A novel DWTimesNet-based short-term multi-step wind power forecasting model using feature selection and auto-tuning methods

C Zhang, Y Wang, Y Fu, X Qiao, MS Nazir… - Energy Conversion and …, 2024 - Elsevier
The share of wind power in global electricity generation is increasing year by year, and the
prediction of wind power is a practical and necessary scientific research. In this paper, the …

[HTML][HTML] Short-term wind power prediction method based on deep clustering-improved Temporal Convolutional Network

Y Sheng, H Wang, J Yan, Y Liu, S Han - Energy Reports, 2023 - Elsevier
Carbon neutrality has become the global consensus, and wind power is one of the key
technologies to achieve carbon neutrality in the power system. However, the randomness …

[HTML][HTML] An advanced short-term wind power forecasting framework based on the optimized deep neural network models

SMJ Jalali, S Ahmadian, M Khodayar… - International Journal of …, 2022 - Elsevier
With the continued growth of wind power penetration into conventional power grid systems,
wind power forecasting plays an increasingly competitive role in organizing and deploying …

EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning

X Peng, H Wang, J Lang, W Li, Q Xu, Z Zhang, T Cai… - Energy, 2021 - Elsevier
Effective wind-power prediction enhances the adaptability of a wind power system to the
instability of wind power, which is beneficial for load and frequency regulation, helping to …

Ultra‐short‐term multi‐step wind power forecasting based on CNN‐LSTM

Q Wu, F Guan, C Lv, Y Huang - IET Renewable Power …, 2021 - Wiley Online Library
The fluctuation and intermission of large‐scale wind power integration is a serious threat to
the stability and security of the power system. Accurate prediction of wind power is of great …

A deep learning framework for day ahead wind power short-term prediction

P Xu, M Zhang, Z Chen, B Wang, C Cheng, R Liu - Applied sciences, 2023 - mdpi.com
Due to the increasing proportion of wind power connected to the grid, day-ahead wind
power prediction plays a more and more important role in the operation of the power system …

Short-term wind power forecasting based on VMD and a hybrid SSA-TCN-BiGRU network

Y Zhang, L Zhang, D Sun, K Jin, Y Gu - Applied Sciences, 2023 - mdpi.com
Wind power generation is a renewable energy source, and its power output is influenced by
multiple factors such as wind speed, direction, meteorological conditions, and the …

Short-term wind power prediction based on two-layer decomposition and BiTCN-BiLSTM-attention model

D Zhang, B Chen, H Zhu, HH Goh, Y Dong, T Wu - Energy, 2023 - Elsevier
In order to solve the security threat brought by the volatility and randomness of large-scale
distributed wind power, this paper proposed a wind power prediction model which integrates …

A novel deep learning approach for wind power forecasting based on WD-LSTM model

B Liu, S Zhao, X Yu, L Zhang, Q Wang - Energies, 2020 - mdpi.com
Wind power generation is one of the renewable energy generation methods which
maintains good momentum of development at present. However, its extremely intense …

An improved Wavenet network for multi-step-ahead wind energy forecasting

Y Wang, T Chen, S Zhou, F Zhang, R Zou… - Energy Conversion and …, 2023 - Elsevier
Accurate multi-step-ahead wind speed (WS) and wind power (WP) forecasting are critical to
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …