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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Abstract	•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.
AbstractList	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. •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.
ArticleNumber	109945
Author	Rosso, Federica Dell'Olmo, Jacopo Ciancio, Virgilio Salata, Ferdinando
Author_xml	– sequence: 1 givenname: Federica surname: Rosso fullname: Rosso, Federica email: federica.rosso@uniroma1.it organization: Sapienza University of Rome, Dept. of Civil, Construction and Environmental Engineering (DICEA), Via Eudossiana 18, 00184 Roma, Italy – sequence: 2 givenname: Virgilio surname: Ciancio fullname: Ciancio, Virgilio organization: Sapienza University of Rome, Dept. of Astronautics, Electronic and Energy Engineering (DIAEE), Via Eudossiana 18, 00184 Roma, Italy – sequence: 3 givenname: Jacopo surname: Dell'Olmo fullname: Dell'Olmo, Jacopo organization: Sapienza University of Rome, Dept. of Astronautics, Electronic and Energy Engineering (DIAEE), Via Eudossiana 18, 00184 Roma, Italy – sequence: 4 givenname: Ferdinando surname: Salata fullname: Salata, Ferdinando organization: Sapienza University of Rome, Dept. of Astronautics, Electronic and Energy Engineering (DIAEE), Via Eudossiana 18, 00184 Roma, Italy
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Keywords	EnergyPlus Building energy optimization Building energy retrofit Genetic algorithm Dynamic simulation Multi-objective optimization Energy performance aNSGA-II Building performance optimization Mediterranean climate
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Snippet	•Active archive non-dominated sorting genetic algorithm aNSGA-II was tested.•Building retrofit is considered by means of multi-objective optimization.•A case... Nowadays, as the role of energy retrofit on the existing building stock is recognized towards energy savings and emissions' reductions, the actions to be...
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SubjectTerms	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
Title	Multi-objective optimization of building retrofit in the Mediterranean climate by means of genetic algorithm application
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