基于集成模型的混合神经网络电力负荷预测

Hybrid neural network power load forecasting based on ensemble model

中文关键词: 负荷预测  集成模型  Bagging集成算法  多特征  自注意力机制

英文关键词: load forecasting  ensemble model  Bagging ensemble algorithm  multiple features  self-attention mechanism

中文摘要:

      为了提高负荷预测的精度与泛化能力，提出了一种基于Bagging集成算法的GRU-BiLSTM-Self-attention模型。为充分提取高维输入数据的多个特征，该模型采用BiLSTM-Self-attention模型提取局部特征，采用GRU模型提取时序特征，从同一训练集中独立抽取样本子集并进行训练，对输出结果集成处理并得到最终的预测结果。选取南京某供电公司真实数据进行实验，并与LSTM神经网络、GRU神经网络、BiLSTM神经网络等预测模型进行对比。实验数据表明，该模型的均方根误差为50.770 3，准确率为97.36%。相较于其它用于对比的模型，该结果表明本模型在预测效果上具有一定程度的优势，说明所提出的模型具有更好的泛化能力与预测精度。

英文摘要:

      In order to improve the accuracy and generalization ability of load forecasting, a GRU-BiLSTM-Self-attention model based on Bagging integrated algorithm is proposed. In order to fully extract multiple features of high-dimensional input data, the model uses BiLSTM- Self- attention model to extract local features and GRU model to extract temporal features, independently extract sample subsets from the same training set and train them, integrate the output results and obtain the final prediction results. The real data of a power supply company in Nanjing are selected for the experiment and compared with the prediction models such as LSTM neural network, GRU neural network and BiSTM neural network.The experimental data show that the root mean square error of the model is 50.770 3, and the accuracy is 97.36% . Compared with other models used for comparison, the results show that this model has certain advantages in prediction effect, which shows that the proposed model has better generalization ability and prediction accuracy.