中国矿业大学 信息与控制工程学院,江苏 徐州 221116
School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116,China
历史数据不足会严重影响到长短时记忆网络(long short-term memory,LSTM)预测建筑负荷的精度.迁移并利用源域中其他相似建筑的用能数据,可以提高LSTM处理目标域中建筑的预测精度,但现有方法没有考虑预测过程中数据增加所导致的源域匹配关系变化问题.鉴于此,提出迁移学习引导的变源域LSTM建筑负荷预测方法.在执行过程中,根据源域建筑负荷与目标建筑负荷在最新窗口的关联度,实时调整要选择的源域建筑及其能耗数据,以确保源域与目标域始终保持较高的相似程度.在多个典型实例上的应用表明,相比传统的定源域迁移学习方法,所提变源域LSTM负荷预测方法可以始终保持较高的预测精度.
Insufficient historical data severely affects the accuracy of the long short-term memory(LSTM) in predicting building loads. Transfering and using the energy consumption data of other similar buildings in the source domain can improve the prediction accuracy of LSTM processing of the buildings in the target domain. However, the existing methods do not take into account the change of the source domain matching relationship caused by the increase in data during the prediction process. In view of this, an LSTM-based building load prediction method guided by transfer learning with variable source domain is proposed. During the execution process, according to the correlation between the load of the source domain building and that of the target building in new windows, the source domain building and its energy consumption data to be selected are adjusted in real time to ensure that the source and target domains always remain high similarity. Finally, the application on several typical examples shows that compared with the traditional fixed source domain transfer learning method, the proposed LSTM-based load prediction method guided by transfer learning with variable source domain can always maintain a higher prediction accuracy.