基于长短期记忆网络的公共建筑短期能耗预测模型  

Short-term energy consumption prediction model of public buildings based on short-term memory network

作　　者：朱国庆[1] 刘显成[1] 田从祥 ZHU Guo-qing;LIU Xian-cheng;TIAN Cong-xiang(School of Urban Construction,Yangtze University,Jingzhou 434023,China;Yangtze University College of Arts and Sciences,Jingzhou 434020,China)

机构地区：[1]长江大学城市建设学院,湖北荆州434023 [2]长江大学文理学院,湖北荆州434020

出　　处：《吉林大学学报（工学版）》2024年第7期2009-2014,共6页Journal of Jilin University:Engineering and Technology Edition

基　　金：教育部产学合作协同育人项目(231103242251727);湖北省教育厅科学技术研究项目(2023-081);湖北省教育厅哲学社会科学研究项目(23Y155);荆州市科学技术局联合科研基金项目(2023LHX01)。

摘　　要：为了提高公共建筑短期能耗预测的精度、泛化能力和鲁棒性能,提出一种基于长短期记忆网络的公共建筑短期能耗预测模型。使用长短期记忆网络作为公共建筑能耗特征提取器,在不断迭代的过程中保留有价值的能耗历史数据,通过自主学习与自组织调整不同时序的输出,并引入灰色系统,减少所需样本数据数量和缩小误差。采用最小乘二法计算输出权值,获得长短期记忆网络下的预测值,将经反归一函数处理后的结果累减计算,得到建筑能耗短期预测值。实验结果证明:本文方法能耗预测能力优秀,可以有效地用于公共建筑能耗预测。In order to improve the accuracy,generalization and robustness of short-term energy consumption prediction for public buildings,a short-term energy consumption prediction model for public buildings based on short-term memory network was proposed.The long-term and short-term memory network is used as the energy consumption feature extractor of public buildings to retain valuable historical energy consumption data in the process of continuous iteration,adjust the output of different time sequences through autonomous learning and self-organization,and introduce the gray system to reduce the number of sample data required and reduce errors.The output weight value is calculated by the minimum multiplication method to obtain the prediction value under the long-term and short-term memory network.The short-term prediction value of building energy consumption is obtained by accumulating the results after the inverse normalization function processing.The experimental results show that the proposed method has excellent energy consumption prediction ability and can be effectively used for public building energy consumption prediction.

关 键 词：长短期记忆网络 灰色系统 公共建筑能耗 预测模型 反归一化函数 记忆单元 

分 类 号：TP267[自动化与计算机技术—检测技术与自动化装置]