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Title: Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application
Authors: Asadi, Ehsan 
Silva, Manuel Gameiro da 
Antunes, Carlos Henggeler 
Dias, Luís 
Glicksman, Leon 
Keywords: Building retrofit; Multi-objective optimization; Genetic algorithm; Artificial neural network; Energy efficiency; Thermal comfort
Issue Date: Oct-2014
Publisher: Elsevier
Citation: ASADI, Ehsan [et. al] - Multi-objective optimization for building retrofit: a model using genetic algorithm and artificial neural network and an application. "Energy and Buildings". ISSN 0378-7788. Vol. 81 (2014) p. 444–456
Serial title, monograph or event: Energy and Buildings
Volume: 81
Abstract: Retrofitting of existing buildings offers significant opportunities for improving occupants’ comfort and well-being, reducing global energy consumption and greenhouse gas emissions. This is being considered as one of the main approaches to achieve sustainability in the built environment at relatively low cost and high uptake rates. Although a wide range of retrofit technologies is readily available, methods to identify the most suitable set of retrofit actions for particular projects are still a major technical and methodological challenge. This paper presents a multi-objective optimization model using genetic algorithm (GA) and artificial neural network (ANN) to quantitatively assess technology choices in a building retrofit project. This model combines the rapidity of evaluation of ANNs with the optimization power of GAs. A school building is used as a case study to demonstrate the practicability of the proposed approach and highlight potential problems that may arise. The study starts with the individual optimization of objective functions focusing on building's characteristics and performance: energy consumption, retrofit cost, and thermal discomfort hours. Then a multi-objective optimization model is developed to study the interaction between these conflicting objectives and assess their trade-offs.
ISSN: 0378-7788
DOI: 10.1016/j.enbuild.2014.06.009
Rights: openAccess
Appears in Collections:FEUC- Artigos em Revistas Internacionais
I&D INESCC - Artigos em Revistas Internacionais
FCTUC Eng.Electrotécnica - Artigos em Revistas Internacionais
FCTUC Eng.Mecânica - Artigos em Revistas Internacionais

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