Publications
Journal and Preprints
S. Li, H. Xiong, and Y. Chen. "DiffCharge: Generating EV Charging Scenarios via a Denoising Diffusion Model," IEEE Transactions on Smart Grid [link] [code]
X. He, D.H. Tsang, and Y. Chen. "Long-Term Carbon-Efficient Planning for Geographically Shiftable Resources: A Monte Carlo Tree Search Approach", submitted to IEEE Transactions on Power Systems, 2024 [link]
Y. Jiang, Y. Li, and Y. Chen. "Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition", Electric Power Systems Research, 2024 [link]
S. Li, H. Xiong, and Y. Chen. "DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load", Electric Power Systems Research, 2024 [link] [code]
C. Huang, S. Li, R. Liu, H. Wang, and Y. Chen. "Large Foundation Models for Power Systems", in submission, 2024 [preprint] [code]
R. Liu, Y. Pan, and Y. Chen. "Laxity-Aware Scalable Reinforcement Learning for HVAC Control," arXiv preprint arXiv:2306.16619 [link]
Y. Zhang, H. Wen, T. Feng, and Y. Chen. “Targeted Demand Response: Formulation, LMP Implications, and Fast Algorithms,” submitted to Applied Energy, 2023 [link]
Y. Chen, L. Zhang, and B. Zhang. "Learning to solve DCOPF: A duality approach." Electric Power Systems Research 213 (2022): 108595 [link][code]
L. Zhang, Y. Chen, and B. Zhang, “A Convex Neural Network Solver for DCOPF with Generalization Guarantees,” IEEE Transactions on Control of Networked Systems, 2021 [link]
A. Pan, Y. Lee, H. Zhang, Y. Chen, and Y. Shi. "Improving Robustness of Reinforcement Learning for Power System Control with Adversarial Training." arXiv preprint arXiv:2110.08956 (2021) [link]
Y. Chen, D. Arnold, Y. Shi, and S. Peisert. "Understanding the Safety Requirements for Learning-based Power Systems Operations." arXiv preprint arXiv:2110.04983 (2021) [link]
Y. Chen, Y. Shi, and B. Zhang, “Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks,” Electric Power Systems Research, vol. 189, 2020 [link]
Y. Chen, and H. Wang. "IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning." IEEE Transactions on Emerging Topics in Computational Intelligence (2020) [link]
J. E. Contreras-Ocana, Y. Chen, U. Siddiqi, and B. Zhang, “Non-Wire Alternatives: an Additional Value Stream for Distributed Energy Resources,” IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1287–1299, 2020 [link]
Y. Chen, W. Yang, B. Zhang, “Using Mobility for Electrical Load Forecasting During the COVID-19 Pandemic”, arXiv preprint arXiv:2006.08826 (2020) [link][code]
X-W. Wang, Y. Chen, and Y-Y. Liu. "Link Prediction through Deep Generative Model." iScience 23, no. 10 (2020): 101626 [link]
Y. Chen, M. Tulio-Angulo, and Y-Y. Liu. "Revealing Complex Ecological Dynamics via Symbolic Regression." BioEssays 41, no. 12 (2019): 1900069 [link] [Cover]
Y. Chen, Y. Wang, D. Kirschen, and B. Zhang, “Model-Free Renewables Scenario Generation Using Generative Adversarial Networks,” IEEE Transaction on Power Systems, vol. 33, no. 3, pp. 3265–3275, 2018 [link][code]
Conference Proceedings
C. Gu, Y. Pan, R. Liu, and Y. Chen. "Learning and Optimization for Price-Based Demand Response of Electric Vehicle Charging", in American Control Conference (ACC), 2024
C. Huang, S. Li, R. Liu, H. Wang, and Y. Chen. "Large Foundation Models for Power Systems", in IEEE Power and Energy Society General Meeting (PESGM), 2024 [link] [code]
Y. Jiang, Y. Li, and Y. Chen. "Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition", in Power Systems Computation Conference (PSCC), 2024 [link]
S. Li, H. Xiong, and Y. Chen. "DiffPLF: A Conditional Diffusion Model for Probabilistic Forecasting of EV Charging Load", in Power Systems Computation Conference (PSCC), 2024 [link]
H. Bai, Y. Chen, and Y. Chen. "Hi-NeuS: Global Geometric Refinement for High-Fidelity Neural Surface Reconstruction", submitted, 2024
Y. Chen, D. Deka, and Y. Shi. "Contributions of Individual Generators to Nodal Carbon Emissions", in ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), 2024 [link] [code]
Y. Pan, Y. Chen, and F. Lin. "Adjustable Robust Reinforcement Learning for Online 3D Bin Packing." In Conference on Neural Information Processing Systems (NeurIPS), 2023 [link] [video]
C. Yeh, V. Li, R. Datta, J. Arroyo, N. Christianson, C. Zhang, Y. Chen, M. Hosseini, A. Golmohammadi, Y. Shi, Y. Yue, A. Wierman. "SustainGym: Reinforcement Learning Environments for Sustainable Energy Systems." In Conference on Neural Information Processing Systems (NeurIPS), 2023, Datasets and Benchmarks Track [link] [code]
