Become a Reviewer

Author Guidelines

Review Process

Privacy Policy

Submit Paper



Issues

Current

2016, Volume 1, Issue 1

2016, Volume 1, Issue 2

2017, Volume 1, Issue 1

2017, Volume 2, Issue 1

2017, Volume 2, Issue 2

2018, Volume 1, Issue 1

2018, Volume 1, Issue 2


Google Scholar

ResearchGate


BIM-Based multi-objective optimization process for energy and comfort simulation: existing tools analysis and workflow proposal on a case study

C. Zanchetta, C. Cecchini, and C. Bellotto

Department of Civil, Environmental and Architectural Engineering, University of Padova, Italy


ABSTRACT

In the last decades, the themes related to comfort simulation have gained a central role in the building process, requiring increasingly thorough analyses. In this field, multidisciplinary simulation-based optimization can be used to help professionals in investigating design alternatives and reaching the best solutions supported by scientific rigor. In addition, due to the complexity of the variables involved, the integration between comfort analysis and BIM-based software is crucial. The research hereby presented starts from a literature review and examines the development of BIM-based optimization with the exploitation of Visual Programming Languages (VPL) in order to achieve an optimization method based on simulation. After the definition the knowledge basis, a workflow that uses the BIM software Archicad in connection with the VPL editor Grasshopper, the environmental plug-ins Ladybug and Honeybee as simulation tools, and Galapagos and Octopus for the optimization stage is proposed. The framework has been tested on a case study in which thermo-hygrometric and visual comfort have been optimized depending on the size of the glazed surfaces. The study highlights the opportunity arising from the application of BIM-Based multi-objective optimization processes and discusses their limitations at the state of the art.


  Keywords: Building Information Modeling, Visual Programming Language, Performance Based Building Design, Optimisation Algorithms, Multi-Objective Optimisation


1. Introduction

The achievement of indoor comfort conditions should be one of the main goals in a user-oriented building process because it affects significantly the quality of life of the individuals using a building. From both science and experience it is well known that the achievement of a wellness condition depends on a wide range of aspects that involve all the five senses in a combination of objective and subjective impressions. While the studies on the improvement of each single aspect that contributes to the internal comfort are consolidated and individually well included on Performance Based Building Design (PBBD) processes, it appears clear that the choice of the best design configuration should be guided by a numerical model able to consider a plurality of parameters. In order to manage the problem, it is necessary to dispose of tools able to share information and support multidisciplinary analyses. Building Information Modeling (BIM) satisfies these requirements, but there are still several interoperability issues to be solved to build a real integrated workflow. In this field a new challenge that deals with multi-objective optimization linked to energy and comfort simulation and suitable to be contained in an integrated BIM-based building process is emerging.

In this paper, the definition of a workflow able to optimize jointly thermo-hygrometric and visual comfort based on the dimension of glazed external surfaces is presented.


2. Indoor environmental quality

There is a great potential for energy savings on air heating and conditioning when using the ideal Window Area (WWR) in offices, depending on orientation and glazing types.   Energy efficient window design should limit both cooling and heating demands.  The brief addressed by this study was to evaluate internal conditions in an office space that have large glazed area in Constantine city, and to define an efficient window in terms of heating and cooling. This work intends to provide guidance to building designers with regard to the thermal performance of office buildings.


The deep comprehension of the relation between the users and the indoor environment in a living space is a fundamental aspect in the building process. The internal comfort, in fact, influences the life style of people that use a building, affecting their attitude and mood.

The Italian technical standard UNI 8289 on internal comfort defines five classes of requirement to ensure wellness: thermo-hygrometric, acoustic, visual, olfactory and tactile comfort. While some of them appear more familiar than others, the principles of design based on performance recommend that all of them should be considered simultaneously to ensure the achievement of the wellness requirements. This consideration generates the need to activate performance analyses that are based on more than one parameter.


