6 papers · 1 filter
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…
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…
TriForecaster: A Mixture of Experts Framework for Multi-Region Electric Load Forecasting with Tri-dimensional Specialization
Zhaoyang Zhu, Zhipeng Zeng, Qiming Chen +4
Electric load forecasting is pivotal for power system operation, planning and decision-making. The rise of smart grids and meters has provided more detailed and high-quality load d…
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…
Task-oriented Time Series Imputation Evaluation via Generalized Representers
Zhixian Wang, Linxiao Yang, Liang Sun +2
Time series analysis is widely used in many fields such as power energy, economics, and transportation, including different tasks such as forecasting, anomaly detection, classifica…
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
Jiehui Zhou, Linxiao Yang, Xingyu Liu +3
In causal inference, estimating heterogeneous treatment effects (HTE) is critical for identifying how different subgroups respond to interventions, with broad applications in field…