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A comprehensive review on deep learning approaches in wind forecasting applications
The effective use of wind energy is an essential part of the sustainable development of
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
Wind power forecast based on improved Long Short Term Memory network
L Han, H **g, R Zhang, Z Gao - Energy, 2019 - Elsevier
In order to improve the forecast accuracy of wind power, an Improved Long Short Term
Memory (ILSTM) network structure is proposed. Firstly, Variational Mode Decomposition …
Memory (ILSTM) network structure is proposed. Firstly, Variational Mode Decomposition …
Wind power forecasting methods based on deep learning: A survey
X Deng, H Shao, C Hu, D Jiang… - Computer Modeling in …, 2020 - ingentaconnect.com
Accurate wind power forecasting in wind farm can effectively reduce the enormous impact
on grid operation safety when high permeability intermittent power supply is connected to …
on grid operation safety when high permeability intermittent power supply is connected to …
On wavelet transform based convolutional neural network and twin support vector regression for wind power ramp event prediction
Power produced from renewable energy sources carbon negative and promises an
increased reliability for grid integration. Wind energy sector globally has an installed …
increased reliability for grid integration. Wind energy sector globally has an installed …
A Multi-Scale Model based on the Long Short-Term Memory for day ahead hourly wind speed forecasting
Crucial to wind energy penetration in electrical systems is the precise forecasting of wind
speed, which turns into accurate future wind power estimates. Current trends in wind speed …
speed, which turns into accurate future wind power estimates. Current trends in wind speed …
[PDF][PDF] Short-Term Prediction of Wind Power Density Using Convolutional LSTM Network.
Efficient extraction of renewable energy from wind depends on the reliable estimation of
wind characteristics and optimization of wind farm installation and operation conditions …
wind characteristics and optimization of wind farm installation and operation conditions …
Short-term wind power forecasting through stacked and bi directional LSTM techniques
Background Computational intelligence (CI) based prediction models increase the efficient
and effective utilization of resources for wind prediction. However, the traditional recurrent …
and effective utilization of resources for wind prediction. However, the traditional recurrent …
Vlstm: Very long short-term memory networks for high-frequency trading
P Ganesh, P Rakheja - arxiv preprint arxiv:1809.01506, 2018 - arxiv.org
Financial trading is at the forefront of time-series analysis, and has grown hand-in-hand with
it. The advent of electronic trading has allowed complex machine learning solutions to enter …
it. The advent of electronic trading has allowed complex machine learning solutions to enter …
Hybrid transformer network for different horizons-based enriched wind speed forecasting
Highly accurate different horizon-based wind speed forecasting facilitates a better modern
power system. This paper proposed a novel astute hybrid wind speed forecasting model and …
power system. This paper proposed a novel astute hybrid wind speed forecasting model and …
A hybrid wind speed forecasting model using complete ensemble empirical decomposition with adaptive noise and convolutional support vector machine
Wind energy is a clean, green energy source that is used effectively in power system grids.
Wind forecasting is the key requirement for enhanced integration. Wind speed forecasting is …
Wind forecasting is the key requirement for enhanced integration. Wind speed forecasting is …