2.1 Thermo-hygrometric comfort

In order to simulate the dynamics of thermo-hygrometric comfort this research will refer to the model theorized by Fanger, who defined experimentally two indices that are still in use to classify the internal comfort of a built space: PMV (Predicted Mean Vote) and PPD (Percentage of Person Dissatisfied).

The PMV index is a mathematical function that express the average vote given by a group of people in a scale from -3 to +3 to indicate cold or hot feeling where 0 represent thermal comfort, while PPD indicates the percentage of people that are dissatisfied with a thermal environment. The scale goes from -2 to +2, corresponding respectively to cold and hot. Fanger's indices have been taken as a reference and are part of the technical standard on thermal comfort UNI EN ISO 7730.

For the aims of this study, in order to perform optimization based on simulation and according to ASHRAE 55, a space is considered comfortable when:





2.2 Visual comfort

Visual or visual comfort is a subjective impression related to quantity, distribution and color of light and is reached when all the objects in a room can be seen clearly and the activities to be carried out in a space can be pursued without any visual effort. Internal lighting can be natural or artificial and usually a combination of the two is used to allow the exploitation of the buildings for the whole day. In a sustainable approach to design, however, it is highly recommended to reduce to the minimum the needs for artificial lighting to ensure both better feelings and energy saving.

This study will use as the reference the daylight factor (DF), defined as the ratio between the illuminance measured in a point lying on a horizontal plane inside a building E_i and the illuminance measured in the same moment in a point outside the structure under an overcast sky E_0, both measured in lux.







Since the value of DF changes in each point of the room, the average daily factor in taken in account.

In this case, according to the indication of the CIBSE Lighting Guide 10 (LG10-1999), the minimum acceptable requirement to consider a space properly enlightened is:





However, without taking in account the problem of glare, a higher value is preferred.


3. State of the art

 

While the purpose of this study is to connect BIM, simulation and optimization processes in an integrated workflow, in this section the state of the art on the topics and the already explored relations between them is presented.


3.1 Optimisation based on simulation

In last decades the use of computer-based simulation to solve complex systems in the field of engineering have increased rapidly and nowadays the AEC (Architecture, Engineering and Construction) industry designers use quite often software able to perform dynamic simulations in order to estimate the energy behavior of buildings. A parametric approach linked to this makes it possible to calculate the minimum of one or more functions that involve dynamic simulations. With the aim to improve building energy performances or internal comfort, every parameter should be changed while the others are maintained steady, in the aim of understanding the influence of each component on the whole system. This process is extremely difficult and laborious and the results are all the same partial, due to the intervention of complex and non-linear iterations. For the purpose of achieving a better solution with less time and effort, the building model can be solved with iterative methods, consisting of plural sequences of progressive approximations that lead to a solution, which is a point that satisfies the optimum conditions.

The use of optimization methods in the studies involving the field of constructions have shown a great increase only in the last decades (Nguyen, Reiter, & Rigo, 2014). In fact, thanks to a rapid technological evolution, they have become very popular in the academic world and have been applied to solve a wide range of problems such as the shape of the building, the design of the building envelope, the management of the HVAC systems and the generation of renewable.


3.1.1 Optimisation based on simulation

Optimization algorithms are usually classified on the basis of six pairs of different non-exclusive aspects (Haupt & Haupt, 2004); and, furthermore, in relation with the number of objective functions to be optimized, there are two different kind of processes:

- Mono objective optimisation;

- Multi-objective optimisation.


Looking at optimization processes related to the AEC industry in literature, about 60% of the cases use the mono objective method (Evins, 2013). However, in the real case of profession, the designers are requested to optimize simultaneously the performances referred to different aspects very far between them and sometime even odds. Most of the difficulties come from the fact that often the functions at stake cannot be minimized (or maximized) simultaneously. Generally speaking, in fact, a solution that determines the optimum of one objective doesn't do the same for the others. For this reason, multi objective optimization (MOO) appears to be more appropriate, at the expense of computational lightness.
























