrepancies. In typical scenarios of power plants, manual monitoring data showed little difference from that of online monitoring, but significant to portable device measurements, which was mainly due to the portable device being susceptible to temperature, humidity and mutual interference between different gases. Research on portable devices in terms of water vapor, high temperature, high humidity, and the coexistence of multiple gases should be strengthened to improve the accuracy of portable devices. Generally, manual monitoring was more accurate and reliable compared to other methods. Reference | Related Articles | Metrics