Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study

Schwartz, Yair; Raslan, Rokia; Mumovic, Dejan

doi:10.1016/j.energy.2015.11.056

Cited by 110 publications

(75 citation statements)

References 37 publications

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“…Although GAs do not guarantee finding the global optimum, they have become advanced optimization tool for a wide range of problems. GAs were further applied in a number of the papers quoted in the introduction of this paper [2,5,14,15,[29][30][31][32][33]35,36,38,[40][41][42].…”

Section: Optimization Algorithmmentioning

confidence: 99%

“…According to the literature, in developed and developing countries it constitutes about 40% of the total energy consumption [1][2][3][4][5][6][7][8]. Residential buildings are responsible for a major part of the energy consumption of the building sector [9].…”

Section: Introductionmentioning

confidence: 99%

“…Some researches apply multi-objective [2,5,6,25,26,[36][37][38][39][40] or multi-criterion [41,42] optimization models. In the study conducted by Ascione et al [39], a procedure that combines EnergyPlus simulation and a genetic algorithm was implemented to determine the best solutions for the HVAC system control in a residential building.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Ferdyn-Grygierek

Grygierek

2017

Energies

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Abstract:The building sector is one of the largest energy consumers in the world, comprising about 40% of the total energy consumption in numerous countries. Early design decisions have a significant impact on the energy performance of buildings. The paper presents the multi-variable optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs (LCC) is analyzed. Optimal selection of the design parameters is carried out using genetic algorithms by coupling the building performance simulation program EnergyPlus with optimization environment. The simulations were conducted for seven optimization cases. The analysis is performed for two variants of a building with heating and cooling systems and with a heating system only. Depending on the analyzed case, the life cycle costs decreased from 7% to 34% LCC value of the reference building. In the case of temperate climate, the building optimization (in terms of heat demand only) substantially reduces the heating costs, yet the summer thermal comfort conditions deteriorate significantly.

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Section: Optimization Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Ferdyn-Grygierek

Grygierek

2017

Energies

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“…The building sector plays an important role in this regard. About 40% of all primary energy is used in buildings all over the world [1][2][3][4][5]. The largest contributors to high energy consumption in buildings are heating, ventilation and air conditioning (HVAC) systems [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

“…Simulation tools can predict the effects of design variables on building energy consumption [3]. The studies have been performed for residential buildings and for one or different climates using TRNSYS (University of Wisconsin, Madison, WI, USA) [21,22], EnergyPlus (U.S. Department of Energy's, Washington, DC, USA) [2,23,24], DOE-2 (Lawrence Berkeley National Laboratory, Berkeley, CA, USA) [17,18], eQUEST (Energy-Models.com, San Francisco, CA, USA) [25] simulation programs.…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

Grygierek

Ferdyn-Grygierek

2018

Energies

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Abstract:A properly designed house should provide occupants with the high level of thermal comfort at low energy demand. On many occasions investors choose to add additional insulation to the buildings to reduce heat demand. This may lead to overheating of the building without a cooling system in summer periods (these prevail in Poland). Additionally, it affects the deterioration of thermal comfort, which can only be improved by increasing ventilation. The paper presents the multi-objective optimization of the selected design parameters in a single-family building in temperate climate conditions. The influence of four types of windows, their size, building orientation, insulation of external wall, roof and ground floor and infiltration on the life cycle costs and thermal comfort is analyzed for the building without cooling. Infiltration changes during the simulation and is controlled by a special controller. Its task is to imitate the behavior of occupants in changing the supply airflow. Optimal selection of the design parameters is carried out using Non-dominated Sorting Genetic Algorithm II (NSGA-II) by coupling the building performance simulation program EnergyPlus with optimization environment. For the single-family house, optimal values of design variables for three different criteria are presented.

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Bibliography

2017

Eco‐generative Design for Early Stages of Architecture

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Implementing multi objective genetic algorithm for life cycle carbon footprint and life cycle cost minimisation: A building refurbishment case study

Cited by 110 publications

References 37 publications

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Multi-Variable Optimization of Building Thermal Design Using Genetic Algorithms

Multi-Objective Optimization of the Envelope of Building with Natural Ventilation

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