电力系统及其自动化学报2024，Vol.36Issue(8)：39-47,59,10.DOI:10.19635/j.cnki.csu-epsa.001373
结合贝叶斯优化及通道注意力的双端优化时序式风功率预测模型
Double-side Optimized Time-series Wind Power Prediction Model Combining Bayesian Optimization and Channel Attention
摘要
Abstract
Aimed at the problem of a lack of data-side parameter optimization and model-side structural optimization in the existing wind power time-series prediction model,a double-side optimized time-series wind power prediction model is proposed in this paper. First,Bayesian optimization is used to efficiently search and optimize the data-side parame-ters. Then,channel attention and convolutional neural network are used to construct a feature extraction module to en-hance the learning of the importance of input influencing factors by the model. Finally,the extracted features are accu-rately fitted using a bi-directional long short-term memory model. Results show that the proposed model can capture thechanging trend of wind power under different prediction scenarios and significantly improve the prediction accuracy.关键词
时序式风功率预测/双端优化/贝叶斯优化/通道注意力Key words
time-series wind power prediction/double-side optimization/Bayesian optimization/channel attention分类
信息技术与安全科学引用本文复制引用
荆志宇,李培强,林文婷..结合贝叶斯优化及通道注意力的双端优化时序式风功率预测模型[J].电力系统及其自动化学报,2024,36(8):39-47,59,10.基金项目
国家自然科学基金资助项目(52377097) （52377097）