There are different ways to solve a multi-objective problem. The simpler approach consists in the assignment of different weights to each function, so that the objective function will become the weighted sum of the criteria (Wright, Loosemore, & Farmani, 2002) with the clear advantage to reduce the problem to a mono objective one with a single dimension. However, it frequently happens that it is impossible to apply this simplification due to the lack of relation between the functions or when the maximization of one objective function causes the decline of the other.

In another approach, which is the one that will be developed here, the concept of Pareto efficiency is used. Here a series of optimal solutions is examined to identify later the best one. This method leads to identify for every problem the set of all efficient allocations, which define the Pareto frontier (Fig. 1).

In this curve all the optimum points can be found, reflecting the solutions for which it is impossible to improve further all the objectives, with no mention to the weight of criteria. Once the Pareto frontier has been defined, the best solution should be chosen by the designer in the light of different aspects with a decision making multi-criteria process. In most of cases there is a specific optimization algorithm defined with the purpose to solve a certain problem. Over the years, in fact, several optimization algorithms have been developed in order to solve precise problems. The decision on the most suitable algorithm is usually based on a series of considerations linked to the specific case, since the definition of a general law to select the best algorithm is impossible. However, thanks to the study made by Nguyen, Reiter e Rigo it is possible to notice that some algorithms are more used than others. The research considered more than 200 case study about optimization based on the simulation of building performance provided by SciVerse Scopus by Elsevier and identified that the most widely used are stochastic algorithms (Nguyen, Reiter, & Rigo, 2014). The family of algorithms based on the generation of stochastic populations includes genetic, hybrid, and evolutionary algorithms. They are all heuristic, that means that they cannot ensure to identify the best solution in a finite number of iterations, but are suitable to find good solutions in a reasonable amount of time.

This class of algorithms is widely used in the AEC industry, and chosen in this research, for several reasons:

 They allow to solve multi-objective optimisation problems (Deb, Multi-objective genetic algorithms: Problem difficulties and construction of test problems, 1999);

 They are an effective method to manage discontinuities and highly constrained problems;

 They show high success rate while combined with simulation.


 Genetic algorithms

Evolutionary algorithms (EA), are based on the theory of evolution, referring directly to the one published by Charles Darwin on his "The origin of species" (1859). They are a stochastic method of optimisation for resolving complex problems and are part of the wider category of model based on natural metaphors.

Genetic algorithms have been developed from evolutionary algorithms since 1975 by John Holland (Michigan University) and are their simpler subcategory. The functioning of a genetic algorithm can be subdivided into three parts (Tettamanzi, 2005). In the first phase, based on a random selection, an initial population of "n" individuals is chosen from the domain of the function. This set of elements constitutes the first possible solution to the problem, codified as a binary string and called chromosome. When the evolutionary cycle begins, firstly the operator of selection is applied, it simulates the Darwin's law of the survival of the fittest by applying a proportional selection based on the fitness value of each solution. While "n" parents have been chosen, the individuals of the next generation are generated by the application of recombination. In genetic algorithms two operators of reproduction, crossover and mutation, are used in order to change the genes of the solutions and explore new possibilities. Finally, the new generation of solutions replaces the previous one. The process is repeated x times until an acceptable approximation of the optimal solution or the maximum number of iterations is reached.

The use of evolutionary algorithms as a method to solve complex problems has both strengths and weaknesses and shows two principal vulnerabilities. Firstly, the convergence to the result is slower than with other optimisation techniques, at the point where the computation for the solution of some problems could even take days. Furthermore, due to their stochastic nature, this kind of algorithms do not guarantee the exact identification of the optimal solution, but most of the times they detect a good approximation, suitable to solve the problem.  For these reasons the application of evolutionary algorithms is not suitable to every kind of problem, but they are particularly useful when the objective function is too complex to be rapidly maximized with non-stochastic methods.
























