Work

Wind power prediction using stacking and transfer learning

Year: 2025

Type: article

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.

Source: Scientific Reports

DOI: 10.1038/s41598-025-96262-6

Authors: Xu Cheng, Yu Cao, Zhiyuan Song, Chenguang Zhang

Institutions: Shenyang Agricultural University, Liaoning Shihua University

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,690