一种多模态融合的建筑能源系统冷负荷超短期预测方法  

A multimodal fusion method for ultra-short-term prediction of cooling loads in building energy systems

作　　者：高正中 程雨盟 殷秀程 初永丽[2] GAO Zhengzhong;CHENG Yumeng;YIN Xiucheng;CHU Yongli(School of Electrical and Automation Engineering,Shandong University of Science and Technology,Qingdao 266590,China;School of Information and Electronic Engineering,Shandong Technology and Business University,Yantai 264005,China)

机构地区：[1]山东科技大学电气与自动化工程学院,山东青岛266590 [2]山东工商学院信息与电子工程学院,山东烟台264005

出　　处：《重庆理工大学学报（自然科学）》2024年第11期185-192,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：山东省自然科学基金项目(ZR2020MF071)。

摘　　要：当前关于建筑能源系统冷负荷超短期预测的研究只局限于构建单一模态的输入特征集,在一定程度上限制了冷负荷预测精度。为解决该问题,提出一种多模态融合的建筑能源系统冷负荷超短期预测方法。首先,为解决输入特征集形式单一的问题,基于建筑能源系统的总冷负荷与各用户单元冷负荷的历史数据,分别构建了类序列、类图像和类视频模态的3种输入特征集;其次,根据3种模态输入特征的数据结构特点,有针对性地构建了3种深度学习预测模型,分别为双向门控循环单元、时空神经网络、三维卷积神经网络,得到3种模态输入下的初步总冷负荷预测结果;最后,提出一种基于Stacking集成学习的多模态融合方法,对3种模态输入下各预测模型的初步预测结果进行二次学习,得到最终的总冷负荷预测结果。根据美国亚利桑纳州立大学能源系统的实际负荷数据进行测试,仿真结果表明:所提出方法能够有效地提升冷负荷超短期预测精度。The current research on the ultra-short-term prediction of building energy system cold load is confined to constructing the input feature set of a single modality,which somehow undermines the accuracy of cold load prediction.To address this,we propose a multimodal fusion method for ultra-short-term prediction of building energy system cold load.First,to solve the problem of a single input feature set,based on the historical data of the total cooling load of the building energy system and the cooling load of each user unit,three input feature sets of sequence-like,image-like,and video-like modalities are built respectively.Then,according to the data structure characteristics of the three modal input features,three deep learning prediction models are built in a targeted manner,namely,a two-way gated loop unit,spatiotemporal neural network,and three-dimensional convolutional neural network,and the preliminary total cooling load prediction results under the three modal inputs are obtained.Finally,a multimodal fusion method based on stacking integrated learning is proposed to carry out secondary learning on the preliminary prediction results of each prediction model under the three modal inputs to obtain the final total cooling load prediction results.Our simulation results from a test with the actual load data of the energy system of Arizona State University show our method effectively improves the ultra-short-term prediction accuracy of cold load.

关 键 词：多模态融合 Stacking集成学习 冷负荷 超短期预测 建筑能源系统 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]