Ensemble probabilistic wind power forecasting with multi-scale features

Yun Wang, Tuo Chen, Runmin Zou, Dongran Song, Fan Zhang and Lingjun Zhang

Renewable Energy, 2022, vol. 201, issue P1, 734-751

Abstract: The uncertainty of wind power forecasting has a significant impact on the decision-making process of power system operators. Herein, to comprehensively characterize this uncertainty, a novel ensemble probabilistic forecasting model is developed in this study. First, the best model inputs were selected to forecast future wind power based on the value of maximum information coefficient, which measures both linear and nonlinear relationships between two random variables. Second, to extract sufficient and useful features from historical wind power data, a multi-scale feature extraction module based on average pooling, convolutional neural network, bidirectional long short-term memory, and attention mechanism was proposed. Third, five multi-scale deep density neural networks with different distribution-based loss functions and the proposed multi-scale feature extraction module were designed as base probabilistic forecasters to characterize the uncertainty from various aspects. Finally, an ensemble framework that combines five multi-scale deep density neural networks was built to determine the optimal ensemble weights for all base forecasters by simultaneously minimizing the pinball loss and continuous ranked probability score. The proposed ensemble model was implemented on four real-world wind power datasets. The results demonstrated the effectiveness of the proposed ensemble strategy and the designed multi-scale feature extraction module for probabilistic wind power forecasting.

Keywords: Probabilistic wind power forecasting; Deep density neural network; Ensemble learning; Multi-scale features; Multi-objective optimization (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014812201624X
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:renene:v:201:y:2022:i:p1:p:734-751

DOI: 10.1016/j.renene.2022.10.122

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

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