Search Results for author:
Found 4 papers, 0 papers with code
A novel automatic wind power prediction framework based on multi-time scale and temporal attention mechanisms
Accurate wind power forecasting is crucial for developing a new power system that heavily relies on renewable energy sources.
Day-Ahead PV Power Forecasting Based on MSTL-TFT
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.)
Forestry digital twin with machine learning in Landsat 7 data
In this paper, we propose an LSTM-based digital twin approach for forest modeling, using Landsat 7 remote sensing image within 20 years.
Crop and weed classification based on AutoML
CNN models already play an important role in classification of crop and weed with high accuracy, more than 95% as reported in literature.
