No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
Simulation-Based Optimization of Building Renovation Considering Energy Consumption and Life-Cycle Assessment
Abstract
Buildings consume a tremendous amount of the total use of secondary energy resulting in a considerable impact on the environment. Therefore, it is necessary to reduce their energy consumption by improving the design of new buildings or renovating existing buildings. However, renovating building envelopes and energy systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of the research about optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their environmental impact using life cycle assessment (LCA). This study aims to develop a methodology to optimize the energy performance of existing buildings by selecting the optimal renovation strategies considering LCA. Different scenarios can be combined in a building renovation strategy to improve energy efficiency. Each scenario considers several factors including improvement of the building envelopes, heating, ventilation, and air conditioning (HVAC) systems, and local energy generation systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to consider the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA) coupled with an energy simulation tool is used for simultaneously minimizing the energy consumption and the LCA of the building. The simulation tool is used to calculate the energy consumption for each potential solution representing one renovation scenario. The data of the building characteristics are extracted from the building information model (BIM). The feasibility of the proposed method is demonstrated using a case study focusing on the buildings of Concordia University.
... In addition to the operational safety aspect, a particular interest is attributed to the technological aspect, focusing mainly on environmental and economic criteria. It targets the minimization of costs, energy consumption (Seow et al., 2016;Sharif & Hammad, 2017), mass and volume of the products, and recycling possibilities (Paraskevas et al., 2015;Li et al., 2016;Unterreiner et al., 2016;Latunussa et al., 2016) in different fields and sectors such as nuclear energy, telecommunications, animal and plant transformations as well as their processes, construction materials, etc. This research axis has led to the creation of inventions that have revolutionized product quality and have contributed to economic development and human well-being. ...
From a sustainable product design perspective, we propose a new multi-criteria decision support approach for the choice of an optimal scenario that aims to minimize environmental, social, and economic impacts. The model combines the system approach and the product approach from a life cycle perspective. It is structured around three significant levels, namely; the strategic, tactical and operational levels applied in the design of new products or services. Our contribution is distinguished by treating two issues. The first concerns the proposal of a mechanism that allows the generation of sustainable design scenarios that are consistent with organizations’ context.This latter is characterized by taking into account internal and external issues and stakeholders requirements. These scenarios are not limited to traditional technological or component choice options. In fact, they are considered value chain-oriented sustainable design strategies. To this end, we use strategic analysis tools such as SWOT, PESTEL, and 7S techniques to identify a multitude of criteria. These criteria form tactics to determine design alternatives by life cycle phase. Design alternatives are then combined to generate design scenarios that are not generic, but meaningful in the context of organizations. The second issue deals with the complexity of life cycle analysis methods and the uncertainty of data and experts’ judgments in order to select an optimal scenario satisfying numerous and often dependent criteria. To this end, we propose to implement a decision support system based on the modelling of environmental, social, and economic assessment for each scenario by life cycle phase. Hence, we calculate the impact indicators related to each assessment. The decision support system is based on control and influence criteria set by organizations as well as the Choquet integral for reducing the number of scenarios. The ANP (Analytic Network Process) method is then deployed to select the optimal design scenario. The validation of the model is tested on a real case study for a company designing, manufacturing, and distributing batteries for motorcycles. The application of the model has effectively generated significant strategic scenarios for the company. The adopted tactical variables are summarized in technology options (AGM, Gel), logistics options (Land transport/Sea transport), manufacturing site options (Tunisia/Tanzania) and distribution options (Local/Exports) with logistics sub-options.On the basis of simulations and impact calculations, we have established environmental, social and economic assessments of each scenario by highlighting the influence of options by scenario nd by phase of the life cycle. Among the most impacting scenarios, we have demonstrated that the choice of AGM technology, manufacturing in Tanzania and maritime logistics generate the most environmental impacts (affecting ecosystem quality and degrading human health) ,the most important social aspects (labor rights, community and governance) and significant costs. The most advantageous scenarios are those using Gel technology, manufacturing at theTunisian site and land transport. The resulting aspects have less impacts. However, the fourteen simulations showed that, although some scenarios are advantageous, they have different impacts per life cycle phase. Thus, the implementation of the fuzzy ANP and the Choquet integral has resolved interactions and dependencies between attributes and between phases of the product’s life cycle. The implementation of this method led to the choice of the optimal scenario while addressing uncertainties of experts’ judgments. The results obtained from this case study confirmed the relevance of the model to the company’s expectations and demonstrated its applicability and ability to minimize environmental, social and economic impacts since early critical design phase.
