WGformer: A Weibull-Gaussian Informer based model for wind speed prediction
Accurate wind speed forecasting can improve energy management efficiency and promote
the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind …
the use of renewable energy. However, the inherent nonlinearity and fluctuation of wind …
Short-term wind speed forecasting based on a hybrid model of ICEEMDAN, MFE, LSTM and informer
W Xinxin, S Xiaopan, A Xueyi, L Shijia - Plos one, 2023 - journals.plos.org
Wind energy, as a kind of environmentally friendly renewable energy, has attracted a lot of
attention in recent decades. However, the security and stability of the power system is …
attention in recent decades. However, the security and stability of the power system is …
An innovative interpretable combined learning model for wind speed forecasting
P Du, D Yang, Y Li, J Wang - Applied Energy, 2024 - Elsevier
Wind energy is taken as one of the most potential green energy sources, whose accurate
and stable prediction is important to improve the efficiency of wind turbines as well as to …
and stable prediction is important to improve the efficiency of wind turbines as well as to …
Wind speed forecasting based on EMD and GRNN optimized by FOA
D Niu, Y Liang, WC Hong - Energies, 2017 - mdpi.com
As a kind of clean and renewable energy, wind power is winning more and more attention
across the world. Regarding wind power utilization, safety is a core concern and such …
across the world. Regarding wind power utilization, safety is a core concern and such …
Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy
J Wang, Y Li - Applied energy, 2018 - Elsevier
Forecasting wind speed accurately is a key task in the planning and operation of wind
energy generation in power systems, and its importance increases with the high integration …
energy generation in power systems, and its importance increases with the high integration …
Wind speed forecasting using deep learning and preprocessing techniques
E Ammar, G Xydis - International Journal of Green Energy, 2024 - Taylor & Francis
Most forecasting algorithms are tuned to a specific location or dataset and will not perform
well in other situations. Some wind speed data might contain outliers, missed values, or …
well in other situations. Some wind speed data might contain outliers, missed values, or …
Advanced deep learning approach for probabilistic wind speed forecasting
M Afrasiabi, M Mohammadi… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
One of the critical challenges in wind energy development is the uncertainty quantification.
Prior knowledge about the wind speed in look-ahead times in shape of probabilistic …
Prior knowledge about the wind speed in look-ahead times in shape of probabilistic …
A novel loss function of deep learning in wind speed forecasting
Wind speed forecasting is an essential task in improving the efficiency of the energy supply.
Currently, deep learning models have become extremely popular, where the traditional …
Currently, deep learning models have become extremely popular, where the traditional …
Statistical wind speed forecasting models for small sample datasets: Problems, Improvements, and prospects
Wind speed forecasting models have seen significant development and growth in recent
years. In particular, hybrid models have been emerging since the last decade. Hybrid …
years. In particular, hybrid models have been emerging since the last decade. Hybrid …
Current perspective on the accuracy of deterministic wind speed and power forecasting
The intermittent nature of wind energy raised multiple challenges to the power systems and
is the biggest challenge to declare wind energy a reliable source. One solution to overcome …
is the biggest challenge to declare wind energy a reliable source. One solution to overcome …