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Spatiotemporal Wind Speed Forecasting Using Dual-Stream Graph Attention Network and Long Short-Term Memory on Dutch Database

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inferredcites →https://doi.org/10.1007/s00521-021-06424-6openalex-extracted
A new insight to the wind speed forecasting: robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment
inferredcites →https://doi.org/10.1007/s11356-022-19388-4openalex-extracted
Multi-step wind speed forecasting based on a hybrid decomposition technique and an improved back-propagation neural network
inferredcites →https://doi.org/10.1016/j.apenergy.2009.12.013openalex-extracted
On comparing three artificial neural networks for wind speed forecasting
inferredcites →https://doi.org/10.1016/j.apenergy.2013.02.002openalex-extracted
Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks
inferredcites →https://doi.org/10.1016/j.apenergy.2018.02.070openalex-extracted
A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
inferredcites →https://doi.org/10.1016/j.apenergy.2021.117766openalex-extracted
A review of wind speed and wind power forecasting with deep neural networks
inferredcites →https://doi.org/10.1016/j.apenergy.2022.119579openalex-extracted
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
inferredcites →https://doi.org/10.1016/j.apenergy.2022.119672openalex-extracted
M2STAN: Multi-modal multi-task spatiotemporal attention network for multi-location ultra-short-term wind power multi-step predictions
inferredcites →https://doi.org/10.1016/j.apenergy.2022.120601openalex-extracted
A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting
inferredcites →https://doi.org/10.1016/j.apenergy.2023.121587openalex-extracted
A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting
inferredcites →https://doi.org/10.1016/j.enconman.2017.06.021openalex-extracted
Non-parametric hybrid models for wind speed forecasting
inferredcites →https://doi.org/10.1016/j.enconman.2018.04.082openalex-extracted
Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction
inferredcites →https://doi.org/10.1016/j.enconman.2022.115433openalex-extracted
Deep non-crossing probabilistic wind speed forecasting with multi-scale features
inferredcites →https://doi.org/10.1016/j.enconman.2022.115590openalex-extracted
Quaternion convolutional long short-term memory neural model with an adaptive decomposition method for wind speed forecasting: North aegean islands case studies
inferredcites →https://doi.org/10.1016/j.enconman.2023.116760openalex-extracted
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
inferredcites →https://doi.org/10.1016/j.eneco.2019.01.012openalex-extracted
Effect of Climate Change on wind speed and its impact on optimal power system expansion planning: The case of Chile
inferredcites →https://doi.org/10.1016/j.energy.2010.05.041openalex-extracted
Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs
inferredcites →https://doi.org/10.1016/j.energy.2015.08.039openalex-extracted
A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China
inferredcites →https://doi.org/10.1016/j.epsr.2022.108863openalex-extracted
Ensemble forecaster based on the combination of time-frequency analysis and machine learning strategies for very short-term wind speed prediction
inferredcites →https://doi.org/10.1016/j.eswa.2022.119129openalex-extracted
Multivariate selection-combination short-term wind speed forecasting system based on convolution-recurrent network and multi-objective chameleon swarm algorithm
inferredcites →https://doi.org/10.1016/j.ijepes.2022.108504openalex-extracted
Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach
inferredcites →https://doi.org/10.1016/j.knosys.2019.05.009openalex-extracted
Deep shared representation learning for weather elements forecasting
inferredcites →https://doi.org/10.1016/j.renene.2008.09.006openalex-extracted
Day-ahead wind speed forecasting using f-ARIMA models
inferredcites →https://doi.org/10.1016/j.renene.2013.08.011openalex-extracted
Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm
inferredcites →https://doi.org/10.1016/j.renene.2022.02.095openalex-extracted
Wind speed forecasting using multi-site collaborative deep learning for complex terrain application in valleys
inferredcites →https://doi.org/10.1016/j.renene.2022.09.036openalex-extracted
Promoting wind energy for sustainable development by precise wind speed prediction based on graph neural networks
inferredcites →https://doi.org/10.1016/j.renene.2022.09.114openalex-extracted
A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error
inferredcites →https://doi.org/10.1016/j.renene.2022.12.120openalex-extracted
A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network
inferredcites →https://doi.org/10.1016/j.renene.2022.12.124openalex-extracted
A CGRU multi-step wind speed forecasting model based on multi-label specific XGBoost feature selection and secondary decomposition
inferredcites →https://doi.org/10.1016/j.seta.2022.102535openalex-extracted
Ensemble wind speed forecasting system based on optimal model adaptive selection strategy: Case study in China
inferredcites →https://doi.org/10.1016/j.solener.2004.09.013openalex-extracted
Forecast of hourly average wind speed with ARMA models in Navarre (Spain)
inferredcites →https://doi.org/10.1038/nature14539openalex-extracted
Deep learning
inferredcites →https://doi.org/10.1038/s41558-020-0848-3openalex-extracted
Global wind patterns and the vulnerability of wind-dispersed species to climate change
inferredcites →https://doi.org/10.1038/s41560-020-00773-7openalex-extracted
A global assessment of extreme wind speeds for wind energy applications
inferredcites →https://doi.org/10.1038/s41560-023-01304-wopenalex-extracted
Climate change impacts on planned supply–demand match in global wind and solar energy systems
inferredcites →https://doi.org/10.1038/s43017-020-0101-7openalex-extracted
Climate change impacts on wind power generation
inferredcites →https://doi.org/10.1049/iet-rpg.2019.0614openalex-extracted
Short‐term wind power forecasting based on two‐stage attention mechanism
inferredcites →https://doi.org/10.1063/5.0108847openalex-extracted
Efficacy and application of the window-sliding ARIMA for daily and weekly wind speed forecasting
inferredcites →https://doi.org/10.1109/access.2021.3065502openalex-extracted
Wind Power Forecasting Using Attention-Based Recurrent Neural Networks: A Comparative Study
inferredcites →https://doi.org/10.1109/ictem56862.2023.10084298openalex-extracted
Wind Speed Forecasting and Probability Distribution Analysis Using Measured Data at Weather Stations in Iran
inferredcites →https://doi.org/10.1109/ssci47803.2020.9308323openalex-extracted
Wind speed prediction using multidimensional convolutional neural networks
inferredcites →https://doi.org/10.1109/tpwrs.2015.2468586openalex-extracted
ARIMA-Based Frequency-Decomposed Modeling of Wind Speed Time Series
inferredcites →https://doi.org/10.1109/tste.2018.2844102openalex-extracted
Spatio-Temporal Graph Deep Neural Network for Short-Term Wind Speed Forecasting
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-extracted
Long Short-Term Memory
inferredcites →https://doi.org/10.14428/esann/2021.es2021-25openalex-extracted
Deep Graph Convolutional Networks for Wind Speed Prediction
inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-extracted
Neural Machine Translation by Jointly Learning to Align and Translate
inferredcites →https://doi.org/10.5194/wes-1-1-2016openalex-extracted
Long-term research challenges in wind energy – a research agenda by the European Academy of Wind Energy
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