... Also, there is a strong correlation between optimizing energy performance and the LCC, as choosing different materials and components for renovation has a significant impact on the LCC. On the other hand, when it comes to improving environmental sustainability, finding a correlation between optimizing energy performance and LCC is a challenge [7]. Finding a balance between these important concepts is crucial to improving a building's energy performance. ...
Buildings are responsible for more than 30% of the total energy consumption and an equally large amount of related greenhouse gas emissions. Improving the energy performance of buildings is a critical element of building energy conservation. Furthermore, renovating existing buildings’ envelopes and systems offers significant opportunities for reducing Life Cycle Cost (LCC) and minimizing negative environmental impacts. This approach can be considered as one of the key strategies for achieving sustainable development goals at a relatively low cost, especially when compared with the demolition and reconstruction of new buildings. One of the main methodological and technical issues of this approach is selecting a desirable renovation strategy among a wide range of available options. The main idea and motivation behind this study relies on trying to bridge the gap between Simulation-Based Multi-Objective Optimization (SBMO) and Artificial Neural Network (ANN). For a whole building simulation and optimization, current SBMOs often need thousands of simulation evaluations. Therefore, the optimization becomes unfeasible because of the computation time and complexity of the dependent parameters. To this end, one feasible technique to solve this problem is to implement surrogate models to computationally imitate expensive real building simulation models. The objective of the research focuses on developing a robust ANN to explore vast and complex data generated from the SBMO model. More specifically, this research aims to propose an accurate ANN to predict energy consumption using data from the SBMO model. The proposed model will potentially offer new venues to predict Total Energy Consumption (TEC), LCC, and Life Cycle Assessment (LCA) for different renovation scenarios, and select the optimum scenario. To illustrate the applicability of the model, a case study was developed and the accuracy of the proposed model was evaluated. Results show that models constructed using ANNs are considerably less time-consuming than the conventional Building Energy Model (BEM) while achieving acceptable accuracy.
... Furthermore, there is a strong correlation between optimizing energy performance and the LCC as choosing different materials and components for renovation has a significant impact on LCC. On the other hand, when it comes to improving environmental sustainability, finding a correlation between optimizing energy performance and sustainability is a challenge [61]. As a result, finding a balance between these important concepts is crucial to improving a building's energy performance. ...
Buildings are responsible for a significant amount of energy consumption resulting in a considerable negative environmental impact. Therefore, it is essential to decrease their energy consumption by improving the design of new buildings or renovating existing buildings. Heat losses or gains through building envelopes affect the energy use and the indoor condition. Heating, Ventilation, and Air Conditioning (HVAC) and lighting systems are responsible for 33% and 25% of the total energy consumption in office buildings, respectively. However, renovating building envelopes and energy consuming systems to lessen energy losses is usually expensive and has a long payback period. Despite the significant contribution of research on optimizing energy consumption, there is limited research focusing on the renovation of existing buildings to minimize their Life Cycle Cost (LCC) and environmental impact using Life Cycle Assessment (LCA). This paper aims to find the optimal scenario for the renovation of institutional buildings considering energy consumption and LCA while providing an efficient method to deal with the limited renovation budget. Different scenarios can be compared in a building renovation strategy to improve energy efficiency. Each scenario considers several methods including the improvement of the building envelopes, HVAC and lighting systems. However, some of these scenarios could be inconsistent and should be eliminated. Another consideration in this research is the appropriate coupling of renovation scenarios. For example, the HVAC system must be redesigned when renovating the building envelope to account for the reduced energy demand and to avoid undesirable side effects. A genetic algorithm (GA), coupled with an energy simulation tool, is used for simultaneously minimizing the energy consumption, LCC, and environmental impact of a building. A case study is developed to demonstrate the feasibility of the proposed method.
