18 papers
From Shared Demand Patterns to Local Uncertainty: Probabilistic Load Forecasting by Mixing Compact Adaptations
Haoran Li, Zhe Cheng, Yang Weng
Probabilistic load forecasting has been widely studied for power-system operation and planning, but customer- and transformer-level forecasting introduces a distinct scalability ch…
Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution
Chenhan Xiao, Xinyu He, Haoran Li +2
Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications. Recent synthetic grid generation…
Enabling Scalable Topology Inference in Distribution Systems via Constrained Multi-Source Inference
Haoran Li, Lihao Mai, Muhao Guo +2
Accurate distribution system topology is essential for outage localization, voltage analytics, and operation of distribution grids, yet maintaining reliable connectivity records re…
Predicting Power-System Dynamic Trajectories with Foundation Models
Haoran Li, Lihao Mai, Chenhan Xiao +2
As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key…
Retrieval-Guided Photovoltaic Inventory Estimation from Satellite Imagery for Distribution Grid Planning
Muhao Guo, Lihao Mai, Erik Blasch +3
The rapid expansion of distributed rooftop photovoltaic (PV) systems introduces increasing uncertainty in distribution grid planning, hosting capacity assessment, and voltage regul…
LASS-ODE: Scaling ODE Computations to Connect Foundation Models with Dynamical Physical Systems
Haoran Li, Chenhan Xiao, Lihao Mai +2
Foundation models have transformed language, vision, and time series data analysis, yet progress on dynamic predictions for physical systems remains limited. Given the complexity o…
Cross-Domain Generalization of Multimodal LLMs for Global Photovoltaic Assessment
Muhao Guo, Yang Weng
The rapid expansion of distributed photovoltaic (PV) systems poses challenges for power grid management, as many installations remain undocumented. While satellite imagery provides…
MOE-GL: A Family of Probabilistic Load Forecasters That Scales to Massive Customers
Haoran Li, Zhe Cheng, Muhao Guo +4
Probabilistic load forecasting is widely studied and underpins power system planning, operation, and risk-aware decision making. Deep learning forecasters have shown strong ability…
Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets
Muhao Guo, Haoran Li, Yang Weng
Neural ordinary differential equations (NODE) have garnered significant attention for their design of continuous-depth neural networks and the ability to learn data/feature dynamic…
Modeling Time Series Dynamics with Fourier Ordinary Differential Equations
Muhao Guo, Yang Weng
Neural ODEs (NODEs) have emerged as powerful tools for modeling time series data, offering the flexibility to adapt to varying input scales and capture complex dynamics. However, t…
Latent Mixture of Symmetries for Sample-Efficient Dynamic Learning
Haoran Li, Chenhan Xiao, Muhao Guo +1
Learning dynamics is essential for model-based control and Reinforcement Learning in engineering systems, such as robotics and power systems. However, limited system measurements,…
PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis
Xinyu He, Chenhan Xiao, Haoran Li +5
Modern power systems are becoming increasingly dynamic, with changing topologies and time-varying loads driven by renewable energy variability, electric vehicle adoption, and activ…
From Imperfect Signals to Trustworthy Structure: Confidence-Aware Inference from Heterogeneous and Reliability-Varying Utility Data
Haoran Li, Lihao Mai, Muhao Guo +4
Accurate distribution grid topology is essential for reliable modern grid operations. However, real-world utility data originates from multiple sources with varying characteristics…
Neural Predictive Control to Coordinate Discrete- and Continuous-Time Models for Time-Series Analysis with Control-Theoretical Improvements
Haoran Li, Muhao Guo, Yang Weng +1
Deep sequence models have achieved notable success in time-series analysis, such as interpolation and forecasting. Recent advances move beyond discrete-time architectures like Recu…
Solar Photovoltaic Assessment with Large Language Model
Muhao Guo, Yang Weng
Accurate detection and localization of solar photovoltaic (PV) panels in satellite imagery is essential for optimizing microgrids and active distribution networks (ADNs), which are…
Graph Structure Learning with Privacy Guarantees for Open Graph Data
Muhao Guo, Jiaqi Wu, Yizheng Liao +3
Publishing open graph data while preserving individual privacy remains challenging when data publishers and data users are distinct entities. Although differential privacy (DP) pro…
External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting
Haoran Li, Muhao Guo, Marija Ilic +2
Accurate residential load forecasting is critical for power system reliability with rising renewable integration and demand-side flexibility. However, most statistical and machine…
ExARNN: An Environment-Driven Adaptive RNN for Learning Non-Stationary Power Dynamics
Haoran Li, Muhao Guo, Yang Weng +2
Non-stationary power system dynamics, influenced by renewable energy variability, evolving demand patterns, and climate change, are becoming increasingly complex. Accurately captur…