no code implementations • 18 Apr 2024 • Songtao Huang, Hongjin Song, Tianqi Jiang, Akbar Telikani, Jun Shen, Qingguo Zhou, BinBin Yong, Qiang Wu
Accurate traffic forecasting is essential for effective urban planning and congestion management.
no code implementations • 2 Feb 2023 • Meiyu Jiang, Jun Shen, XueTao Jiang, Lihui Luo, Rui Zhou, Qingguo Zhou
Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources.
no code implementations • 14 Jan 2023 • XueTao Jiang, Meiyu Jiang, Qingguo Zhou
In recent years, renewable energy resources have accounted for an increasing share of electricity energy. Among them, photovoltaic (PV) power generation has received broad attention due to its economic and environmental benefits. Accurate PV generation forecasts can reduce power dispatch from the grid, thus increasing the supplier's profit in the day-ahead electricity market. The power system of a PV site is affected by solar radiation, PV plant properties and meteorological factors, resulting in uncertainty in its power output. This study used multiple seasonal-trend decomposition using LOESS (MSTL) and temporal fusion transformer (TFT) to perform day-ahead PV prediction on the desert knowledge Australia solar centre (DKASC) dataset. We compare the decomposition algorithms (VMD, EEMD and VMD-EEMD) and prediction models (BP, LSTM and XGBoost, etc.)
no code implementations • 2 Apr 2022 • XueTao Jiang, Meiyu Jiang, YuChun Gou, Qian Li, Qingguo Zhou
In this paper, we propose an LSTM-based digital twin approach for forest modeling, using Landsat 7 remote sensing image within 20 years.
no code implementations • 2 Nov 2021 • Peng Zhi, Haoran Zhou, Hang Huang, Rui Zhao, Rui Zhou, Qingguo Zhou
In the field of state-of-the-art object detection, the task of object localization is typically accomplished through a dedicated subnet that emphasizes bounding box regression.
no code implementations • 28 Oct 2020 • XueTao Jiang, BinBin Yong, Soheila Garshasbi, Jun Shen, Meiyu Jiang, Qingguo Zhou
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature.