An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation

Hui Huang (), Qiliang Zhu, Xueling Zhu and Jinhua Zhang
Additional contact information
Hui Huang: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Qiliang Zhu: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Xueling Zhu: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China
Jinhua Zhang: School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450011, China

Energies, 2023, vol. 16, issue 4, 1-20

Abstract: With the increasing integration of wind and photovoltaic power, the security and stability of the power system operations are greatly influenced by the intermittency and fluctuation of these renewable sources of energy generation. The accurate and reliable short-term forecasting of renewable energy generation can effectively reduce the impacts of uncertainty on the power system. In this paper, we propose an adaptive, data-driven stacking ensemble learning framework for the short-term output power forecasting of renewable energy. Five base-models are adaptively selected via the determination coefficient (R 2 ) indices from twelve candidate models. Then, cross-validation is used to increase the data diversity, and Bayesian optimization is used to tune hyperparameters. Finally, base modes with different weights determined by minimizing the cross-validation error are ensembled using a linear model. Four datasets in different seasons from wind farms and photovoltaic power stations are used to verify the proposed model. The results illustrate that the proposed stacking ensemble learning model for renewable energy power forecasting can adapt to dynamic changes in data and has better prediction precision and a stronger generalization performance compared to the benchmark models.

Keywords: wind power forecast; photovoltaic power forecast; stacking ensemble; Bayesian optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/4/1963/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/4/1963/ (text/html)

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:gam:jeners:v:16:y:2023:i:4:p:1963-:d:1070664

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().