基于LSTM和多任务学习的综合能源系统多元负荷预测

随着综合能源利用技术的不断发展与用户用能需求的多元化,现有单一负荷预测方法难以反映多元负荷间的耦合特性,精确的多元负荷预测将成为综合能源系统优化调度和经济运行的首要前提。基于此,提出一种以长短时记忆神经网络作为共享层的多任务学习负荷预测方法,经由共享层模拟多元负荷间的耦合特性,进而达到提升预测精度的目的。首先,以"硬共享机制+长短时记忆共享层"方式构建多任务学习负荷预测模型,利用共享机制学习不同子任务提供的耦合信息。其次,通过神经网络可解释性技术对离线训练结果进行可视化解释,证实了所构建模型能够利用子任务提供的耦合信息来提高预测精度。最后,与传统模型进行对比分析,结果表明所构建模型在预测精度和时间上具有更好的应用效果。

Multiple Load Prediction of Integrated Energy System Based on Long Short-term Memory and Multi-task Learning

With the continuous development of integrated energy utilization technologies and the diversification of users’energy consumption needs,the existing method for single type load prediction is difficult to reflect the coupling characteristics of multiple loads.Accurate multiple load prediction will become the primary prerequisite for optimal dispatch and economic operation of integrated energy systems.Based on this,a multi-task learning load prediction method is proposed by using a long short-term memory(LSTM)neural network as a shared layer.In this method,the coupling characteristics of multiple loads are simulated through the shared layer to improve the prediction accuracy.Firstly,a multi-task learning load prediction model is developed by means of"hard sharing mechanism+LSTM sharing layer",and the sharing mechanism is used to learn the coupling information provided by different subtasks.Secondly,the neural network interpretability technology is used to visually explain the offline training results,which demonstrates that the proposed model can use the coupling information provided by the subtasks to improve the prediction accuracy.Finally,compared with the traditional model,the results show that the proposed model has better effect on prediction accuracy and time.