57 citations · 127 across the 16 of their papers we have counts for
6 papers · 1 filter
Safe Reinforcement Learning with Preference-based Constraint Inference
Chenglin Li, Grant Ruan, Hua Geng
Safe reinforcement learning (RL) is a standard paradigm for safety-critical decision making. However, real-world safety constraints can be complex, subjective, and even hard to exp…
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…
Tilted Quantile Gradient Updates for Quantile-Constrained Reinforcement Learning
Chenglin Li, Guangchun Ruan, Hua Geng
Safe reinforcement learning (RL) is a popular and versatile paradigm to learn reward-maximizing policies with safety guarantees. Previous works tend to express the safety constrain…
Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy
Wenlong Liao, Fernando Porte-Agel, Jiannong Fang +3
Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address…
Short-Term Electricity Price Forecasting based on Graph Convolution Network and Attention Mechanism
Yuyun Yang, Zhenfei Tan, Haitao Yang +2
In electricity markets, locational marginal price (LMP) forecasting is particularly important for market participants in making reasonable bidding strategies, managing potential tr…