Buildings consume a huge amount of energy, resulting in a considerable impact on the environment. In Canada, almost 70% of the total energy used by the commercial and institutional sectors was consumed by Heating, Ventilation and Air-Conditioning (HVAC) and lighting systems, which makes them the main targets of energy performance optimization methods. Furthermore, based on a governmental report, 40% of Quebec university buildings are in poor or very poor shape regarding structure and materials, and require immediate renovation. Therefore, it is of utmost importance to reduce energy consumption, and this can be accomplished by improving the design of new buildings or by renovating existing ones. Moreover, Simulation-Based Multi-Objective Optimization (SBMO) models can be used for optimizing and assessing different renovation scenarios considering Total Energy Consumption (TEC) and Life Cycle Cost (LCC). The time-consuming nature of SBMO has triggered the development of simplified and surrogate models within the design process. This study proposes a generative deep learning building energy model using Variational Autoencoders (VAEs), which could potentially overcome the current limitations. The proposed VAEs extract deep features from a whole building renovation dataset and generate renovation scenarios considering TEC and LCC of the existing institutional buildings. The proposed model also has the generalization ability due to its potential to reuse the dataset from a specific case in similar situations. The performance of the developed model has been demonstrated using a simulated renovation dataset to prove its potential. The results show that using generative VAEs is acceptable considering computational time and accuracy.
The debate on the relevance of the global sustainability (including energy, environmental, social, economic, and political aspects) of building stock is becoming increasingly important in Europe. In this context, special attention is placed on the refurbishment of existing buildings, in particular those characterized by significant volumes and poor energy performance. Directive 2012/27/EU introduced stringent constraints (often disregarded) for public administrations to ensure a minimum yearly renovation quota of its building stock. This study describes how Life Cycle Cost analysis (LCC) can be used as a tool to identify the “cost-optimal level” among different design solutions to improve the energy performance of existing buildings. With this aim, a social housing building located in the town of Pisa (Italy) was chosen as the case study, for which two alternative renovation designs were compared using the LCC methodology to identify the optimal solution. The two alternatives were characterized by the same energy performance—one was based on the demolition of the existing building and the construction of a new building (with a wooden frame structure, as proposed by the public company owner of the building), while the other was based on the renovation of the existing building. This study can provide useful information, especially for designers and public authorities, about the relevance of the economic issues related to the renovation of social housing in a Mediterranean climate.
Renovating the existing building stock is well recognized in the construction industry as a very important issue. While the number of new buildings annually provides maximum 1% to the building stock, the other 99% represent buildings which are already built. Studies show that the environmental impact to extend the life of a building is definitely smaller than that of demolition and new construction. Recent figures suggest that the residential sector can provide significant reductions in energy consumption.
As many vital decisions are taken in the early stages of the refurbishment process, planners need tools that will help them create better and more sustainable retrofitting projects based on the improvement of the building energy performance.
This study describes a methodology to support decision making in the retrofitting of the existing building stock of the Leopold Quarter in Brussels to enable the development of an integrated strategy for different cases and specifications. It is intended that the planners know since an early stage, the energy impact of the project according to the selected interventions.
All the proposed retrofitting strategies are compiled in a database that is the basis of the tool named TCS Matrix (TCS stands for: Typology, Component, Solution).
This paper provides an insight of the methodology to create the aforementioned databases as well as a series of scenarios that supposes a first step of the aimed methodology to identify in an early stage the best solutions for this specific part of the building stock to achieve the energy efficiency targets defined by the Energy Performance of Buildings Directive.