3.2 BIM tools for performance simulation

The achievement of the objectives related to internal comfort, in particular with a view to integrated performance-based design, requires the development of multidisciplinary models able to represent the built organism in all its aspects. In this field the most effective approach is the adoption of BIM technology to manage the complexity of the building process by simulating its real behavior in terms of constructive, formal and functional aspects.

A BIM is a building model based on data other than geometry that contains multidisciplinary information and includes all the links and the hierarchical relations between the elements. It is a shared digital representation of a construction with its physical and functional characteristics, based on open standard for interoperability. In this sense an informative model can be used as a central database able to communicate with external codes in order proceed performance analyses. While an informative model is intrinsically multidisciplinary, the real challenge is to link it to the processes of simulation and optimization, making the set of information stored on it available for a series of external platforms and readable by the algorithms involved in the calculations. A virtuous building process, in fact, should be characterized by an effective flow of information through different software tools to ensure the functionality of a cycle made of design, analysis and validation of each choice. In the field of energy simulation there are still lots of technical barriers that prevent the effective exchange of information between software and, thus, the achievement of an integrated multidisciplinary process between modelling and simulation (Zanchetta, Paparella, Borin, Cecchini, & Volpin, 2014). In the usual workflow the incompatibility between the tools leads to the need to define several times the same information in different platforms, making the design process more burdensome and highly error-prone. In order to overcome this problem, the role of interoperability becomes central. From a literary review, in fact, it appears clear that parametric optimization processes used to improve building performances in the early stage of design are very far from being a standard in the AEC industry. This is mainly due to the lack of adequate instruments (Lin & Gerber, 2014) and to the complexity of the method. Consequently, at present time this system is applied only in the academic field where some researches on this topic have been developed.

The application of algorithms in the field of energy building performance simulation began in the '70s, but only in more recent times the number of studies on the subject has really increased as demonstrated in "Improving the energy performance of residential buildings: A literature review". However, only few of them focus on the use of BIM to support the optimization process. For the purpose of this paper it is important to cite:


- The multi-objective optimisation process called "ThermalOpt" developed by Welle (Welle, Haymaker, & Rogers, 2011) that consists in a workflow able to automate dynamic thermal simulations by managing the information though a BIM database;


- The EEPFD (Evolutionary Energy Performance Feedback for Design) process (Lin & Gerber, 2014) that allows the exploration of complex geometric shapes to optimise the energy behaviour of buildings in the first stages of a BIM-based design process;


- The "BPOpt" method (Asl, Zarrinmehr, Bergin, & Yan, 2015), that is an integrated system involving Building Information Modeling (BIM) and a Visual Programming Language (VPL) interface (Asl, Zarrinmehr, Bergin, & Yan, 2015). It allows to link the information stored in the model with simulation tools and to develop the optimisation process in a close relation with it.


All those studies introduce a methodological framework useful both to mitigate the problems derived from the lack of interoperability between software and to link optimization processes to Building Information Modeling.


4. Methodology and tools

The set of tools implemented in the presented workflow are in a close relation that identifies them from the general to the specific. Starting from the authoring software, used in order to achieve the informative model of the building, a process based on Visual Programming Language (VPL) is activated and finally, within it, a series of plug-ins able to support energy and comfort simulation and optimization processes are used.

4.1 Authoring software

One of the aims of this study is to connect BIM with algorithmic modelling in order to achieve a process of energy optimization while disposing of an informative model. This can be possible only by linking together a BIM software with a visual programming engine equipped with both environmental and optimization plug-ins. For this purpose, it was decided to evaluate and study the connection between the BIM software Archicad and the VPL editor Grasshopper. In this process, however, there is a clear limit to be overcome: Grasshopper, in fact, is an application linked to the 3D CAD software Rhinoceros, a NURBS modeler which is not conceived for informative modelling. Usually the only information that it can exchange with a BIM software are geometrical data, which are a good basis for reasoning on the building shapes, but are insufficient to build an informative model.

