Multi-variable optimization of thermal energy efficiency retrofitting of buildings using static modelling and genetic algorithms - A case study

被引:77
|
作者
Murray, Sean N. [1 ]
Walsh, Brendan P. [1 ]
Kelliher, Denis [2 ]
O'Sullivan, D. T. J. [1 ]
机构
[1] Univ Coll Cork, Dept Civil & Environm Engn, IERG, Cork, Ireland
[2] Univ Coll Cork, Dept Civil & Environm Engn, RUSO, Cork, Ireland
关键词
Multi-variable optimization; Genetic algorithms; Existing building retrofitting; Degree-days simulation; Energy modelling; Energy efficiency; MULTIOBJECTIVE OPTIMIZATION; DESIGN; STRATEGIES;
D O I
10.1016/j.buildenv.2014.01.011
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The retrofitting of existing buildings is an area of research that requires development in order to overcome the 'rule of thumb' based approach currently being undertaken. Simulation-based optimization is one approach that can assist consultant engineers, architects and other professionals who undertake retrofit projects. This paper presents a degree-days simulation technique coupled with a genetic algorithms optimization procedure to propose optimal retrofit solutions. The research is applied to a recently retrofitted case-study building. A comparison between the implemented retrofit solution and the simulation-based optimal solution is included to demonstrate the applicability of the research to real-world situations. This research demonstrates the necessity to carry out analysis of a project before retrofit works commence to ensure an optimal approach is taken in accordance with the project specific criteria. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码:98 / 107
页数:10
相关论文
共 50 条
  • [1] Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms
    Ferdyn-Grygierek, Joanna
    Grygierek, Krzysztof
    ENERGIES, 2017, 10 (10):
  • [2] Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms
    Salata, Ferdinando
    Ciancio, Virgilio
    Dell'Olmo, Jacopo
    Golasi, Iacopo
    Palusci, Olga
    Coppi, Massimo
    APPLIED ENERGY, 2020, 260
  • [3] Multi-variable optimization of an ytterbium-doped fiber laser using genetic algorithm
    Hashemi, Somaye Sadat
    Sabouri, Saeed Ghavami
    Khorsandi, Alireza
    OPTICA APPLICATA, 2015, 45 (03) : 355 - 367
  • [4] MULTI-VARIABLE OPTIMIZATION MODELS FOR BUILDING ENVELOPE DESIGN USING ENERGYPLUS SIMULATION AND METAHEURISTIC ALGORITHMS
    Grygierek, Krzysztof
    Ferdyn-Grygierek, Joanna
    ARCHITECTURE CIVIL ENGINEERING ENVIRONMENT, 2019, 12 (02) : 81 - 90
  • [5] Multi-objective Optimization of Semi-active Control of Seismically Exited Buildings Using Variable Damper and Genetic Algorithms
    Pourzeynali, S.
    Malekzadeh, M.
    Esmaeilian, F.
    INTERNATIONAL JOURNAL OF ENGINEERING, 2012, 25 (03): : 265 - 276
  • [6] PARETO BASED MULTI-OBJECTIVE OPTIMIZATION OF SOLAR THERMAL ENERGY STORAGE USING GENETIC ALGORITHMS
    Khalkhali, Abolfazl
    Sadafi, Mohamadhosein
    Rezapour, Javad
    Safikhani, Hamed
    TRANSACTIONS OF THE CANADIAN SOCIETY FOR MECHANICAL ENGINEERING, 2010, 34 (3-4) : 463 - 474
  • [7] Co-operative co-evolutionary multi-variable system identification using structured genetic algorithms
    Oliveira, PBD
    Jones, AH
    APPLICATION OF MULTI-VARIABLE SYSTEM TECHNIQUES (AMST '98), 1998, : 149 - 158
  • [8] Multireservoir systems optimization using genetic algorithms: Case study
    Sharif, M
    Wardlaw, R
    JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2000, 14 (04) : 255 - 263
  • [9] Thermal Performance and Energy Efficiency of Lightweight Steel Buildings: a Case-Study
    Buzatu, Raluca
    Muntean, Daniel
    Ciutina, Adrian
    Ungureanu, Viorel
    5TH WORLD MULTIDISCIPLINARY CIVIL ENGINEERING-ARCHITECTURE-URBAN PLANNING SYMPOSIUM (WMCAUS), 2020, 960
  • [10] Nexus of thermal resilience and energy efficiency in buildings: A case study of a nursing home
    Sun, Kaiyu
    Specian, Michael
    Hong, Tianzhen
    BUILDING AND ENVIRONMENT, 2020, 177