4 papers
UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
Ronghui Xu, Tongxin Wu, Guozhen Zhang +4
Day-ahead wind power forecasting is essential for cost-effective power-system operation. It is primarily driven by future meteorological conditions while retaining temporal depende…
REnergy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Zhi Sheng, Yuan Yuan, Guozhen Zhang +1
The rapid expansion of renewable energy, particularly wind and solar power, has made reliable forecasting critical for power system operations. While recent deep learning models ha…
Probing Neural Topology of Large Language Models
Yu Zheng, Yuan Yuan, Yue Zhuo +4
Probing large language models (LLMs) has yielded valuable insights into their internal mechanisms by linking neural activations to interpretable semantics. However, the complex mec…
Memory-enhanced Invariant Prompt Learning for Urban Flow Prediction under Distribution Shifts
Haiyang Jiang, Tong Chen, Wentao Zhang +4
Urban flow prediction is a classic spatial-temporal forecasting task that estimates the amount of future traffic flow for a given location. Though models represented by Spatial-Tem…