A multivariable hybrid prediction model of offshore wind power based on multi-stage optimization and reconstruction prediction

Hao Wang, Jingzhen Ye, Linxuan Huang, Qiang Wang and Haohua Zhang

Energy, 2023, vol. 262, issue PA

Abstract: Offshore wind power prediction is the basis for safe operation and grid dispatch. However, it is difficult due to the high volatility. Aiming at the three shortcomings of current methods: lack of analysis of the impact of multiple variables; the reconstruction method of decomposition components often adopts the summation method; the traditional machine learning prediction methods are not accurate enough, while the deep learning methods are prone to overfitting. This paper proposes a multi-variable hybrid prediction model based on multi-stage optimization and reconstruction prediction. Firstly, the isolated forest is used for data preprocessing. Secondly, the power sequence is decomposed by the variational modal decomposition optimized by the gray wolf algorithm to reduce the non-stationarity. Thirdly, the kernel extreme learning machine optimized by sparrow algorithm is used to predict. Finally, the reconstruction prediction is carried out through the long short-term memory network. Compared with the traditional machine learning method and the deep learning method, the model is effectively improved on two European offshore datasets. Then the interval prediction based on this model further verifies the accuracy and reliability.

Keywords: Offshore wind power; Decomposition and reconstruction model; Optimization algorithm; Interval prediction (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222023106
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:262:y:2023:i:pa:s0360544222023106

DOI: 10.1016/j.energy.2022.125428

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().