6 papers
Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
Ziqing Ma, Kai Ying, Xinyue Gu +7
Accurate day-ahead solar irradiance forecasting is essential for integrating solar energy into the power grid. However, it remains challenging due to the pronounced diurnal cycle a…
Baguan-TS: A Sequence-Native In-Context Learning Model for Time Series Forecasting with Covariates
Linxiao Yang, Xue Jiang, Gezheng Xu +9
Transformers enable in-context learning (ICL) for rapid, gradient-free adaptation in time series forecasting, yet most ICL-style approaches rely on tabularized, hand-crafted featur…
Target Concept Tuning Improves Extreme Weather Forecasting
Shijie Ren, Xinyue Gu, Ziheng Peng +6
Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typicall…
ProtoTS: Learning Hierarchical Prototypes for Explainable Time Series Forecasting
Ziheng Peng, Shijie Ren, Xinyue Gu +3
While deep learning has achieved impressive performance in time series forecasting, it becomes increasingly crucial to understand its decision-making process for building trust in…
SolarBoost: Distributed Photovoltaic Power Forecasting Amid Time-varying Grid Capacity
Linyuan Geng, Linxiao Yang, Xinyue Gu +1
This paper presents SolarBoost, a novel approach for forecasting power output in distributed photovoltaic (DPV) systems. While existing centralized photovoltaic (CPV) methods are a…
Integrated Influence: Data Attribution with Baseline
Linxiao Yang, Xinyu Gu, Liang Sun
As an effective approach to quantify how training samples influence test sample, data attribution is crucial for understanding data and model and further enhance the transparency o…