Because the student residences of the Vrije Universiteit Brussel built in 1973 are not adapted to current comfort standards, the university decided to construct new accommodation facilities at the border of the campus. However, besides demolition, there was no strategy on how to deal with the existing ones. In the search for a more sustainable strategy, the university's administration assigned the TRANSFORM research team to define various design strategies and to assess the long-term environmental consequences in order to select the best strategy by the use of Life Cycle Environmental Assessment. Current Life Cycle Environmental Assessments generally include maintenance, repair, replacement and operational energy consumption during use, but do not include future refurbishments. However, it is likely that their impact cannot be neglected either. Therefore, this article offers a framework which takes future refurbishments into account, in addition to the standard use impacts: initial and end-of-life impact. We report on the construction assemblies, the results of the assessments conducted and the advice provided. The results confirm that the impact of future refurbishments cannot be neglected. In addition, we observed that there were significant environmental savings when transforming the residences compared to new construction, and long-term benefits of a design enabling the reuse of building elements.
The starting point of the research is the need to refurbish existing residential building stock, in order to reduce its energy demand, which accounts for over one fourth of the energy consumption in the European Union. Refurbishment is a necessary step to reach the ambitious energy and decarbonisation targets for 2020 and 2050 that require an eventual reduction up to 90% in CO2 emissions. In this context, the rate and depth of refurbishment need to grow. The number of building to be renovated every year should increase, while the energy savings in renovated buildings should be over 60% reduction to current energy demand. To achieve that, not only is it necessary to find politics and incentives, but also to enable the building industry to design and construct effective refurbishment strategies. This research focuses on refurbishment of the building envelope, as it is very influential with regard to energy reduction. The early design phases are particularly important, as decisions taken during this stage can determine the success or failure of the design. Even though the design decisions made earlier can have bigger impact with lower cost and effort, most existing tools focus on post-design evaluation. The integration of all aspects during the early design phases is complex, particularly as far as energy efficient design is concerned. At this stage, architects are in search for a design direction to make an informed decision. If the designer is provided with an indication of how efficient refurbishment options are, it is possible to apply them as part of an integrated strategy rather than trying to add measures at later stages, after the strategy has been developed. Therefore, taking into account the need to refurbish residential buildings and the importance of integrated design of façade refurbishment strategies, the thesis aims at answering the following question. How can the energy upgrade potential of residential façade refurbishment strategies be integrated in the early design phase, in order to support decision-making? The objective of the research is to enable the design of refurbishment strategies that acknowledge the potential of energy savings. Having available an assessment of the energy performance results in informed decisions that improve the efficiency of the strategy and the final refurbished building. The answer to the research question is given by the Façade Refurbishment Toolbox (FRT) approach. It consists of three different types of information that can support the decision-making of residential façade refurbishment strategies. Firstly, the building envelope components that need to be addressed in an integrated refurbishment strategy are identified and different retrofitting measures for each one are proposed, composing the façade refurbishment toolbox. Secondly, the measures are quantified in terms of energy upgrade potential, expressed by the simulated energy demand reduction after the application of the measure. Finally, a roadmap to the key decision aspects in the refurbishment strategy development indicates when the toolbox information can be used. The methodology used to develop the FRT approach includes organising and calculating information about component retrofitting measures. The steps of the methodology were developed in the different chapters of the thesis. The first three chapters (Chapters 2-4) comprise the theoretical background that shapes the research question, discussion of the residential building stock, energy performance and refurbishment practice. Chapters 5 and 6 describe the process of the composition of the toolbox. Finally, Chapters 7 and 8 are concerned with its further applicability, regarding the approach validation and usability. This thesis concludes with an approach to enable informed and energy-efficiency conscious decisions in the early stage of the design of refurbishment strategies. To improve the design process, the Façade Refurbishment Toolbox facilitates the development of strategies in different cases and for different specifications, without limiting or dictating designers’ choices. Designing is deciding. Knowledge and information can lead to better understanding of a decision consequent and, therefore, result in better design solutions. Different buildings have different energy saving potential. They also have different specifications, performance requirements and design ambitions. All these parameters result in different refurbishment strategies. The aim of the proposed approach is not replacing the design process and generating a solution, but supporting it by providing information to lead into responsible and knowledgeable decisions. In this way, refurbishment strategies that take into account the building improvement, occupants’ comfort and efficient energy use can be designed, contributing to the greater society’s goals of CO2 emissions reduction and sustainable development.
