基于CEEMDAN-CNN-GRU组合模型的短期负荷预测方法

Short-term load forecasting based on CEEMDAN-CNN-GRU combined model

负荷数据的高度随机性和不确定性,导致短期负荷预测的精度很难提升。为了提高短期负荷预测的准确度,提出了一种基于完全自适应噪声集合经验模态分解(CEEMDAN)与卷积神经网络(CNN)和门控循环单元(GRU)组合模型的短期负荷预测方法。首先,利用CEEMDAN模型将复杂的原始负荷序列分解为几个相对简单的子序列;其次,利用卷积神经网络(CNN)和门控循环单元(GRU)建立各分量预测模型,将归一化后的分量输入训练模型,得到预测子序列;最后,将所有分量的结果汇总,得到最终预测结果。结果表明,与LSTM模型、GRU模型、CNN-GRU及CEEMDAN-GRU组合模型相比,CEEMDAN-CNN-GRU组合模型所测精度有了明显提升,平均提升了25.08%,23.59%,20.41%和13.53%。CEEMDAN-CNN-GRU组合模型能够提取历史负荷数据中的非线性特征,有效提升短期负荷预测精度,可为电力系统建设提供有力支撑。

The high randomness and uncertainty of load data make it difficult to improve the accuracy of short-term load forecasting.In order to improve the accuracy of short-term load forecasting,a short-term load forecasting method based on a combined model of fully adaptive noise ensemble empirical mode decomposition(CEEMDAN),convolutional neural network(CNN)and gated recurrent unit(GRU)was proposed.First,the CEEMDAN model was used to decompose the complex original load sequence into several relatively simple sub-sequences;secondly,CNN and GRU were used to establish a prediction model for each component,and the normalized components were input into the training model to obtain the predictive subsequence.Finally,the results of all components were summarized to get the final prediction results.Experimental results show that the CEEMDAN-CNN-GRU combined model has a significant improvement in accuracy compared with the LSTM model,GRU model,CNN-GRU and CEEMDAN-GRU combined model,with an average increase of 25.08%,23.59%,20.41%and 13.53%.The CEEMDAN-CNN-GRU combined model can extract nonlinear features from historical load data,effectively improve the accuracy of short-term load forecasting,and provide strong support for power system construction.