电力系统保护与控制2025，Vol.53Issue(1)：13-23,11.DOI:10.19783/j.cnki.pspc.240596
贝叶斯优化超参数的空时融合压缩残差网络在风速区间预测中的研究
Wind speed interval prediction using spatio-temporal fusion compressed residual networks with Bayesian optimized hyperparameters
摘要
Abstract
To address the challenge of high wind speed variability in wind farm planning,a small-sample-based spatio-temporal integration and compression deep residual point prediction model,spatio-temporal integration and compression deep residual(STiCDRS),is proposed.This model is designed to deeply explore the spatial and temporal characteristics within wind speed sequences to enhance the accuracy of point prediction.Initially,the spatio-temporal integration and compression deep residual network is employed to obtain point prediction results.Subsequently,an innovative hybrid model,STiCDRS-Gaussian process regression(STiCDRS-GPR),is introduced to achieve interval prediction results,thereby providing more reliable probabilistic forecasts of wind speed.The model utilizes a Bayesian optimization method for hyperparameter selection,ensuring efficient and automated tuning.Finally,the wind speed dataset from a wind farm in Inner Mongolia is used to compare the prediction results of the STiCDRS model with those of traditional classical models.Experimental results demonstrate that,in comparison to other models,the proposed STiCDRS-GPR model delivers superior point prediction accuracy,appropriate interval predictions,and reliable probabilistic forecasting outcomes,fully showcasing its considerable potential in the domain of wind speed forecasting.关键词
风速预测/时序卷积网络/STiCDRS模型/GPR区间预测/贝叶斯优化Key words
wind speed prediction/temporal convolutional network/STiCDRS model/GPR interval prediction/Bayesian optimization引用本文复制引用
伍耘,葛佳敏,王文烨,李小勇,车亮..贝叶斯优化超参数的空时融合压缩残差网络在风速区间预测中的研究[J].电力系统保护与控制,2025,53(1):13-23,11.基金项目
This work is supported by the National Key Research and Development Program of China(No.2021YFB2601504). 国家重点研发计划项目资助(2021YFB2601504) （No.2021YFB2601504）
