S
Shengyi Wang Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
13 h-index
67 pubs
1,020 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Shengyi Wang's research focuses on the application of machine learning and optimization techniques to complex engineering and energy systems. Wang has investigated methods for optimizing process parameters in selective laser melting and developed intelligent classification systems for in-situ porosity monitoring. In the realm of energy systems, Wang's work includes deep reinforcement learning for energy storage system scheduling to regulate voltage in power grids with high photovoltaic penetration, and learning in potential games for electric power grids. Further research explores stochastic synchronous learning for electric vehicle aggregators considering their collective age of information and semi-supervised disaggregation of load profiles in transmission buses with significant behind-the-meter solar generation. Wang also studies state-of-charge estimation for lithium-ion batteries using a quantum particle swarm optimization extended Kalman quantum particle filter approach.
Metrics
- h-index: 13
- Publications: 67
- Citations: 1,020
Selected Publications
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Cascaded Learning of Grid-to-Graph Embeddings for Voltage Area Partition in Inaccurate Multiphase Distribution Networks (2025)
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Online Hierarchical Aggregate Power Flexibility Characterization of EV Charging Stations With Disaggregation Feasibility Guarantee (2025)
Collaboration Network
Top Collaborators
Liang Du
Temple University (US)
15 shared publications
- Deep Reinforcement Scheduling of Energy Storage Systems for Real-Time Voltage Regulation in Unbalanced LV Networks With High PV Penetration
- Learning in Potential Games for Electric Power Grids: Models, Dynamics, and Outlook
- Stochastically Stable Synchronous Learning for EV Aggregators Considering Their Collective Age of Information
- Semi-Supervised Disaggregation of Load Profiles at Transmission Buses with Significant Behind-the-Meter Solar Generations
- An Efficient Power Flexibility Aggregation Framework via Coordinate Transformation and Chebyshev Centering Optimization
Showing 5 of 15 shared publications
Nora El-Gohary
University of Illinois Urbana-Champaign (US)
4 shared publications
- Segmentation of Bridge Components from Various Real Scene Inspection Images
- SAM-based Segmentation of Multi-Class Bridge Components from Diverse Real-Scene Inspection Images
- Automated Bridge Inspection Image Interpretation Based on Vision-Language Pre-Training
- Text-Enhanced Label-Efficient Automated Bridge Defect Semantic Segmentation from Inspection Images
Qi Zhou
Huazhong University of Science and Technology (CN)
3 shared publications
- Multi-objective process parameters optimization of SLM using the ensemble of metamodels
- In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing
- An adaptive space preselection method for the multi-fidelity global optimization
Xiaoyuan Fan
Pacific Northwest National Laboratory (US)
3 shared publications
- Deep Reinforcement Scheduling of Energy Storage Systems for Real-Time Voltage Regulation in Unbalanced LV Networks With High PV Penetration
- Semi-Supervised Disaggregation of Load Profiles at Transmission Buses with Significant Behind-the-Meter Solar Generations
- Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar
Liang Zhu
Shanghai University (CN)
3 shared publications
- Neural Spectral Clustering Based Voltage Area Partition of Active Distribution Systems
- Graph Learning Method for Voltage Area Partition in Inaccurate Multiphase Distribution Networks
- Cascaded Learning of Grid-to-Graph Embeddings for Voltage Area Partition in Inaccurate Multiphase Distribution Networks
Jiexiang Hu
Huazhong University of Science and Technology (CN)
2 shared publications
- Multi-objective process parameters optimization of SLM using the ensemble of metamodels
- An adaptive space preselection method for the multi-fidelity global optimization
Longchao Cao
Huazhong University of Science and Technology (CN)
2 shared publications
- Multi-objective process parameters optimization of SLM using the ensemble of metamodels
- In situ porosity intelligent classification of selective laser melting based on coaxial monitoring and image processing
Xiaonan Lu
Temple University (US)
2 shared publications
- Harmonic Compensation Control of Grid Interactive Inverters Based on Data-driven Harmonic State Space Modeling
- Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection
Yuhua Du
Temple University (US)
2 shared publications
- Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection
- MindSynchro
Yübo Wang
Siemens (United States) (US)
2 shared publications
- Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection
- MindSynchro
Bruno P. Leão
Siemens (United States) (US)
2 shared publications
- Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection
- MindSynchro
Sindhu Suresh
Siemens (United States) (US)
2 shared publications
- Physics-Based Feature Extraction from Bulk Time-Series PMU Datasets for Event Detection
- MindSynchro
Zhenyu Zhao
Temple University (US)
2 shared publications
- Semi-Supervised Disaggregation of Load Profiles at Transmission Buses with Significant Behind-the-Meter Solar Generations
- Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar
Daniel Moscovitz
National Audubon Society (US)
2 shared publications
- Semi-Supervised Disaggregation of Load Profiles at Transmission Buses with Significant Behind-the-Meter Solar Generations
- Deep Factorization Machine Learning for Disaggregation of Transmission Load Profiles With High Penetration of Behind-the-Meter Solar
Bai Cui
Iowa State University (US)
2 shared publications
- An Efficient Power Flexibility Aggregation Framework via Coordinate Transformation and Chebyshev Centering Optimization
- Multi-Factor-Coupled, Ahead-of-Time Aggregation of Power Flexibility Under Forecast Uncertainty
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