Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency
Highlights Great potential for retrofit. Significant improvement of objective functions reported. NSGA-II is the most popular GA for retrofit. Pareto-based MOO used in 80% of cases. Yielding optimal retrofit solutions may require GA-mixed techniques or a modified GA. Key challenges linked to qualitative objective function definition in heritage retrofit. Future work should focus on a standard systematic approach for the method in retrofit.
Abstract Most common practices for solving building retrofit problems lack efficiency and overall robustness. Knowledge of novel methods that support decision-making (DM) for retrofitting is critical for sustainability and energy performance improvement. This systematic review for the first time provides a large evidence-base to assess the potential of Multi-objective optimisation (MOO) using Genetic algorithm (GA) for supporting the development of retrofitting strategies and its DM process. From 557 screened studies, 57 were reviewed focusing on outcomes, current trends, and the method's potential, challenges, and limitations. Key findings reveal a strong suitability for solving a wide range of building retrofit MOO problems, based on robust outcomes with significant objectives improvement. However, results also indicate that yielding optimal retrofit solutions may require GA-mixed techniques or modified GA, due to time-consuming and effectiveness issues. Heritage buildings, where qualitative objective function definition is particularly challenging, have been little addressed. Further challenges include: lack of standard systematic approach; complex switch between modelling and optimisation environment; high expertise needed to perform MOO and manage software; and lack of confidence in results. While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques.
A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency
Highlights Great potential for retrofit. Significant improvement of objective functions reported. NSGA-II is the most popular GA for retrofit. Pareto-based MOO used in 80% of cases. Yielding optimal retrofit solutions may require GA-mixed techniques or a modified GA. Key challenges linked to qualitative objective function definition in heritage retrofit. Future work should focus on a standard systematic approach for the method in retrofit.
Abstract Most common practices for solving building retrofit problems lack efficiency and overall robustness. Knowledge of novel methods that support decision-making (DM) for retrofitting is critical for sustainability and energy performance improvement. This systematic review for the first time provides a large evidence-base to assess the potential of Multi-objective optimisation (MOO) using Genetic algorithm (GA) for supporting the development of retrofitting strategies and its DM process. From 557 screened studies, 57 were reviewed focusing on outcomes, current trends, and the method's potential, challenges, and limitations. Key findings reveal a strong suitability for solving a wide range of building retrofit MOO problems, based on robust outcomes with significant objectives improvement. However, results also indicate that yielding optimal retrofit solutions may require GA-mixed techniques or modified GA, due to time-consuming and effectiveness issues. Heritage buildings, where qualitative objective function definition is particularly challenging, have been little addressed. Further challenges include: lack of standard systematic approach; complex switch between modelling and optimisation environment; high expertise needed to perform MOO and manage software; and lack of confidence in results. While GA-based MOO's robust evaluation for supporting building retrofit and its DM process needs further research, promising potential is shown overall, when complemented with auxiliary techniques.
A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency
Costa-Carrapiço, Inês (Autor:in) / Raslan, Rokia (Autor:in) / González, Javier Neila (Autor:in)
Energy and Buildings ; 210
08.12.2019
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Systematic review , Multi-objective , Optimization , Genetic algorithms , Retrofit , AB , archetype building , AHP , analytic hierarchy method , AM , aggregating methods , ANN , artificial neural network , BEOT , building energy optimisation tool , GA , genetic algorithm , BPO , building performance optimisation , BPS , building performance simulation , DM , decision-making , ERM , energy retrofit measures , HVAC , heating, ventilation and air conditioning , IEQ , indoor environment quality , IR , interested reader , Isum , summer comfort index , LHS , latin hypercube sampling , MOEA , multi-objective evolutionary algorithms , MOGA , multi-objective genetic algorithm , MOO , multi-objective optimisation , NSGA , non-dominated sorting genetic algorithm , NSGA-II , elitist non-dominated sorting genetic algorithm , PMV , predicted man vote index , PPD , predicted percentage of dissatisfied , PS , primary studies , PV , photovoltaic , RB , real buildings , RSA , response surface approximation model , SA , sensitivity analysis , SBM , simplified building model , SPEA , strength pareto evolutionary algorithm , SR , systematic review , SSS , sobol sequence sampling , VEGA , vector evaluated genetic algorithm , WSM , weighted sum method , ZOGP , zero-one goal programing
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