X. He, J. Tian, Y. Zhang, H. Wen, and Y. Chen. "Fast Constraint Screening for Multi-Interval Unit Commitment." in IEEE Conference on Decision and Control (CDC), 2023 [link]
H. Bai, Y. Lin, Y. Chen, L. Wang. "Dynamic PlenOctree for Adaptive Sampling Refinement in Explicit NeRF." in International Conference on Computer Vision (ICCV), 2023 [link]
R. Liu and Y. Chen. "Learning a Multi-Agent Controller for Shared Energy Storage System." in IEEE Power and Energy Society General Meeting (PESGM), 2023
K. Cheng, Y. Chen and Y. Shi. "GridViz: a Toolkit for Interactive and Multi-Modal Power Grid Data Visualization." in IEEE Power and Energy Society General Meeting (PESGM), 2023 [Website]
C. Zhang, Y. Shi and Y. Chen. “BEAR: Physics-Principled Building Environment for Control and RL.” in ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy), 2023 [link][code]
X. He, H. Wen, Y. Zhang and Y. Chen. “Enabling Fast Unit Commitment Constraint Screening via Learning Cost Model.” Submitted [link]
Q. Zhu, Y. Chen, H. Wang, Z. Zeng, H. Liu. “A Knowledge-Enhanced Framework for Imitative Transportation Trajectory Generation.” in IEEE International Conference on Data Mining (ICDM), 2022
R. Liu and Y. Chen. "Learning Task-Aware Energy Disaggregation: a Federated Approach." in IEEE Conference on Decision and Control (CDC), 2022 [link]
K. Cheng, Y. Bian, Y. Shi, and Y. Chen. "Carbon-Aware EV Charging." in IEEE SmartGridComm, 2022 [link][code]
Y. Chen and B. Zhang, “State-of-Charge Aware EV Charging.” in Power and Energy Society General Meeting (PESGM), 2022 Best Paper [link][code]
Y. Chen, Y. Shi, D. Arnold, and S. Peisert, “SAVER: Safe Learning-Based Controller for Real-Time Voltage Regulation”, in Power and Energy Society General Meeting (PESGM), 2022 [link]
Y. Chen, L. Zhang, and B. Zhang, “Learning to Solve DCOPF: A Duality Approach”, in Power Systems Computation Conference (PSCC), 2022 [link]
D Arnold, ST Ngo, C Roberts, Y Chen, A Scaglione, S Peisert, “Adam-based Augmented Random Search for Control Policies for Distributed Energy Resource Cyber Attack Mitigation”, in American Control Conference (ACC), 2022 [link]
Y. Chen, Y. Tan, L. Zhang, and B. Zhang, “Vulnerabilities of Power System Operations to Load Forecasting Data Injection Attacks,” in IEEE SmartGridComm, 2021 [link]
Y. Chen and B. Zhang, “Learning to Solve Network Flow Problems via Neural Decoding,” 2020 [link]
Y. Chen, Y. Shi, and B. Zhang, “Data-Driven Optimal Voltage Regulation Using Input Convex Neural Networks,” in Power Systems Computation Conference (PSCC), 2020, pp. 1–7. Highlight Paper [link]
Y. Chen, Y. Shi, and B. Zhang, “Optimal Control Via Neural Networks: A Convex Approach,” in International Conference on Learning Representations (ICLR), 2019 [link][code]
Y. Chen, Y. Tan, and B. Zhang, “Exploiting Vulnerabilities of Load Forecasting Through Adversarial Attacks,” in Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy), 2019, pp. 1–11. Best Paper Runner-Up [link][code]
Y. Chen, M-U. Hashmi, D. Deka, and M. Chertkov. "Stochastic battery operations using deep neural networks." In IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019 [link]
Y. Chen, Y. Tan, and D. Deka. "Is machine learning in power systems vulnerable?." In IEEE SmartGridComm, 2018 [link][code]
Y. Chen, P. Li, and B. Zhang, “Bayesian Renewables Scenario Generation via Deep Generative Networks,” in Conference on Information Sciences and Systems (CISS), 2018 [link]
Y. Chen, X. Wang, and B. Zhang, “An Unsupervised Deep Learning Approach for Scenario Forecasts,” in Power Systems Computation Conference (PSCC), 2018, pp. 1–7 [link][code]
Y. Chen, Q. Li, and H. Wang. "Towards Trusted Social Networks with Blockchain Technology." In Symposium on Foundations and Applications of Blockchain, p. 37. 2018 [link]
Y. Chen, Y. Shi, and B. Zhang, “Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks,” in Asilomar Conference, 2017 [link]
H. Hosseini, Y. Chen, S. Kannan, B. Zhang, and R. Poovendran. "Blocking Transferability of Adversarial Examples in Black-Box Learning Systems." arXiv preprint arXiv:1703.04318 (2017) [link]
Thesis
Y. Chen, "Learning to Operate a Sustainable Power System." Ph.D. dissertation, University of Washington, June 2021 [link]