MULTI-OBJECTIVE OPTIMIZATION APPROACH FOR IMPROVING PERFORMANCE OF BUILDING

被引:1
|
作者
Kamenders, A. [1 ]
Blumberga, A. [1 ]
机构
[1] Riga Tech Univ, Inst Energy Syst & Environm, Kronvalda Blvd 1, LV-1010 Riga, Latvia
关键词
energy and building; energy efficiency; optimization;
D O I
10.2478/v10145-009-0009-4
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Energy efficiency measures are different from energy efficiency and cost effectiveness perspective. For decision maker it is hard to make right decision about different energy efficiency measure combinations in building. It is a complex problem to choose the best energy efficiency measure combination as decision involves many different factors that should be taken in account. Decision on implementation of energy efficiency measure implementation usually depends on investment costs and pay back time. Standards like Latvian Building Code LBN 002-01 can't be used to achieve reasonable expenses in renovation of buildings. Therefore, in order to find the optimal energy-efficiency measures, it is necessary to carry out optimization taking all the variable parameters into account. In the paper target function was presented that gives ability of the multi- objective optimization approach to handle the problem of improving energy efficiency in buildings. Case study is used to demonstrate the feasibility of the approach. 104. series soviet type dwellings was analysed to optimized insulation thickness for external walls. Even if accord with the LBN 002-01 it is enough to use 7 cm thick isolation (lambda-0,039 W/(m(2)K)) layers optimal insulation layer is 12 cm (lambda-0,039 W/(m(2)K)).
引用
收藏
页码:70 / +
页数:5
相关论文
共 50 条
  • [1] Improving evolutionary algorithm performance for integer type multi-objective building system design optimization
    Xu, Weili
    Chong, Adrian
    Karaguzel, Omer T.
    Lam, Khee Poh
    ENERGY AND BUILDINGS, 2016, 127 : 714 - 729
  • [2] Performance of a sequential versus holistic building design approach using multi-objective optimization
    Gagnon, Richard
    Gosselin, Louis
    Decker, Stephanie Armand
    JOURNAL OF BUILDING ENGINEERING, 2019, 26
  • [3] MOBO A NEW SOFTWARE FOR MULTI-OBJECTIVE BUILDING PERFORMANCE OPTIMIZATION
    Palonen, Matti
    Hamdy, Mohamed
    Hasan, Ala
    BUILDING SIMULATION 2013: 13TH INTERNATIONAL CONFERENCE OF THE INTERNATIONAL BUILDING PERFORMANCE SIMULATION ASSOCIATION, 2013, : 2567 - 2574
  • [4] Multi-objective optimization algorithms for building performance assessment - A benchmark
    da Silva, Mario Alves
    Garcia, Rafael de Paula
    Carlo, Joyce Correna
    INTERNATIONAL JOURNAL OF ARCHITECTURAL COMPUTING, 2024,
  • [5] Surrogate Models for Efficient Multi-Objective Optimization of Building Performance
    Araujo, Goncalo Roque
    Gomes, Ricardo
    Gomes, Maria Gloria
    Guedes, Manuel Correia
    Ferrao, Paulo
    ENERGIES, 2023, 16 (10)
  • [6] Improving performance in swarm robots using multi-objective optimization
    Ordaz-Rivas, Erick
    Torres-Trevino, Luis
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2024, 223 : 433 - 457
  • [7] Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort
    Ascione, Fabrizio
    Bianco, Nicola
    De Masi, Rosa Francesca
    Mauro, Gerardo Maria
    Vanoli, Giuseppe Peter
    SUSTAINABILITY, 2015, 7 (08) : 10809 - 10836
  • [8] Evaluating the effectiveness, reliability and efficiency of a multi-objective sequential optimization approach for building performance design
    Talami, Riccardo
    Wright, Jonathan
    Howard, Bianca
    ENERGY AND BUILDINGS, 2025, 329
  • [9] Multi-objective approach to the optimization of shape and envelope in building energy design
    Ciardiello, Adriana
    Rosso, Federica
    Dell'Olmo, Jacopo
    Ciancio, Virgilio
    Ferrero, Marco
    Salata, Ferdinando
    APPLIED ENERGY, 2020, 280
  • [10] A multi-objective optimization framework for building performance under climate change
    Li, Zhixing
    Zhao, Yafei
    Xia, Huijuan
    Xie, Shujing
    JOURNAL OF BUILDING ENGINEERING, 2023, 80