Grid integration challenges of wind energy: A review
SD Ahmed, FSM Al-Ismail, M Shafiullah… - Ieee …, 2020 - ieeexplore.ieee.org
The strengthening of electric energy security and the reduction of greenhouse gas
emissions have gained enormous momentum in previous decades. The integration of large …
emissions have gained enormous momentum in previous decades. The integration of large …
Short-term wind power forecasting approach based on Seq2Seq model using NWP data
Y Zhang, Y Li, G Zhang - Energy, 2020 - Elsevier
Wind power is one of the main sources of renewable energy. Precise forecast of the power
output of wind farms could greatly decrease the negative impact of wind power on power …
output of wind farms could greatly decrease the negative impact of wind power on power …
Short-term offshore wind speed forecast by seasonal ARIMA-A comparison against GRU and LSTM
Offshore wind power is one of the fastest-growing energy sources worldwide, which is
environmentally friendly and economically competitive. Short-term time series wind speed …
environmentally friendly and economically competitive. Short-term time series wind speed …
Short-term wind speed forecasting using recurrent neural networks with error correction
J Duan, H Zuo, Y Bai, J Duan, M Chang, B Chen - Energy, 2021 - Elsevier
As a type of clean energy, wind energy has been effectively used in power systems.
However, due to the influence of the atmospheric boundary layer, wind speed exhibits …
However, due to the influence of the atmospheric boundary layer, wind speed exhibits …
Adaptive VMD based optimized deep learning mixed kernel ELM autoencoder for single and multistep wind power forecasting
In this paper, an efficient new hybrid time series forecasting model combining variational
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
mode decomposition (VMD) and Deep learning mixed Kernel ELM (MKELM) Autoencoder …
Short-term wind speed prediction model based on GA-ANN improved by VMD
Y Zhang, G Pan, B Chen, J Han, Y Zhao, C Zhang - Renewable energy, 2020 - Elsevier
Wind power, as a potential new energy generation technology, is gradually developing
towards to the mainstream energy in the world. However, the inherent random volatility of …
towards to the mainstream energy in the world. However, the inherent random volatility of …
A short-term wind power prediction model based on CEEMD and WOA-KELM
Y Ding, Z Chen, H Zhang, X Wang, Y Guo - Renewable Energy, 2022 - Elsevier
Effective short-term wind power prediction is crucial to the optimal dispatching, system
stability, and operation cost control of a power system. In order to deal with the intermittent …
stability, and operation cost control of a power system. In order to deal with the intermittent …
A novel hybrid model based on nonlinear weighted combination for short-term wind power forecasting
J Duan, P Wang, W Ma, S Fang, Z Hou - International Journal of Electrical …, 2022 - Elsevier
Wind power forecasting plays a vital role in enhancing the efficiency of power grid operation
and increasing the competitiveness of power market. In this paper, a novel hybrid …
and increasing the competitiveness of power market. In this paper, a novel hybrid …
A hybrid deep learning architecture for wind power prediction based on bi-attention mechanism and crisscross optimization
A Meng, S Chen, Z Ou, W Ding, H Zhou, J Fan, H Yin - Energy, 2022 - Elsevier
Accurate wind power forecasting is of great significance for power system operation. In this
study, a triple-stage multi-step wind power forecasting approach is proposed by applying …
study, a triple-stage multi-step wind power forecasting approach is proposed by applying …
On the origins of randomization-based feedforward neural networks
PN Suganthan, R Katuwal - Applied Soft Computing, 2021 - Elsevier
This letter identifies original independent works in the domain of randomization-based
feedforward neural networks. In the most common approach, only the output layer weights …
feedforward neural networks. In the most common approach, only the output layer weights …