针对气象因素对多元负荷变化的灵敏度差异及多元负荷间耦合强度的差异导致多任务学习(multi-task learning,MTL)预测模型精度受限的问题,该文提出一种MTL和单任务学习(single-task learning,STL)组合的多元负荷预测方法.首先使用基于长短期记忆(long and short-term memory,LSTM)网络的MTL模型提取多元负荷间的耦合信息进行初步预测;然后采用基于前置双重注意力长短期记忆(dual attention before LSTM,DABLSTM)网络的 STL 模型减少输入噪声进行二次预测;同时将初步的预测值输入STL模型,使得STL模型可以考虑未来的时序信息;最后,通过全连接层对两个模型的预测结果进行融合得到最终的预测结果.实验结果表明,所提组合模型相比单一的MTL和STL模型具有更高的预测精度.
Abstract
Aiming at the problem that the prediction accuracy of a multi-task learning(MTL)model is limited due to the difference in sensitivity of the meteorological factors to multi-load changes and the difference in coupling intensity between multivariate loads,a MTL and single-task learning(STL)-combined multi-loads forecasting method is proposed.Firstly,the MTL model based on the long and short-term memory(LSTM)network is used to extract the coupling information between multiple loads for preliminary prediction.Then the STL model based on the dual attention before the LSTM(DABLSTM)network is used to reduce the input noises for secondary prediction.The preliminary predicted values are fed into the single-task learning model,allowing the STL model to take future time series information into account.Finally,the prediction results of the two models are fused through the fully connected layer to obtain the final prediction result.The experimental results show that the proposed combined model has higher prediction accuracy compared to the single MTL or the STL model.
关键词
综合能源系统/多任务学习/单任务学习/长短时记忆网络/注意力机制/负荷预测
Key words
integrated energy systems/multi-tasking learning/single-task learning/long and short time neural networks/dual attention mechanism/load forecasting
维普
万方数据