collaborators

6 papers

cs.AI2026

PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction

Hang Fan, Weican Liu, Ying Lu +3

Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly s…

cs.CE2026

An End-to-end Building Load Forecasting Framework with Patch-based Information Fusion Network and Error-weighted Adaptive Loss

Hang Fan, Ying Lu, Weican Liu +3

Accurate building load forecasting plays a critical role in facilitating demand response aggregation and optimizing energy management. However, the complex temporal dependencies an…

cs.CE2025

IDS-Net: A novel framework for few-shot photovoltaic power prediction with interpretable dynamic selection and feature information fusion

Hang Fan, Weican Liu, Zuhan Zhang +3

With the growing demand for renewable energy, countries are accelerating the construction of photovoltaic (PV) power stations. However, accurately forecasting power data for newly…

cs.CE2025

EV-STLLM: Electric vehicle charging forecasting based on spatio-temporal large language models with multi-frequency and multi-scale information fusion

Hang Fan, Yunze Chai, Chenxi Liu +4

With the proliferation of electric vehicles (EVs), accurate charging demand and station occupancy forecasting are critical for optimizing urban energy and the profit of EVs aggrega…

eess.SP2025

Spatiotemporal Prediction of Electric Vehicle Charging Load Based on Large Language Models

Hang Fan, Mingxuan Li, Jingshi Cui +3

The rapid growth of EVs and the subsequent increase in charging demand pose significant challenges for load grid scheduling and the operation of EV charging stations. Effectively h…

cs.LG2025

M2WLLM: Multi-Modal Multi-Task Ultra-Short-term Wind Power Prediction Algorithm Based on Large Language Model

Hang Fana, Mingxuan Lib, Zuhan Zhanga +3

The integration of wind energy into power grids necessitates accurate ultra-short-term wind power forecasting to ensure grid stability and optimize resource allocation. This study…