Short-term wind speed forecasting based on spatial-temporal graph transformer networks
出版年份 2022 全文链接
标题
Short-term wind speed forecasting based on spatial-temporal graph transformer networks
作者
关键词
-
出版物
ENERGY
Volume 253, Issue -, Pages 124095
出版商
Elsevier BV
发表日期
2022-04-28
DOI
10.1016/j.energy.2022.124095
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- Spatio-temporal Wind Speed Prediction of Multiple Wind Farms Using Capsule Network
- (2021) Ling Zheng et al. RENEWABLE ENERGY
- Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network
- (2020) Ying-Yi Hong et al. ENERGY
- A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting
- (2019) Xinsong Niu et al. APPLIED ENERGY
- Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting
- (2019) Yong Chen et al. ENERGY CONVERSION AND MANAGEMENT
- Hybrid wind energy forecasting and analysis system based on divide and conquer scheme: A case study in China
- (2019) Wendong Yang et al. JOURNAL OF CLEANER PRODUCTION
- Hybrid forecasting system based on an optimal model selection strategy for different wind speed forecasting problems
- (2019) Qingguo Zhou et al. APPLIED ENERGY
- A combined forecasting model for time series: Application to short-term wind speed forecasting
- (2019) Zhenkun Liu et al. APPLIED ENERGY
- Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model
- (2019) Yongqi Liu et al. APPLIED ENERGY
- Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm
- (2019) Ling-Ling Li et al. JOURNAL OF CLEANER PRODUCTION
- Learning Temporal and Spatial Correlations Jointly: A Unified Framework for Wind Speed Prediction
- (2019) Qiaomu Zhu et al. IEEE Transactions on Sustainable Energy
- A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
- (2018) Jingjing Song et al. APPLIED ENERGY
- Wind Speed Prediction with Spatio–Temporal Correlation: A Deep Learning Approach
- (2018) Qiaomu Zhu et al. Energies
- Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
- (2018) Jie Chen et al. ENERGY CONVERSION AND MANAGEMENT
- Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network
- (2018) Hui Liu et al. ENERGY CONVERSION AND MANAGEMENT
- Short-term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy
- (2018) Xiong Luo et al. IEEE Transactions on Industrial Informatics
- Spatio-temporal Graph Deep Neural Network for Short-term Wind Speed Forecasting
- (2018) Mahdi Khodayar et al. IEEE Transactions on Sustainable Energy
- Rough Deep Neural Architecture for Short-Term Wind Speed Forecasting
- (2017) Mahdi Khodayar et al. IEEE Transactions on Industrial Informatics
- Deep belief network based deterministic and probabilistic wind speed forecasting approach
- (2016) H.Z. Wang et al. APPLIED ENERGY
- Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA
- (2015) Osamah Basheer Shukur et al. RENEWABLE ENERGY
- Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach
- (2013) Kuilin Chen et al. APPLIED ENERGY
- Forecasting wind speed with recurrent neural networks
- (2012) Qing Cao et al. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
- Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model
- (2010) Erasmo Cadenas et al. RENEWABLE ENERGY
- Day-ahead wind speed forecasting using f-ARIMA models
- (2008) Rajesh G. Kavasseri et al. RENEWABLE ENERGY
Create your own webinar
Interested in hosting your own webinar? Check the schedule and propose your idea to the Peeref Content Team.
Create NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started