Genetic algorithm-based approach for optimizing the energy rating on existing buildings

被引:11
作者
Fresco Contreras, Rafael [1 ]
Moyano, Juan [1 ]
Rico, Fernando [1 ]
机构
[1] Univ Seville, ETSIE, Dept Graph Express & Bldg Engn, Ave Reina Mercedes 4A, E-41012 Seville, Spain
关键词
Genetic algorithm; energy rating; building energy retrofit measures; energy saving; MULTIOBJECTIVE OPTIMIZATION; RETROFIT STRATEGIES; DESIGN; MODEL; EFFICIENCY;
D O I
10.1177/0143624416644484
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
The problem of improving the energy behaviour of existing buildings is a current topic of interest in scientific research. In recent years, Public Administrations have made an effort to introduce norms that help to reorient the tendency toward increasing energy consumption by buildings. To do so, manufacturers have developed numerous energy efficiency measures that have become widely extended. The main problem when selecting one or various measures is to identify the ones that will provide the best trade-off between services and implementation costs. This paper presents a study focused on implementing techniques for calculating the heating and cooling energy demand, along with genetic algorithm, to optimize the process of adjusting the building's energy efficiency rating to a determined rating for existing building. The proposed optimization approach is applied to a real case to demonstrate its validity in a real-world situation. Practical application: This paper presents an innovative method for the building energy retrofit process. By applying a simple genetic algorithm, the aim is to optimize the cost of intervening in an existing building by fixing the energy rating obtained at a given value. The practical potential of the method presented here is quite extensive, with its greatest exponent being its use by technicians who are unfamiliar with optimization processes. The application of this calculation methodology would simplify the study of projects in the phase of selecting energy-saving measures, given that there are currently many of them, with their independent characteristics, which makes the selection process a slow and ineffective task. In addition, the method's intuitive interface and the fact that it is programmed in MS Excel make it an innovative method with great applicability in the field of building process optimization.
引用
收藏
页码:664 / 681
页数:18
相关论文
共 33 条
  • [1] [Anonymous], 152172012 EN
  • [2] [Anonymous], 1999, Intelligence through simulated evolution: Forty years of evolutionary programming
  • [3] [Anonymous], 90012008 ISO
  • [4] A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB
    Asadi, Ehsan
    da Silva, Manuel Gameiro
    Antunes, Carlos Henggeler
    Dias, Luis
    [J]. BUILDING AND ENVIRONMENT, 2012, 56 : 370 - 378
  • [5] Multi-objective optimization for building retrofit strategies: A model and an application
    Asadi, Ehsan
    da Silva, Manuel Gameiro
    Antunes, Carlos Henggeler
    Dias, Luis
    [J]. ENERGY AND BUILDINGS, 2012, 44 : 81 - 87
  • [6] Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design
    Attia, Shady
    Hamdy, Mohamed
    O'Brien, William
    Carlucci, Salvatore
    [J]. ENERGY AND BUILDINGS, 2013, 60 : 110 - 124
  • [7] Low-energy design: combining computer-based optimisation and human judgement
    Coley, DA
    Schukat, S
    [J]. BUILDING AND ENVIRONMENT, 2002, 37 (12) : 1241 - 1247
  • [8] Towards a multi-objective optimization approach for improving energy efficiency in buildings
    Diakaki, Christina
    Grigoroudis, Evangelos
    Kolokotsa, Dionyssia
    [J]. ENERGY AND BUILDINGS, 2008, 40 (09) : 1747 - 1754
  • [9] A multi-objective decision model for the improvement of energy efficiency in buildings
    Diakaki, Christina
    Grigoroudis, Evangelos
    Kabelis, Nikos
    Kolokotsa, Dionyssia
    Kalaitzakis, Kostas
    Stavrakakis, George
    [J]. ENERGY, 2010, 35 (12) : 5483 - 5496
  • [10] Ant system: Optimization by a colony of cooperating agents
    Dorigo, M
    Maniezzo, V
    Colorni, A
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01): : 29 - 41