Boosted GRU model for short-term forecasting of wind power with feature-weighted principal component analysis

Yulong Xiao, Chongzhe Zou, Hetian Chi and Rengcun Fang

Energy, 2023, vol. 267, issue C

Abstract: Wind power is a clean resource that is widely used as a renewable energy source. Accurate wind power forecasting is important for the efficient and stable use of wind energy. The erratic stochastic nature of wind power generation and the complexity of the data pose a significant challenge for short-term forecasting. Extracting features from the complex wind power data can improve the prediction models, which is a key issue for short-term forecasting. In this paper, a feature-weighted principal component analysis (WPCA) method and an improved gated recurrent unit (GRU) neural network model with optimized hyperparameters using a particle swarm optimization (PSO) algorithm are proposed. Compared with other good machine learning models, the proposed hybrid WPCA-PSO-GRU model is used to perform power prediction for a real-world wind farm. The results show that the MAE and RMSE of the WPCA-PSO-GRU model are reduced by 5.3%–16% and 10%–16% respectively, and R2 is increased by 2.1%–3.1% compared to the conventional model. The proposed model can reduce the impact of noisy data on model training, randomness, and the volatility of wind power generation. This study can also have wide applicability with complex data samples.

Keywords: feature-Weighted; Principal component analysis; Particle swarm optimization; Gated recurrent neural network; Wind power forecasting (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544222033898
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:267:y:2023:i:c:s0360544222033898

DOI: 10.1016/j.energy.2022.126503

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 ().