Knowledge-data-driven power flow calculation for lowvoltage active distribution network considering gray data by LIU Siliang, ZHENG Zenan, ZHANG Yongjun, YI Yingqi, CHI Yuquan

“…Inaccurate topology and line parameters in low-voltage distribution networks (LVDNs) render traditional power flow calculation methods ineffective. …”
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A Topology Identification Framework for Medium and Low Voltage Distribution Networks Driven by Multiple Source Data by Chaoyang ZHU, Dongsheng JING, Heting LI, Yibin WANG, Ping HE

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Data Driven Reduced Pi-Model of Feeders for Distribution Network Representation With DERs for Fast Reconfiguration by Tharmini Thavaratnam, Bala Venkatesh

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Estimation of Arterial Path Flow Considering Flow Distribution Consistency: A Data-Driven Semi-Supervised Method by Zhe Zhang, Qi Cao, Wenxie Lin, Jianhua Song, Weihan Chen, Gang Ren

“…To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. …”
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Transient Pressure Estimation Using Data-Driven Models: An Approach Based on Ensemble Trees by Rafael Barreto, Rui Gabriel Souza, Gustavo Meirelles, Bruno Brentan

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Research on distributed data spatial architecture model for ownership governance by SUN Jinye, GUO Shuxing

“…Subsequently, a distributed data space architecture model based on the perspective of cross-domain collaboration for data authorization operators was innovatively proposed by combining distributed architecture theory with dynamic It is found that the architecture model can effectively guide the application of cross-domain scenarios so that different dimensions' data can generate large-scale gain value through superposition and optimize ownership allocation to improve orderliness in data market transactions.…”
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Research on distributed data spatial architecture model for ownership governance by SUN Jinye, GUO Shuxing

“…Subsequently, a distributed data space architecture model based on the perspective of cross-domain collaboration for data authorization operators was innovatively proposed by combining distributed architecture theory with dynamic It is found that the architecture model can effectively guide the application of cross-domain scenarios so that different dimensions' data can generate large-scale gain value through superposition and optimize ownership allocation to improve orderliness in data market transactions.…”
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A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling by Nastaran Gholizadeh, Petr Musilek

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Surrogate‐Model Assisted Plausibility‐Check, Calibration, and Posterior‐Distribution Evaluation of Subsurface‐Flow Models by Jonas Allgeier, Olaf A. Cirpka

“…This is the basis for Markov‐Chain Monte Carlo (MCMC) simulations using GPR to estimate the posterior parameter distribution. We tested several variants of the scheme on a 3‐D variably‐saturated steady‐state subsurface‐flow model and compared it to a Neural Posterior Estimation (NPE) scheme, which requires samples of the prior distribution only. …”
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Multi-Scenario Robust Distributed Permutation Flow Shop Scheduling Based on DDQN by Shilong Guo, Ming Chen

“…In order to address the Distributed Displacement Flow Shop Scheduling Problem (DPFSP) with uncertain processing times in real production environments, Plant Simulation is employed to construct a simulation model for the MSRDPFSP. …”
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Time-division prediction method for peak power of distributed new energy access power grid based on ARIMA–RBF by Yangrui Zhang, Junpeng Zhao, Chao Zhang, Bingyu Zhang, Xiaoyu Liu, Shaokang Feng

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Design of Instrumentation and Control Components of Power Distribution Systems by Y. A. Klimenko, Y. E. Lvovich, A. P. Preobrazhensky

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One-shot generative distribution matching for augmented RF-based UAV identification by Amir Kazemi, Salar Basiri, Volodymyr Kindratenko, Srinivasa Salapaka

“…This approach, when utilizing a distributional distance metric, demonstrates significant promise in low-data regimes, outperforming deep generative methods such as conditional generative adversarial networks (GANs) and variational autoencoders (VAEs). …”
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Simulation and Experimental Study of Arc Model in a Low-Voltage Distribution Network by Binbin Zhang, Jiaqing Zhang, Yifeng Cheng, Qixu Chen, Qian Zhang

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A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks by Hao Jiao, Chen Wu, Lei Wei, Jinming Chen, Yang Xu, Manyun Huang

“…This paper proposes a data-driven state estimation based on sample migration for low-observable distribution networks, addressing the challenge of traditional state estimators being unsuitable for distribution networks with low observability. …”
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Discrete event simulation and agent-based modelling of distributed situation awareness in patient flow management by Abdulrahman A. Alhaider, Nathan Lau, Osama Alotaik, Paul B. Davenport

“…This paper presents quantitative modelling of distributed situation awareness (DSA) with discrete event simulation (DES) and agent-based modelling (ABM) to capture and assess the transactions and distribution of SA for intrahospital transportation in patient flow management. …”
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Hyporheic Flows in Stratified Sediments: Implications on Residence Time Distributions by Alessandra Marzadri, Valentina Ciriello, Felipe P. J. de Barros

“…This study examines the effects of hydraulic conductivity stratification on steady‐state, two‐dimensional, hyporheic flows and solute residence time distribution. First, we derive an integral transform‐based semi‐analytical solution for the flow field, capable of accounting for the effects of any functional shape of the vertically varying hydraulic conductivity. …”
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Estimating Aggregate Capacity of Connected DERs and Forecasting Feeder Power Flow With Limited Data Availability by Amir Reza Nikzad, Amr Adel Mohamed, Bala Venkatesh, John Penaranda

“…However, forecasting, power flow analysis, and optimization of feeders for operational decision-making by individually modeling each of these numerous renewables in the absence of complete information are operationally challenging and technically impractical. …”
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Benefits of upstream data for downstream streamflow forecasting: data assimilation in a semi-distributed flood forecasting model by Paul Royer-Gaspard, François Bourgin, Charles Perrin, Vazken Andréassian, Alban De Lavenne, Guillaume Thirel, François Tilmant

“…Overall, the findings confirm the benefits of incorporating multiple flow observations into a semi-distributed model and suggest several avenues for further improvement.…”
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Power flow analysis and volt/var control strategy of the active distribution network based on data-driven method by Chen Hui, Zhu Weiping, Liu Liguo, Shi Mingming, Xie Wenqiang, Zhang Chenyu

“…Firstly, the CatBoost machine learning model for the distribution network power flow analysis is proposed, and the nonlinear mapping relationship between the distribution network state and power flow results is described from the data-driven perspective. …”
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