针对波动性大、复杂性高的电力负荷数据预测精度低的问题，提出一种结合变分模态分解（variational mode decomposition, VMD）、改进鱼鹰优化算法（improved osprey optimization algorithm, IOOA）与双向门控循环单元（bidirectional gated recurrent unit, BiGRU）以及误差补偿（error compensation, EC）的混合电力负荷预测模型，称为VMD-IOOA-BiGRU-EC。首先，利用VMD对负荷序列进行初次分解，提取出多个模态和残差。然后，采用circle混沌映射、动态精英引导机制和“最优-随机均值”变异3种策略改进OOA优化BiGRU模型的相关超参数，以提升对初次分解的模态的预测效果；同时，针对初次分解产生的残差构建VMD-BiGRU模型，对其进行二次分解，再使用BiGRU对分解后的残差进行预测，即误差补偿。最后，将初次分解和二次分解后的各模态的预测结果进行叠加，得到最终的负荷预测值。在湖南省株洲市的真实电力负荷数据集上进行实验验证，结果显示所提方法的平均绝对误差、均方根误差和平均绝对百分比误差均低于其余对比模型，证明了其在处理复杂负荷数据时的有效性。

Aiming at the problem of low prediction accuracy of power load data with large volatility and high complexity, a hybrid power load forecasting model combining variational mode decomposition (VMD), improved osprey optimization algorithm (IOOA), bidirectional gated recurrent unit (BiGRU), and error compensation was proposed, called VMD-IOOA-BiGRU-EC. First, VMD was used to perform the initial decomposition of the load sequence to extract multiple modes and residuals. Then, three strategies circle chaos mapping, dynamic elite guidance mechanism, and "optimal-random mean" mutation were used to improve the relevant hyperparameters of the OOA optimized BiGRU model to enhance the prediction effect of the initially decomposed modes. Simultaneously, a VMD-BiGRU model was constructed for the residuals generated by the initial decomposition; the residuals are decomposed twice, and BiGRU was used to predict the decomposed residuals, constituting error compensation. Finally, the prediction results of each mode after the initial decomposition and the secondary decomposition were superimposed to obtain the final load prediction value. Experimental verification was conducted on a real power load dataset from Zhuzhou City, Hunan Province. The results show that the proposed method has lower average absolute error, root mean square error, and average absolute percentage error than other comparison models, demonstrating its effectiveness in processing complex load data.