电气技术2025，Vol.26Issue(9)：34-44,11.
基于二次分解和混合深度神经网络的短期风电功率预测
Short term wind power forecasting model based on secondary decomposition and hybrid deep neural network
摘要
Abstract
Given the volatility and randomness of wind power,a model for short term wind power forecasting which utilizes secondary mode decomposition and an secretary bird optimization algorithm(SBOA)-optimized temporal convolutional netwaork(TCN)-bidirectional gate recurrent unit(BiGRU)-Attention mechanism to enhance prediction accuracy.Firstly,complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)and variational mode decomposition(VMD)algorithms are applied for secondary mode decomposition of the wind power time series.Secondly,the decomposed sub-series are fed into the SBOA-TCN-BiGRU-Attention network for combined prediction,with the SBOA optimizing the neural network's hyperparameters to avoid local optima.Finally,the predicted values of the sub-series are aggregated to derive the final result.The simulation findings indicate the proposed combined forecasting method predicts short term wind power with high accuracy.关键词
风电功率预测/二次分解/秘书鸟优化算法/时序卷积网络Key words
wind power forecasting/secondary decomposition/secretary bird optimization algo-rithm/temporal convolutional network引用本文复制引用
何宁静,张程..基于二次分解和混合深度神经网络的短期风电功率预测[J].电气技术,2025,26(9):34-44,11.基金项目
国家自然科学基金资助项目(52377088)福建省财政厅专项(GY-Z220230)福建省自然基金(2023J01951) （52377088）
