Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system

Yagang Zhang, Hui Wang, Jingchao Wang, Xiaodan Cheng, Tong Wang and Zheng Zhao

Energy, 2024, vol. 292, issue C

Abstract: Wind power is a critical source of renewable energy, but the variability of wind speed can impact the efficiency and reliability of wind power generation. To improve the accuracy of wind speed prediction, this paper proposes a novel system that combines deep learning and decomposition algorithms based on ensemble optimization methods. Firstly, the paper proposes an innovative hybrid modal decomposition (HMD) method that extracts accurate features from wind speed data using Singular Value Decomposition (SVD), modal number selection method, and Variational Mode Decomposition (VMD). Secondly, the variable screening method identifies relevant factors affecting wind speed as fixed inputs. Then, the stationary test method is used to identify the sequences of different features in the mode and establish different prediction models: a bidirectional long-term short-time neural network model (Bi-LSTM) model optimized by artificial hummingbird algorithm (AHA) is used for non-stationary sequences; an ARIMA model is used for stationary sequences. Data from Sotavento and Changma wind farms are used to test this forecasting system. Simulation experiments are carried out from multiple dimensions. The results show that all error indicators are significantly improved, and the proposed prediction system is better than other comparative forecasting schemes.

Keywords: Short-term wind forecasts; Hybrid mode decomposition; Optimization algorithm; Bi-directional long short-term memory; Auto regressive integrated moving average model (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:292:y:2024:i:c:s0360544224002639

DOI: 10.1016/j.energy.2024.130492

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