Výsledky vyhledávání - "Fang, Jiannong"

Report

Zero-Shot Load Forecasting with Large Language Models

Autor: Liao, Wenlong, Yang, Zhe, Jia, Mengshuo, Rehtanz, Christian, Fang, Jiannong, Porté-Agel, Fernando

Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before being applied to new scenarios, which limits their effectiveness in data-scarce scenarios. Inspired by

Externí odkaz: http://arxiv.org/abs/2411.11350

Zobrazit plný text záznamu

Report

TimeGPT in Load Forecasting: A Large Time Series Model Perspective

Autor: Liao, Wenlong, Porte-Agel, Fernando, Fang, Jiannong, Rehtanz, Christian, Wang, Shouxiang, Yang, Dechang, Yang, Zhe

Machine learning models have made significant progress in load forecasting, but their forecast accuracy is limited in cases where historical load data is scarce. Inspired by the outstanding performance of large language models (LLMs) in computer visi

Externí odkaz: http://arxiv.org/abs/2404.04885

Zobrazit plný text záznamu

Report

Improving the Accuracy and Interpretability of Neural Networks for Wind Power Forecasting

Autor: Liao, Wenlong, Porte-Agel, Fernando, Fang, Jiannong, Bak-Jensen, Birgitte, Yang, Zhe, Zhang, Gonghao

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecasted errors are severely limited by the local optimal weight issu

Externí odkaz: http://arxiv.org/abs/2312.15741

Zobrazit plný text záznamu

Report

Explainable Modeling for Wind Power Forecasting: A Glass-Box Approach with High Accuracy

Autor: Liao, Wenlong, Porte-Agel, Fernando, Fang, Jiannong, Bak-Jensen, Birgitte, Ruan, Guangchun, Yang, Zhe

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box approach that combines hig

Externí odkaz: http://arxiv.org/abs/2310.18629

Zobrazit plný text záznamu

Report

A Fast Hybrid Pressure-Correction Algorithm for Simulating Incompressible Flows by Projection Methods

Autor: Fang, Jiannong

Publikováno v: Algorithms 2023, 16(6), 287

For simulating incompressible flows by projection methods. it is generally accepted that the pressure-correction stage is the most time-consuming part of the flow solver. The objective of the present work is to develop a fast hybrid pressure-correcti

Externí odkaz: http://arxiv.org/abs/2304.14690

Zobrazit plný text záznamu