This paper analyses renovation alternatives to improve energy performance of historic rural houses in three countries (Estonia, Finland, Sweden) in the Baltic Sea region (cold climate). The study was conducted by a combination of field measurements and simulations. Indoor climate, typical houses and structures as well as the current condition and need for renovation were determined by field measurements. Based on field measurements, indoor climate and energy simulation models were validated and used to calculate energy use for different renovation measures. Energy renovation packages were calculated for different scenarios (minimal influence on the appearance of the house, improvement of thermal comfort, improvement of building service systems) for different energy saving levels. The analysis showed that the improvement of building service systems and the energy source holds the largest energy saving potential. The building envelope of old rural houses needs improvement also due to high thermal transmittance and air leakage. The insulation of the external wall has the largest single energy saving potential of the building's envelope. The results show how energy savings depend on energy saving targets, typology of the building, thermal transmittance of original structures, and building service systems.
Energy consumption of buildings accounts for around 20–40% of all energy consumed in advanced countries. Over the last decade, more and more global organizations are investing significant resources to create sustainably built environments, emphasizing sustainable building renovation processes to reduce energy consumption and carbon dioxide emissions. This study develops an integrated decision support system to assess existing office building conditions and to recommend an optimal set of sustainable renovation actions, considering trade-offs between renovation cost, improved building quality, and environmental impacts. A hybrid approach that combines A* graph search algorithm with genetic algorithms (GA) is used to analyze all possible renovation actions and their trade-offs to develop the optimal solution. A two-stage system validation is performed to demonstrate the practical application of the hybrid approach: zero-one goal programming (ZOGP) and genetic algorithms are adopted to validate the effectiveness of the algorithm. A real-world renovation project is introduced to validate differences in energy performance projected for the renovation solution suggested by the system. The results reveal that the proposed hybrid system is more computationally effective than either ZOGP or GA alone. The system's suggested renovation actions would provide substantial energy performance improvements to the real project if implemented.
Multi-objective evolutionary algorithms (MOEAs) that use
non-dominated sorting and sharing have been criticized mainly for: (1)
their O(MN<sup>3</sup>) computational complexity (where M is the number
of objectives and N is the population size); (2) their non-elitism
approach; and (3) the need to specify a sharing parameter. In this
paper, we suggest a non-dominated sorting-based MOEA, called NSGA-II
(Non-dominated Sorting Genetic Algorithm II), which alleviates all of
the above three difficulties. Specifically, a fast non-dominated sorting
approach with O(MN<sup>2</sup>) computational complexity is presented.
Also, a selection operator is presented that creates a mating pool by
combining the parent and offspring populations and selecting the best N
solutions (with respect to fitness and spread). Simulation results on
difficult test problems show that NSGA-II is able, for most problems, to
find a much better spread of solutions and better convergence near the
true Pareto-optimal front compared to the Pareto-archived evolution
strategy and the strength-Pareto evolutionary algorithm - two other
elitist MOEAs that pay special attention to creating a diverse
Pareto-optimal front. Moreover, we modify the definition of dominance in
order to solve constrained multi-objective problems efficiently.
Simulation results of the constrained NSGA-II on a number of test
problems, including a five-objective, seven-constraint nonlinear
problem, are compared with another constrained multi-objective
optimizer, and the much better performance of NSGA-II is observed
Life Cycle Assessment (LCA) and Life Cycle Energy Analysis of Buildings and the Building Sector: A
- L F Cabeza
- L Rincón
- I Vilariño
- G Pérez
- A Castell
Building Science for Building Enclosures (Book)
- J F Straube
- E F P Burnett
Advanced Energy Design Guide for Small to Medium Office Buildings
- Ashrae Design Guide
The Integrated Design Process; History and Analysis International Initiative for a Sustainable Built Environment (iiSBE)
- N Larsson