基于门控循环单元与误差修正的短期负荷预测

针对负荷预测模型迭代训练过程中存在误差积累的问题，提出结合叠式双向门控循环单元（SBiGRU）、完整自适应噪声集成经验模态分解（CEEMDAN）和误差修正的组合预测模型. 建立SBiGRU模型学习在气温、日期类型影响下负荷序列的时序特征，误差特征体现在SBiGRU模型预测产生的误差序列中；使用CEEMDAN算法将误差序列分解为数个本征模态函数（IMF）分量与趋势分量，对每项分量再次建立SBiGRU模型进行学习与预测，并对各分量的预测值进行序列重构，得到误差的预测结果；对预测结果进行求和以修正误差. 模型评估结果表明，组合模型的预测准确精度为98.86%，与SBiGRU、BiRNN、支持向量回归等方法相比，该模型具有更好的精度.

Short term load forecasting based on gated recurrent unit and error correction

A combined prediction model combining stacked bidirectional gated recurrent unit (SBiGRU), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and error correction was proposed, aiming at the problem of error accumulation in iterative training process of load forecasting model. The SBiGRU model was established to learn the time series characteristics of load series under the influence of temperature and date type, and the error characteristics were reflected in the error series generated in the prediction process of SBiGRU model. Then CEEMDAN algorithm was used to decompose the error series into several intrinsic mode function (IMF) components and trend components. For each component, SBiGRU model was established again for learning and forecasting, and the predicted values of all components were reconstructed to obtain the error prediction results. Finally, the prediction results were summed to correct the error. Model evaluation results show that the prediction accuracy of the combined model is 98.86%. Compared with SBiGRU, BiRNN, support vector regression, ect., the combined model has better accuracy.