电网技术2024，Vol.48Issue(4)：1510-1518,中插37-中插40,13.DOI:10.13335/j.1000-3673.pst.2023.0841
基于多任务学习和单任务学习组合模型的综合能源系统多元负荷预测
Multivariate-load Forecasting of Integrated Energy System Based on Combined Multi-task Learning and Single-task Learning Model
摘要
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分类
信息技术与安全科学引用本文复制引用
秦烁,赵健,徐剑,魏敏捷..基于多任务学习和单任务学习组合模型的综合能源系统多元负荷预测[J].电网技术,2024,48(4):1510-1518,中插37-中插40,13.基金项目
国家自然科学基金项目(U1936213).Project Supported by the National Natural Science Foundation of China(NSFC)(U1936213). （U1936213）
