摘要
为充分挖掘蕴含在电力负荷数据中的有效时序信息,提高短期电力负荷预测准确度,本文提出一种基于改进注意力机制的时间卷积网络(TCN)-长短期记忆(LSTM)网络负荷预测模型。首先,将时序数据输入TCN模型中进行时序特征提取;然后,将所提取的时序特征与非时序数据组合,并输入LSTM模型中进行训练;最后,采用贝叶斯优化方法进行超参数寻优以获得TCN-LSTM模型的最优参数,引入通过多层感知器(MLP)改进的注意力机制以减少历史信息丢失并加强重要信息的影响,完成短期负荷预测。通过对比多种深度学习模型的预测效果表明,本文所提模型的短期电力负荷预测准确度更高。
To fully explore the effective temporal information contained in power load data and improve the accuracy of short term power load prediction,this article proposes a power load forecasting model based on an improved attention mechanism for the temporal convolutional network(TCN)-long short term memory(LSTM)network.Firstly,the temporal data is input into the TCN model to extract temporal features.Then,the extracted temporal features are combined with non temporal to be input into the LSTM model for training.Finally,Bayesian optimization method is used for hyperparameter optimization to get the best parameters in TCN-LSTM.An attention mechanism improved by multi-layer perceptron(MLP)is introduced to reduce the loss of historical information and strengthen the influence of important information,completing short term load forecasting.By comparing the predictive performance of various deep learning models,it is verified that the model proposed in this article has higher accuracy in short term power load forecasting.
作者
刘伟
王洪志
LIU Wei;WANG Hongzhi(College of Electrical Information Engineering,Northeast Petroleum University,Daqing,Heilongjiang 163000)
出处
《电气技术》
2024年第10期8-14,共7页
Electrical Engineering
