A review of wind speed and wind power forecasting with deep neural networks
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …
has attracted increasing attention. However, intermittent electricity generation resulting from …
[HTML][HTML] A review and taxonomy of wind and solar energy forecasting methods based on deep learning
G Alkhayat, R Mehmood - Energy and AI, 2021 - Elsevier
Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …
forecasting has a significant impact on making decisions related to operating and managing …
Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks
Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …
systems. However, the accuracy of power forecasting results is easily impaired by the …
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
KU Jaseena, BC Kovoor - Energy Conversion and Management, 2021 - Elsevier
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …
renewable energy resources. Wind energy is attracting attention worldwide due to its …
An evolutionary deep learning model based on TVFEMD, improved sine cosine algorithm, CNN and BiLSTM for wind speed prediction
Accurate prediction of wind speed is of great significance to the stable operation of wind
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
power equipment. In this study, a hybrid deep learning model based on convolutional neural …
Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution
G Yu, C Liu, B Tang, R Chen, L Lu, C Cui, Y Hu… - Renewable Energy, 2022 - Elsevier
Accurate regional wind power prediction is of great significance to the wind farm clusters
integration and the economic dispatch of the regional power grid. The complex …
integration and the economic dispatch of the regional power grid. The complex …
A dual‐stage attention‐based Conv‐LSTM network for spatio‐temporal correlation and multivariate time series prediction
Multivariate time series (MTS) prediction aims at predicting future time series by extracting
multiple forms of dependencies of past time series. Traditional prediction methods and deep …
multiple forms of dependencies of past time series. Traditional prediction methods and deep …
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 shaping a low carbon …
human society, in particular, at the recent unprecedented pressure in shaping a low carbon …
[HTML][HTML] Multistep short-term wind speed forecasting using transformer
H Wu, K Meng, D Fan, Z Zhang, Q Liu - Energy, 2022 - Elsevier
Wind power can effectively alleviate the energy crisis. However, its integration into the grid
affects power quality and power grid stability. Accurate wind speed prediction is a key factor …
affects power quality and power grid stability. Accurate wind speed prediction is a key factor …
[HTML][HTML] U-FLOOD–Topographic deep learning for predicting urban pluvial flood water depth
This study investigates how deep-learning can be configured to optimise the prediction of
2D maximum water depth maps in urban pluvial flood events. A neural network model is …
2D maximum water depth maps in urban pluvial flood events. A neural network model is …