[1]王 茜,于军琪.基于相似日LM神经网络的高校图书馆能耗预测[J].西安建筑科技大学学报(自然科学版),2022,54(03):459-465.[doi:10.15986/j.1006-7930.2022.03.017]
 WANG Qian,YU Junqi.Energy consumption prediction model of a university library based on similar day selection and levenberg-marquardt neural network[J].J. Xi'an Univ. of Arch. & Tech.(Natural Science Edition),2022,54(03):459-465.[doi:10.15986/j.1006-7930.2022.03.017]
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基于相似日LM神经网络的高校图书馆能耗预测()
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西安建筑科技大学学报(自然科学版)[ISSN:1006-7930/CN:61-1295/TU]

卷:
54
期数:
2022年03期
页码:
459-465
栏目:
出版日期:
2022-06-28

文章信息/Info

Title:
Energy consumption prediction model of a university library based on similar day selection and levenberg-marquardt neural network
文章编号:
1006-7930(2022)03-0459-07
作者:
王 茜于军琪
(西安建筑科技大学 建筑设备科学与工程学院,陕西 西安 710055)
Author(s):
WANG Qian YU Junqi
(School of Building Services Science and Engineering, Xi'an Univ. of Arch. & Tech., Xi'an 710055,China)
关键词:
能耗预测 高校图书馆 相似日 列文伯格-马夸尔特算法
Keywords:
energy consumption forecast university library similar day Levenberg-Marquardt algorithm
分类号:
TU852
DOI:
10.15986/j.1006-7930.2022.03.017
文献标志码:
A
摘要:
图书馆在高校建筑中具有非常重要的地位,有较大的节能潜力.然而,近年来对于高校图书馆建筑节能的研究偏少,本文通过提出一种基于相似日LM(Levenberg-Marquardt)神经网络的高校图书馆能耗预测模型,为高校图书馆能耗研究提供参考.以我国某高校图书馆为例,首先通过统计分析的方法得到影响图书馆能耗较为重要的因素,即室内人员、开放策略及气温.然后利用模糊聚类法划分相似日,依据相似日将原有数据进行筛选.接着将处理后的数据对预测模型进行训练.最后将改进后的预测模型与原预测模型的各项指标进行对比分析.依据对比结果可知,改进后模型的平均绝对百分比误差和均方误差分别降低了1.28%和23.06,拟合度提高了0.0421.
Abstract:
As a major part of campus buildings, libraries have great potential of energy-saving. But there are very few recent studies focused on energy conservation in university library buildings. This paper explores energy use patterns in library buildings and proposes a energy consumption prediction model for campus libraries based on Levenberg-Marquardt neural network. The model may provide reference to future energy-saving efforts. A university library building in China was chosen as the research subject. By analyzing related data, we are able to find the factors affecting energy consumption of the library most, which are occupancy, outdoor air temperature, as well as the opening hours of the library. The similar day method was employed to train the model: Through soft clustering, the energy consumption profiles of each day was put into categories which were in turn used to as inputs. Compared with the traditional LM model, the optimized LM neural network had showed a decrease in absolute percentage error and mean square error by 1.28%and 23.06, respectively, and the fitting degree had been improved by 0.042 1.

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备注/Memo

备注/Memo:
收稿日期:2021-04-22修改稿日期:2022-05-23
基金项目:陕西省社发重点基金项目(2017ZDL-SF-16-5,2017ZDCXL-SF-03-02)
第一作者:王 茜(1996—),女,硕士生,主要研究方向:智能建筑. E-mail:781871461@qq.com.通信作者:于军琪(1969—),男,教授,博导,主要研究方向:智能建筑.E-mail:junqiyu@126.com
更新日期/Last Update: 2022-06-28