电力系统保护与控制2024，Vol.52Issue(4)：100-108,9.DOI:10.19783/j.cnki.pspc.230914
融合深度误差反馈学习和注意力机制的短期风电功率预测
Short-term wind power forecasting with the integration of a deep error feedback learning and attention mechanism
摘要
Abstract
To enhance the accuracy of wind power forecasting,a short-term wind power forecasting method is proposed,one that synergistically integrates deep feedback learning with attention mechanisms.First,the historical data of numerical weather prediction(NWP)from the wind farm is taken as the original input.A dual-layer long short-term memory(LSTM)-based learning model is used for the preliminary prediction of wind power.Next,an error estimation model is established based on an extreme gradient boosting(XGBoost)algorithm.This enables fast estimation of the initial prediction errors given the future NWP data.Then,complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)is used to decompose the initial prediction errors into error sequences of different frequency bands.These serve as an additional feedback input for the secondary prediction of wind power.Also,an attention mechanism is introduced into the secondary prediction model to dynamically allocate weights to the wind power forecasting and error sequences and thereby instructing the prediction model to fully mine and learn the key features related to the prediction errors during the learning process.Finally,the simulation results indicate that the proposed method can remarkably enhance the reliability of short-term wind power forecasting.关键词
风电功率预测/深度学习/反馈学习/长短时记忆单元/注意力机制Key words
wind power forecasting/deep learning/feedback learning/LSTM/attention mechanism引用本文复制引用
胡宇晗,朱利鹏,李佳勇,李杨,曾杨,郑李梦千,帅智康..融合深度误差反馈学习和注意力机制的短期风电功率预测[J].电力系统保护与控制,2024,52(4):100-108,9.基金项目
This work is supported by the National Natural Science Foundation of China(No.52207094 and No.52377095). 国家自然科学基金项目资助(52207094,52377095)博士后创新型人才计划项目资助(BX20220100) （No.52207094 and No.52377095）
