MULTI-FACTOR ANALYSIS OF PASSIVE ENERGY SAVING DESIGN FOR HIGH-RISE RESIDENCE BASED ON RBF AND ORTHOGONAL EXPERIMENT

ZHANG Hui; HAN Xiaolin; GONG Nan; ZHU Fucheng

doi:10.13204/j.gyjzG20042002

Volume 51 Issue 4

Aug. 2021

Turn off MathJax

Article Contents

Article Navigation > INDUSTRIAL CONSTRUCTION > 2021 > 51(4): 46-52,19

ZHANG Hui, HAN Xiaolin, GONG Nan, ZHU Fucheng. MULTI-FACTOR ANALYSIS OF PASSIVE ENERGY SAVING DESIGN FOR HIGH-RISE RESIDENCE BASED ON RBF AND ORTHOGONAL EXPERIMENT[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(4): 46-52,19. doi: 10.13204/j.gyjzG20042002

Citation:

ZHANG Hui, HAN Xiaolin, GONG Nan, ZHU Fucheng. MULTI-FACTOR ANALYSIS OF PASSIVE ENERGY SAVING DESIGN FOR HIGH-RISE RESIDENCE BASED ON RBF AND ORTHOGONAL EXPERIMENT[J]. INDUSTRIAL CONSTRUCTION, 2021, 51(4): 46-52,19. doi: 10.13204/j.gyjzG20042002

Citation:

PDF( 4252 KB)

MULTI-FACTOR ANALYSIS OF PASSIVE ENERGY SAVING DESIGN FOR HIGH-RISE RESIDENCE BASED ON RBF AND ORTHOGONAL EXPERIMENT

doi: 10.13204/j.gyjzG20042002

School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China

Received Date: 2020-04-20
Available Online: 2021-08-19

Abstract

Abstract

The goal of energy conservation design and the requirement of building performance have been constantly improved, which has put forward higher requirements for energy conservation design. Moreover, the nonlinear relationship of multiple factors that affect the energy saving design undoubtedly increases the difficulty of further tapping the potential of energy saving design. Aiming at the optimization of passive design of high-rise residential buildings, the paper introduced radial basis function (RBF) neural network and orthogonal experiment design range method, and discussed the influence of passive design factors on energy consumption of high-rise residential buildings under different energy saving targets of 50%, 65% and 75%. In this research, analysis model was set up, and different factors, such as exterior wall, exterior window, roof, floor, interior wall, window-wall ratio, building orientation and number of floors were selected. Meanwhile, based on radial basis function (RBF) neural network, the heating and cooling energy consumption rapid response model was established to explore different energy-saving targets, the influencing factors on the heating and cooling energy consumption relations as well as the influence of the priority order, through the orthogonal experiment design process. The experiment showed that 65% and 75% of the energy saving targets could make the energy saving effect of the outer wall and the outer window of the high-rise residence more prominent. The improvement of energy saving target had changed the influence ranking of passive factors, and the increase in energy efficiency requirements from 50% to 75% reduced the impact of passive factors on energy efficiency from 3% to 0.5%.
- high-rise residence,
- passive energy-saving design,
- radial basis function neural network,
- orthogonal experiment design

FullText(HTML)

References(27)

References

[1]	董海荣, 祁少明, 徐梦杰, 等. 寒冷和夏热冬冷地区墙体保温对居住建筑能耗影响的异同[J]. 工业建筑, 2017, 47(6):65-69.
[2]	陈华, 郭娟利, 贾怡红,等. 建筑节能参数对全年耗热量的影响研究[J]. 建筑节能, 2018, 46(1):90-94.
[3]	JAIN M, PATHAK K K. Thermal Modeling of Insulator for Energy Saving in Existing Residential Building[J]. Journal of Building Engineering, 2018(19):62-68.
[4]	方鹏飞. 高层住宅外窗设计形态对室内自然通风影响的研究[D]. 合肥:合肥工业大学, 2018.
[5]	罗建河, 石刚. 覃思源,等. 被动节能技术对岭南高层住宅生态性的影响[J]. 华南理工大学学报(自然科学版), 2019(7):58-67.
[6]	朱丽, 邵泽彪, 孙勇,等. 郑州地区高层住宅节能优化量化研究[J]. 建筑节能, 2016(4):100-103.
[7]	梅凌云, 吴杰, 张宇峰. 单体建筑及建筑群表面风压计算的湍流模型研究[J]. 建筑科学, 2017, 33(6):96-107.
[8]	丁磊. 天津地区高层住宅自然通风与建筑节能设计参数优化研究[D]. 天津:天津大学, 2017.
[9]	潘斐, 樊越胜, 王欢,等. 建筑间效应对自然通风住宅能耗影响的研究[J]. 建筑热能通风空调, 2019, 38(8):28-30.
[10]	FABRIZIO A, ROSA F DE M, et al. Optimization of Building Envelope Design for nZEBs in Mediterranean Climate:Performance Analysis of Residential Case Study[J]. Applied Energy, 2016, 183:938-957.
[11]	周淑玲. 基于零能耗可行性分析下的城市住宅模型研究[D]. 天津:天津大学, 2014.
[12]	李洁. 寒冷地区近零能耗高层住宅围护结构关键参数优化组合研究[D]. 济南:山东建筑大学, 2018.
[13]	中华人民共和国住房和城乡建设部.夏热冬冷地区居住建筑节能设计标准:JGJ 134-2010[S]. 北京:中国建筑工业出版社, 2010.
[14]	MELO A P, VERSAGE R S, SAWAYA G, et al. A Novel Surrogate Model to Support Building Energy Labelling System:A New Approach to Assess Cooling Energy Demand in Commerical Building[J]. Energy and Buildings, 2016, 131:233-247.
[15]	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.
[16]	王茹, 宋爽, 贺佳. 改进鲸鱼算法构建混合模型的建筑能耗预测[J]. 计算机测量与控制, 2020, 28(2):197-201 ,205.
[17]	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.
[18]	FAIZOLLAHZADEH A S, MAHMOUDI A, MESRI G T. Modeling and Simulation Controlling System of HVAC Using Fuzzy and Predictive (Radial Basis Function, RBF) Controllers[J]. Journal of Building Engineering, 2016(6):301-308.
[19]	MAY T O, LIVAS G A, JIMENEZ T M, et al. Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring[J]. Journal of Energy Engineering, 2020(2):146.
[20]	张淇, 程志辉. 基于ACO的BP-RBF算法在建筑基础选型中的应用[J]. 土木工程与管理学报, 2019, 36(5):187-191 ,199.
[21]	段冠囡, 王岳人. 超高层建筑暖通空调能耗精准预测仿真[J]. 计算机仿真, 2018, 35(12):317-320 , 379.
[22]	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(11):109-112.
[23]	戴坤成, 王贵评, 赵超. 基于PCA与RBF的建筑能耗预测建模[J]. 福州大学学报(自然科学版), 2015,43(4):512-516.
[24]	湖北省住房和城乡建设厅.低能耗居住建筑节能设计标准:DB42/T 559-2013[S]. 北京:中国建筑工业出版社, 2013.
[25]	夏麟. 上海住宅建筑低能耗的可行性及技术路线研究[J]. 建筑节能, 2017, 45(4):82-89.
[26]	张三明, 欧阳金龙, 卢玫珺. 高层住宅建筑能耗计算方法的探讨[J]. 工业建筑, 2007,37(增刊1):50-52.
[27]	夏博, 赵敬源, 宋德萱, 等. 上海高层住宅冬季室内热环境研究[J]. 四川建筑科学研究, 2014, 40(4):361-364.