Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application

•Active archive non-dominated sorting genetic algorithm aNSGA-II was tested.•Building retrofit is considered by means of multi-objective optimization.•A case study building was modelled and simulated via EnergyPlus and Python.•Optimal solutions for the retrofit of a Mediterranean building were ident...

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Vydáno v:Energy and buildings Ročník 216; s. 109945
Hlavní autoři: Rosso, Federica, Ciancio, Virgilio, Dell'Olmo, Jacopo, Salata, Ferdinando
Médium: Journal Article
Jazyk:angličtina
Vydáno: Lausanne Elsevier B.V 01.06.2020
Elsevier BV
Témata:
Algorithms
aNSGA-II
Building energy optimization
Building energy retrofit
Building performance optimization
Carbon dioxide
Carbon dioxide emissions
Computer applications
Computing time
Cost control
Dynamic simulation
Energy conservation
Energy costs
Energy demand
Energy performance
EnergyPlus
Genetic algorithm
Genetic algorithms
Investment
Mediterranean climate
Multi-objective optimization
Multiple objective analysis
Pareto optimization
Residential areas
Residential buildings
Residential location
Retrofitting
Sorting algorithms
EnergyPlus
Building energy optimization
Building energy retrofit
Genetic algorithm
Dynamic simulation
Multi-objective optimization
Energy performance
aNSGA-II
Building performance optimization
Mediterranean climate
ISSN:0378-7788, 1872-6178
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Shrnutí:•Active archive non-dominated sorting genetic algorithm aNSGA-II was tested.•Building retrofit is considered by means of multi-objective optimization.•A case study building was modelled and simulated via EnergyPlus and Python.•Optimal solutions for the retrofit of a Mediterranean building were identified. Nowadays, as the role of energy retrofit on the existing building stock is recognized towards energy savings and emissions’ reductions, the actions to be undertaken towards this aim require complex decisions, in terms of the choice among active and passive strategies and among often conflicting objectives of the retrofit. Depending on the actor of the retrofit (e.g., private, public), the main objective could be minimizing the investment, minimizing the energy demand or cost, or minimizing emissions. To facilitate the selection of the optimal retrofit actions, here the application of active archive non-dominated sorting genetic algorithm (aNSGA-II) towards multi-objective optimization is illustrated. The results of the algorithm implementation are analyzed with respect to a residential building located in Rome, Italy. The genes (i.e., the implemented strategies) are described and the optimal solution in the R4 space is discussed, alongside with considerations about the solutions pertaining to the Pareto frontier. The applied method allowed to considerably lower the computational time and identifying the multi-objective optimal solution, which was able to reduce by 49.2% annual energy demand, by 48.8% annual energy costs, by 45.2% CO2 emissions while still maintaining almost 60% lower investment cost with respect to other criterion-optimal solutions.
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ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2020.109945