7 papers
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…
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…
Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecasting
Hang Fan, Haoran Pei, Runze Liang +3
Photovoltaic (PV) power forecasting plays a critical role in power system dispatch and market participation. Because PV generation is highly sensitive to weather conditions and clo…
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…
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…
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…