Research on Multi-Objective Optimization of Building Energy Efficiency and Comfort Based on RBF Neural Network

HE Long; LYU Bao; MA Deyu

doi:10.13204/j.gyjzG21111518

Volume 53 Issue 10

Oct. 2023

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2023 > 53(10): 36-43,35

HE Long, LYU Bao, MA Deyu. Research on Multi-Objective Optimization of Building Energy Efficiency and Comfort Based on RBF Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(10): 36-43,35. doi: 10.13204/j.gyjzG21111518

Citation:

HE Long, LYU Bao, MA Deyu. Research on Multi-Objective Optimization of Building Energy Efficiency and Comfort Based on RBF Neural Network[J]. INDUSTRIAL CONSTRUCTION, 2023, 53(10): 36-43,35. doi: 10.13204/j.gyjzG21111518

Citation:

PDF( 2581 KB)

Research on Multi-Objective Optimization of Building Energy Efficiency and Comfort Based on RBF Neural Network

doi: 10.13204/j.gyjzG21111518

College of Architecture, Inner Mongolia University of Technology, Hohhot 010051, China

Received Date: 2021-11-15
Available Online: 2023-12-18

Abstract

Abstract

Building morphology, as a design factor to be focused on in the design phase of building scheme, has a direct impact on several performance indicators of the building. The meteorological parameters of different sub-climatic zones in severe cold regions were used as the research background, and the building form was decomposed into 8 quantitative factors, and the building energy consumption and the annual discomfort time of the building based on Thermal Environmental Conditions for Human Occupancy(ANSI/ASHRAE 55-2004) evaluation standard were used as the optimization indexes. Based on the EnergyPlus simulation platform, the original data of the evaluation indexes was obtained, the radial basis function neural network was introduced to establish the rapid response model between the influencing factors and the optimization indexes, combined with the orthogonal test method, the single-objective optimization calculation of the building energy consumption and the annual discomfort time in each study area were carried out, and the influence weight and optimization potential of the three morphological factors representing urban buildings in severe cold regions on different optimization indexes were analyzed. The optimal combination of energy saving and comfort based on morphological factors in each study area was further investigated. The results showed that the quantitative design parameters of building morphology had considerable optimization potential for reducing building energy consumption and building discomfort time throughout the year.
- building form,
- building energy efficiency,
- thermal comfort,
- RBF neural network

FullText(HTML)

References(29)

References

[1]	PANG Z H, O'NEILL Z, LI Y F, et al.The role of sensitivity analysis in the building performance analysis:a critical review[J]. Energy and Buildings, 2020, 209:1-28.
[2]	LEBRUN J, ANDRE P. IEA annex 30:bringing simulation to application.final report[R].Liège:Laboratoire de Thermodynamique, Université de Liège,1998:5-126.
[3]	洪烽桓,傅绍辉,徐岩,等.建筑师主导下基于能耗模拟的建筑形体与空间组织节能设计研究:以北京市门头沟区体育文化中心为例[J].西部人居环境学刊,2020,35(6):40-48.
[4]	ABDOU N, MGHOUCHI Y E, HAMDAOUI S, et al. Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco[J]. Building and Environment, 2021, 204:108-124.
[5]	DELGARM N, SAJADI B, KOWSARY F, et al. Multi-objective optimization of the building energy performance:a simulation-based approach by means of particle swarm optimization (PSO)[J]. Applied Energy, 2016(170):293-303.
[6]	JAVANROODI K, NIK V M, ADL-ZARRABI B. A multi-objective optimization framework for designing climate-resilient building forms in urban areas[C/OL] IOP Conference Series:Earth and Environmental Science. IOP Publishing, 2020, 588(3). https://iopscience.iop.org/article/10.1088/1755-1315/588/3/032036/meta.
[7]	CHEN K W, JANSSEN P, SCHLUETER A. Multi-objective optimisation of building form, envelope and cooling system for improved building energy performance[J]. Automation in Construction, 2018, 94:449-457.
[8]	YUE N H, LI L L, Alessandro Morandi,et al.A metamodel-based multi-objective optimization method to balance thermal comfort and energy efficiency in a campus gymnasium[J/OL].Energy & Buildings,2021,253[2021-11-15].https://doi.org/10.1016/j.enbuild.2021.111513.
[9]	原野,郭彬彬,徐宗武,等.建筑形体光热性能耦合设计:以寒冷地区高校教学楼建筑为例[J].建筑师,2022(02):76-82.
[10]	唐小谦,陈焕新,郭亚宾.基于数据挖掘的多联机能耗预测[J].制冷技术,2020,40(3):8-12 +23.
[11]	李紫微,林波荣,陈洪钟.建筑方案能耗快速预测方法研究综述[J].暖通空调,2018,48(5):1-8.
[12]	季文娟,顾永松.基于PSO-RBF的建筑能耗预测模型研究[J].建筑节能,2015,43(11):109-112.
[13]	陈锐彬,李泽奇,黄永益.基于BP神经网络模型的大型公共建筑冷负荷预测[J].建设科技,2019(1):38-42.
[14]	杨柳.建筑节能综合设计[M].北京:中国建材工业出版社,2014.
[15]	张峙,李雯喆,应小宇.低能耗办公建筑标准层平面形态参数化研究[J].南方建筑,2019(1):76-81.
[16]	端木琳,王振江,李祥立,等.区域建筑形状对围护结构冷负荷的影响分析[J].土木建筑与环境工程,2011,33(增刊1):1-5.
[17]	王超,张伶伶,吕宵.低能耗目标下的北方高大空间公共建筑形体导控研究[J].建筑学报,2020(增刊1):38-43.
[18]	贺龙,吕保.基于太阳辐射得热的严寒地区中庭空间形态比较研究[J].建筑技术,2021,52(7):867-870.
[19]	徐燊,江海华,王江华.五种气候区条件下建筑窗墙比对建筑能耗影响的参数研究[J].建筑科学,2019,35(4):91-95 ,90.
[20]	中华人民共和国住房和城乡建设部.公共建筑节能设计标准:GB 50189-2015[S].北京:中国建筑工业出版社,2015.
[21]	张辉,韩啸霖,弓南,等.基于RBF与正交试验的高层住宅被动式节能设计多元因素分析[J].工业建筑,2021,51(4):46-52 ,19.
[22]	中华人民共和国住房和城乡建设部.办公建筑设计标准:JGJ/T 67-2019[S].北京:中国建筑工业出版社,2019.
[23]	朱丹丹,燕达,王闯,等.建筑能耗模拟软件对比:DeST、EnergyPlus and DOE-2[J].建筑科学,2012,28(增刊2):213-222.
[24]	中华人民共和国住房和城乡建设部.建筑节能气象参数标准:JGJ/T 346-2014[S].北京:中国建筑工业出版社,2014.
[25]	成雄蕾,王雯翡,张成昱,等.基于数据驱动的办公建筑电耗预测方法[J].建筑节能(中英文),2021,49(2):36-42.
[26]	莫程凯,邰可欣,邓深元,等.基于智能算法的PID控制参数优化策略[J].信息技术与信息化,2021(9):222-223,227.
[27]	ANSI. Thermal environmental conditions for human occupancy:ASHRAE 55-1981[S].Atlanta, USA:American Society of Heating, 1981.
[28]	ANSI.Thermal environmental conditions for human occupancy:ASHRAE 55-2004[S]. Atlanta, USA:American Society of Heating, 2004.
[29]	ANSI.Thermal environmental conditions for human occupancy:ASHRAE 55-2017[S]. Atlanta, USA:American Society of Heating, 2017.