The attention-assisted ordinary differential equation networks for short-term probabilistic wind power predictions
To improve the practicality, data-driven techniques of predicting the wind power generation
and its uncertainty still need to address three technical challenges, uplifting the prediction …
and its uncertainty still need to address three technical challenges, uplifting the prediction …
Short-term wind power forecasting based on Attention Mechanism and Deep Learning
B Xiong, L Lou, X Meng, X Wang, H Ma… - Electric Power Systems …, 2022 - Elsevier
Wind power forecasting is an important means to alleviate the pressure of peak and
frequency regulation in power systems and improve the acceptance capacity of wind power …
frequency regulation in power systems and improve the acceptance capacity of wind power …
A hybrid deep learning model and comparison for wind power forecasting considering temporal-spatial feature extraction
H Zhen, D Niu, M Yu, K Wang, Y Liang, X Xu - Sustainability, 2020 - mdpi.com
The inherent intermittency and uncertainty of wind power have brought challenges in
accurate wind power output forecasting, which also cause tricky problems in the integration …
accurate wind power output forecasting, which also cause tricky problems in the integration …
An algorithm for forecasting day-ahead wind power via novel long short-term memory and wind power ramp events
Y Cui, Z Chen, Y He, X Xiong, F Li - Energy, 2023 - Elsevier
Reliable wind power and ramp event prediction is essential for the safe and stable operation
of electric power systems. Previous prediction methods struggled to forecast large …
of electric power systems. Previous prediction methods struggled to forecast large …
Hourly day-ahead wind power forecasting with the EEMD-CSO-LSTM-EFG deep learning technique
AS Devi, G Maragatham, K Boopathi… - Soft …, 2020 - search.proquest.com
Wind power forecasting has gained significant attention due to advances in wind energy
generation in power frameworks and the uncertain nature of wind. In this manner, to …
generation in power frameworks and the uncertain nature of wind. In this manner, to …
Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network
J Zhang, H Li, P Cheng, J Yan - Energies, 2024 - mdpi.com
High-precision spatial-temporal wind power prediction technology is of great significance for
ensuring the safe and stable operation of power grids. The development of artificial …
ensuring the safe and stable operation of power grids. The development of artificial …
Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model
D Niu, L Sun, M Yu, K Wang - Energy, 2022 - Elsevier
Accurate and reliable wind power forecasting (WPF) is significant for ensuring power
systems' economic operation and safe dispatching and for reducing the technical and …
systems' economic operation and safe dispatching and for reducing the technical and …
Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty
Wind energy is an important renewable clean energy resource. However, the stochastic and
volatile nature of wind power brings significant challenges to the power system's reliable …
volatile nature of wind power brings significant challenges to the power system's reliable …
Ultra-short-term wind power interval prediction based on multi-task learning and generative critic networks
J Shi, B Wang, K Luo, Y Wu, M Zhou, J Watada - Energy, 2023 - Elsevier
Wind power forecast has played a significant role in modern power systems operation.
Meanwhile, interval forecast, as a practical way to represent wind power uncertainty, has …
Meanwhile, interval forecast, as a practical way to represent wind power uncertainty, has …