6 citations · 6 across the 4 of their papers we have counts for
7 papers
Flow-PIN: A Two-Stage Power-Flow-Guided Method for System-Wide Multivariate Profile Inpainting in Distribution Networks
Zhenghao Zhou, Yiyan Li, Yike Guo +3
High-quality system measurement data is critical for power distribution system operation. As deep generative models (e.g., GAN, Diffusion, etc.) have been widely studied to solve t…
A Physics-guided Fine-tuned LLM-based Framework for Customized Power Distribution System Feeder Generation
Zhenghao Zhou, Yiyan Li, Tao Xu +3
Power distribution system feeder models (e.g., IEEE 33-bus system, IEEE 13-bus system, etc.) are cornerstones for conducting power distribution system studies. As real-world feeder…
Deep-Learning-based Frequency-Domain Watermarking for Energy System Time Series Data Asset Protection
Zhenghao Zhou, Yiyan Li, Xinjie Yu +4
Data has been regarded as a valuable asset with the fast development of artificial intelligence technologies. In this paper, we introduce deep-learning neural network-based frequen…
A Glass-Box Deep-Learning Method for Electrical Energy System Modeling Based on Kolmogorov-Arnold Network
Zhenghao Zhou, Yiyan Li, Zelin Guo +2
Deep learning methods have been widely used as an end-to-end modeling strategy of electrical energy systems because of their conveniency and powerful pattern recognition capability…
An LLM-Enabled Frequency-Aware Flow Diffusion Model for Natural-Language-Guided Power System Scenario Generation
Zhenghao Zhou, Yiyan Li, Fei Xie +5
Diverse and controllable scenario generation (e.g., wind, solar, load, etc.) is critical for robust power system planning and operation. As AI-based scenario generation methods are…
A Causal-Guided Multimodal Large Language Model for Generalized Power System Time-Series Data Analytics
Zhenghao Zhou, Yiyan Li, Xinjie Yu +6
Power system time series analytics is critical in understanding the system operation conditions and predicting the future trends. Despite the wide adoption of Artificial Intelligen…