电力系统保护与控制2025，Vol.53Issue(8)：117-129,13.DOI:10.19783/j.cnki.pspc.240939
基于超参数优化和误差修正的STAGN超短期风电功率预测
STAGN ultra-short-term wind power forecasting based on hyperparameter optimization and error correction
摘要
Abstract
To address the issues of data correlation and error correction adaptability in wind power forecasting models,an ultra-short-term wind power prediction method based on hyperparameter optimization and error correction unit switching mechanism is proposed.First,a spatiotemporal attention gated network(STAGN)forecasting model is developed,and hyperparameter optimization is carried out using the improved Kepler optimization algorithm.Second,an error correction adaptive unit is constructed by considering the nonlinear correlation between wind farm data and forecasting errors.Meanwhile,the temporal variation characteristics of wind speed are explored to construct a deep learning unit.On this basis,the error correction unit switching strategy based on the wind speed matrix gradient is proposed.Finally,the model is applied to power forecasting in an actual wind farm and compared with other models.The results show that the proposed method outperforms others in terms of forecasting accuracy and maintains high forecasting accuracy in wind farms with highly variable wind speeds,verifying its accuracy and applicability.关键词
超短期风电功率预测/改进开普勒算法/误差修正/风速矩阵梯度Key words
ultra-short-term wind power prediction/improved Kepler optimization algorithm/error correction/wind speed matrix gradient引用本文复制引用
潘超,王超,孙惠,孟涛..基于超参数优化和误差修正的STAGN超短期风电功率预测[J].电力系统保护与控制,2025,53(8):117-129,13.基金项目
This work is supported by the National Key Research and Development Program of China(No.2022YFB2404000). 国家重点研发计划项目资助(2022YFB2404000) （No.2022YFB2404000）
