A multi-factor driven spatiotemporal wind power prediction model based on ensemble deep graph attention reinforcement learning networks
Y Chengqing, Y Guangxi, Y Chengming, Z Yu, M Xiwei - Energy, 2023 - Elsevier
Spatiotemporal wind power prediction technology could provide technical support for wind
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
farm energy regulation and dynamic planning. In the paper, a novel ensemble deep graph …
An optimized fuzzy deep learning model for data classification based on NSGA-II
As a powerful paradigm, deep learning (DL) models have been used in many applications
for classification tasks in images, text, and audio. Through DL models, we can learn task …
for classification tasks in images, text, and audio. Through DL models, we can learn task …
A survey on graph neural networks in intelligent transportation systems
Intelligent Transportation System (ITS) is vital in improving traffic congestion, reducing traffic
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
accidents, optimizing urban planning, etc. However, due to the complexity of the traffic …
Dsformer: A double sampling transformer for multivariate time series long-term prediction
Multivariate time series long-term prediction, which aims to predict the change of data in a
long time, can provide references for decision-making. Although transformer-based models …
long time, can provide references for decision-making. Although transformer-based models …
Attention mechanism is useful in spatio-temporal wind speed prediction: Evidence from China
The spatio-temporal wind speed prediction technology provides the key technical support for
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
the energy management and space allocation of the wind farm. To obtain an accurate spatio …
[HTML][HTML] Forecasting hourly PM2. 5 concentrations based on decomposition-ensemble-reconstruction framework incorporating deep learning algorithms
P Cai, C Zhang, J Chai - Data Science and Management, 2023 - Elsevier
Accurate predictions of hourly PM 2.5 concentrations are crucial for preventing the harmful
effects of air pollution. In this study, a new decomposition-ensemble framework incorporating …
effects of air pollution. In this study, a new decomposition-ensemble framework incorporating …
MRIformer: A multi-resolution interactive transformer for wind speed multi-step prediction
Wind speed prediction is crucial for managing energy consumption in wind farms.
Traditional wind speed prediction techniques often overlook two essential characteristics of …
Traditional wind speed prediction techniques often overlook two essential characteristics of …
Data analysis and preprocessing techniques for air quality prediction: a survey
C Yu, J Tan, Y Cheng, X Mi - Stochastic Environmental Research and Risk …, 2024 - Springer
Air quality prediction technology can provide effective technical means for environmental
governance. In recent years, due to the strong nonlinearity of data, there has been extensive …
governance. In recent years, due to the strong nonlinearity of data, there has been extensive …
A novel dynamic ensemble of Numerical Weather Prediction for multi-step wind speed forecasting with deep reinforcement learning and error sequence modeling
J Zhao, Y Guo, Y Lin, Z Zhao, Z Guo - Energy, 2024 - Elsevier
Accurate wind forecasts for one day ahead or longer periods have significant impacts on the
safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) …
safe and efficient dispatch of power grids, where Numerical Weather Prediction (NWP) …
TFEformer: A new temporal frequency ensemble transformer for day-ahead photovoltaic power prediction
The accurate prediction of day-ahead Photovoltaic (PV) power can provide technical support
for complex solar management systems. This problem involves forecasting a long time …
for complex solar management systems. This problem involves forecasting a long time …