A statistical-based optimization method to integrate thermal comfort in the Che design of low energy consumption building

被引:23
作者
Hawila, Abed Al Waheed [1 ]
Merabtine, Abdelatif [2 ,3 ]
机构
[1] Univ Technol Troyes, Inst Charles Delaunay, 12 Rue Marie Curie,CS 42060, F-10004 Troyes, France
[2] EPF Sch Engn, 2 Rue Fernand Sastre, F-10430 Rosieres Pres Troyes, France
[3] Univ Reims, SFR Condorcet FR CNRS 341, Lab Thermomech, GRESPI, Campus Moulin Housse, F-51687 Reims, France
关键词
Thermal comfort; Energy savings; Design of experiments; Sensitivity analysis; Optimization; Analysis of variance; PREDICTIVE CONTROL; NEURAL-NETWORK; CONSERVATION; MODELS;
D O I
10.1016/j.jobe.2020.101661
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
It is necessary to design energy-efficient buildings so that a trade-off between energy-savings and occupants' thermal comfort is fulfilled. Advanced thermal comfort-based control strategies have been proposed for this purpose. However, such an approach could consume energy as the conventional one if the building is poorly designed. The aim of this study is to propose a method that integrates thermal comfort in the design of energyefficient buildings. The use of sensitivity analysis and an optimization approach to identify the values of design parameters represent its core steps. The meta-modeling approach based on the design of experiments technique is adopted to perform the sensitivity analysis. Then, the obtained meta-models are used to optimize building design for the intended objectives. A case study is selected to test the proposed method. Theresults indicated that implementing the suggested strategy leads to about 20% of heating energy-savings compared to the base case while significantly enhancing occupant thermal comfort. Moreover, the results indicated that a reduction of about 22% of heating energy can be achieved compared to the comfort controlled case while it consumes 4% more if the comfort control is applied to the optimized design while maiainining consistent thermal comfort conditions.
引用
收藏
页数:13
相关论文
共 32 条
[1]   Computational intelligence techniques for HVAC systems: A review [J].
Ahmad, Muhammad Waseem ;
Mourshed, Monjur ;
Yuce, Baris ;
Rezgui, Yacine .
BUILDING SIMULATION, 2016, 9 (04) :359-398
[2]   Application of passive measures for energy conservation in buildings - a review [J].
Amirifard, Farhad ;
Sharif, Seyed Amirhosain ;
Nasiri, Fuzhan .
ADVANCES IN BUILDING ENERGY RESEARCH, 2019, 13 (02) :282-315
[3]  
[Anonymous], 2005, 7730: Ergonomics of the thermal environment Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria, V3, pe615
[4]  
ANSI/ASHRAE, 2002, SHRAE GUID, P14
[5]   Neural network and polynomial approximated thermal comfort models for HVAC systems [J].
Castilla, M. ;
Alvarez, J. D. ;
Ortega, M. G. ;
Arahal, M. R. .
BUILDING AND ENVIRONMENT, 2013, 59 :107-115
[6]  
Cen E.N., 2007, EUR COMM STAND, V3, P1, DOI [10.1520/e2019-03r13, DOI 10.1520/E2019-03R13]
[7]   Improving the energy performance of residential buildings: A literature review [J].
De Boeck, L. ;
Verbeke, S. ;
Audenaert, A. ;
De Mesmaeker, L. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2015, 52 :960-975
[8]   A review of thermal comfort models and indicators for indoor environments [J].
Enescu, Diana .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2017, 79 :1353-1379
[9]   Neural networks based predictive control for thermal comfort and energy savings in public buildings [J].
Ferreira, P. M. ;
Ruano, A. E. ;
Silva, S. ;
Conceicao, E. Z. E. .
ENERGY AND BUILDINGS, 2012, 55 :238-251
[10]   Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings [J].
Garnier, Antoine ;
Eynard, Julien ;
Caussanel, Matthieu ;
Grieu, Stephane .
APPLIED SOFT COMPUTING, 2015, 37 :847-862