Wind power prediction using stacking and transfer learning.

Xu Cheng, Yu Cao, Zhiyuan Song, Chenguang Zhang

Abstract read

In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Enhanced Dhole Optimization Algorithm for hyperparameter tuning of a TCN-BiGRU-MHA hybrid architecture for wind power forecasting.Scientific reports · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xu ChengCollege of Economics and Management, Shenyang Agricultural University, Shenyang, 110886, China.

Yu CaoSchool of Information Science and Control Engineering, Liaoning Petrochemical University, Fushun, 113005, China. caoyu@lnpu.edu.cn.

Zhiyuan SongSchool of Information Science and Control Engineering, Liaoning Petrochemical University, Fushun, 113005, China.

Chenguang ZhangSchool of Artificial Intelligence and Software, Liaoning Petrochemical University, Fushun, 113005, China.

Funding

Applied Basic Research Program Project of Liaoning Province 2023JH26/10300021, 2022JH2/101300272,2023JH26/10300013Fundamental scientific research project of Liaoning Provincial Department of Education LJ212410148031

6 · The paper itself

Abstract

As countries focus more on renewable energy, especially wind power, predicting wind power output accurately is crucial for managing power grids and saving costs. This paper presents a new method for ultra-short-term wind power prediction using a combination of Stacking and Transfer Learning. To improve accuracy, we first reduce the data dimensions using PCA. Then, we use several models like LSTM, BiLSTM, GRU, BiGRU, and LSTM-Attention as base learners. These models are combined using a Stacking ensemble model. We also use Transfer Learning to share trained models between tasks, which helps improve performance. Tests with real data from a wind farm show that our method is more accurate than single models.

Indexed as

Long short-term memoryPrincipal component analysisStacking ensemble modelTransfer learningUltra-short-term predictionWind power

Identifiers

PMID40185825

DOI10.1038/s41598-025-96262-6

PMCPMC11971366

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.