State of the Art on Retrofit Strategies Selection Using Multi-objective Optimization and Genetic Algorithms

Asadi, Ehsan; Silva, Manuel Gameiro da; Antunes, Carlos Henggeler; Dias, Luís C.

doi:10.1007/978-1-4471-5523-2_11

Cited by 9 publications

(7 citation statements)

References 46 publications

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“…Moreover, multi-criteria analysis-based methodologies do not provide the designer with information about how sensitive each criterion is to changes of the other criteria [242].…”

Section: What Are the Different Methodologies For The Assessment Of Bmentioning

confidence: 99%

A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment

Soares

Bastos

Pereira

et al. 2017

Renewable and Sustainable Energy Reviews

198

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Nowadays, debates addressing climate change, fossil fuels depletion and energy security highlight the need for a more sustainable built environment in order to reduce energy consumption and emission trends in the buildings sector. Meeting these targets is a challenge that calls for innovative research to improve the use of renewable energy sources, new technologies, and holistic tools and methodologies. Such research should integrate the dynamics and main drivers of energy supply and demand in buildings to support new policies, plans and actions towards lowering the built environment burdens. This paper brings together ten research topics concerning the energy and environmental performance of buildings, which can support a shift towards a more sustainable built environment. Background information and state of the art literature on the covered research topics is briefly summarized, gaps are identified and guidelines for future research are provided. The selected topics cover different stages along the lifetime of buildings (from design and operation, to retrofitting and endof-life), different scale approaches (from building elements/components, to the building, district and urban scales), and different methods to assess the energy and environmental performance of buildings (life-cycle assessment, generative design methods and retrofitting tools). Other topics are discussed such as: nearly zero-energy buildings, the control of domestic energy resources in smart grid scenarios, the need to include end-users' behaviors in the dynamics of energy demand, the advantages of improving thermal storage by using phase change materials, the importance of reducing heating and cooling energy demand (maintaining indoor thermal comfort), and the optimization of heating and cooling fluids, and their system control.

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“…Moreover, multi-criteria analysis-based methodologies do not provide the designer with information about how sensitive each criterion is to changes of the other criteria [242].…”

Section: What Are the Different Methodologies For The Assessment Of Bmentioning

confidence: 99%

A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment

Soares

Bastos

Pereira

et al. 2017

Renewable and Sustainable Energy Reviews

198

View full text Add to dashboard Cite

show abstract

“…On the opposite case, when a large number of solutions are defined the required evaluation and selection process may become extremely difficult to handle. Moreover, MCA-based methodologies do not provide the designer with information about how sensitive each criterion is to changes of the other criteria [18].…”

Section: Page 6 Of 46mentioning

confidence: 99%

Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application

Asadi

Silva

Antunes

et al. 2014

Energy and Buildings

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407

170

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Page 2 of 46 A c c e p t e d M a n u s c r i p t 2 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.

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“…Kaklauskas et al [66] proposed a multivariate design and MCA approach for deep energy retrofit, defining the importance and advantages of building retrofit options and choosing the topmost preferred alternative. Another drawback MCA based methods is that the information about the sensitivity of each criterion to alteration of others is not provided [67]. Multi-criteria optimisation of CO 2 emission and retrofit cost by considering a few variables including insulation values, air tightness, lighting controls, system efficiencies and PV provision has been reported [13].…”

Section: Decision Making For Retrofitmentioning

confidence: 99%

Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making

et al. 2020

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Non-domestic buildings contribute 20% of the UK's annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7% is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retrofitting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retrofit packages can be very challenging. The need for effective decision-making has led to the wide adoption of artificial intelligence in the retrofit strategy design process. However, the vast retrofit solution space and high time-complexity of energy simulations inhibit artificial intelligence's application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retrofit planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the effectiveness of the feature set in covering retrofit technologies. The machine learning model which is optimised using

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State of the Art on Retrofit Strategies Selection Using Multi-objective Optimization and Genetic Algorithms

Cited by 9 publications

References 46 publications

A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment

A review on current advances in the energy and environmental performance of buildings towards a more sustainable built environment

Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application

Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making

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