10 papers · 1 filter
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,…
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…