A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

Z Qian, Y Pei, H Zareipour, N Chen - Applied energy, 2019 - Elsevier
With the continuous growth of wind power integration into the electrical grid, accurate wind
power forecasting is an important component in management and operation of power …

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 …

Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features

Z Zhao, S Yun, L Jia, J Guo, Y Meng, N He, X Li… - … Applications of Artificial …, 2023 - Elsevier
Accurate and reliable short-term forecasting of wind power is vital for balancing energy and
integrating wind power into a grid. A novel hybrid deep learning model is designed in this …

A hybrid attention-based deep learning approach for wind power prediction

Z Ma, G Mei - Applied Energy, 2022 - Elsevier
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …

Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting

G Zuo, J Luo, N Wang, Y Lian, X He - Journal of Hydrology, 2020 - Elsevier
Reliable and accurate streamflow forecasting is vital for water resource management. Many
streamflow prediction studies have demonstrated the excellent prediction ability of …

A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting

RG da Silva, MHDM Ribeiro, SR Moreno, VC Mariani… - Energy, 2021 - Elsevier
Wind energy is one of the sources which is still in development in Brazil. However, it already
represents 17% of the National Interconnected System. Due to the high level of uncertainty …

Monthly runoff time series prediction by variational mode decomposition and support vector machine based on quantum-behaved particle swarm optimization

Z Feng, W Niu, Z Tang, Z Jiang, Y Xu, Y Liu… - Journal of Hydrology, 2020 - Elsevier
Accurate monthly runoff prediction plays an important role in the planning and management
of water resources. However, owing to climate changes and human activities, natural runoff …

A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting

Y Hao, C Tian - Applied energy, 2019 - Elsevier
With the fast growth of wind power penetration into the electric grid, wind power forecasting
plays an increasingly significant role in the secure and economic operation of power …

[HTML][HTML] An overview of performance evaluation metrics for short-term statistical wind power forecasting

JM González-Sopeña, V Pakrashi, B Ghosh - Renewable and Sustainable …, 2021 - Elsevier
Wind power forecasting has become an essential tool for energy trading and the operation
of the grid due to the increasing importance of wind energy. Therefore, estimating the …

Prediction interval of wind power using parameter optimized Beta distribution based LSTM model

X Yuan, C Chen, M Jiang, Y Yuan - Applied Soft Computing, 2019 - Elsevier
Prediction interval of wind power (PIWP) is crucial to assessing the economic and safe
operation of the wind turbine and providing support for analysis of the stability of power …