Actually, excluding the methods that involve IFC (Industry Foundation Classes), there exist three options to connect Archicad to Rhinoceros and Grasshopper:

- Rhinoceros Import/Export add-on: exchanges model referring to the native Rhinoceros file format;

- Grasshopper - Archicad Live Connection: allows to generate and manage BIM elements within Archicad working with Grasshopper;

- Rhinoceros-GDL Converter: permits to create native Archicad objects inside Rhinoceros.

Among the three, Grasshopper-Archicad Live Connection has been chosen because it enables a smart workflow that allows to explore design alternatives. With its implementation, the elementary geometry created in Rhinoceros or derived from Grasshopper can be directly translated into BIM elements. This workflow does not necessitate to import or export files to exchange data, but, with a dynamic link between the two platforms, there is an immediate graphic feedback in both the softwares. The direct communication allows to exploit simultaneously the advantages of the algorithmic design and of the informative modelling.

4.2 VPL environment

Visual programming language (VPL) is a simplified coding approach that helps user to design algorithms by manipulating graphic elements rather than writing text strings. Recently some CAD and BIM software provide internal Visual Programming interfaces, helping the professionals to define advanced design processes without the need to use scripting. Grasshopper (GH) is the VPL editor developed in 2007 by David Rutten and Robert McNeel & Associates and integrated in the NURBS modelling software Rhinoceros 3D. GH is open source, has a spontaneous attitude to interact with several external simulation tools and a wide number of add-ons including applications for mono and multi-objective optimization.

From a literature review, three studies very close to our purpose which link Rhinoceros with simulation tools thanks to Grasshoper, have been selected:

- Lagios et al. created a workflow based on Grasshoper able to export geometry, material properties and sensitive grids to Radiance and DAYSIM, in order to calculate a series of index related to the natural lighting of spaces (Lagios, Niemasz, & Reinhart, 2010);

- Jakubiec and Reinhart described a design process to integrate the daylight analysis made with Radiance and DASIM with the thermal simulation processed with EnergyPlus thanks to their own plug-in called DIVA (Jakubiec & Reinhart, 2011);

- Roudsari and Pak developed Ladybug and Honeybee, two free and open source plug-ins able to connect from the Grasshoper environment EnergyPlus, Radiance, DAYSIM and Open studio with the aim to process lighting and thermal simulations. Furthermore, these plug-ins allows to import and manage EnergyPlus climatic data (.epw) and to represent the results of the analyses on building performance  (Roudsari, Pak, & Smith, 2013).

Despite their significance, it is observed that even though all these examples define a parametric workflow that involves performance simulation, they do not really exploit the access to all the information stored into an informative model, starting from a geometric more than an informative database.

4.3 Simulation plug-ins

To face the need of parametric design tools integrated with energy and comfort simulation engines, recently several studies applied to energy modelling have been developed, and most of them deals with Grasshopper (Jakubiec & Reinhart, 2011) (Lagios, Niemasz, & Reinhart, 2010) (Roudsari, Pak, & Smith, 2013). The common objective of these researches is to link a series of instruments to access performance based design processes in the early stages of the process, allowing to explore different design alternatives and giving in advance the results of performance analyses.

After a comparative analysis carried among several environmental plug-ins as showed in Table 1, it was decided to use Ladybug and Honeybee, a powerful couple of tools that allows the integration of EnergyPlus, Radiance, DAYSIM, and e Openstudio in Grasshopper (Roudsari, Pak, & Smith, 2013).



















4.4 Optimisation plug-ins

Galapagos and Octopus are two genetic solver that work with Grasshopper. Their application takes place especially in the first stages of the design process with the aim to define the parameters and the constrains of the project, and dispose of a preliminary representation of the related problems, in order to direct the designer toward the development of design alternatives that can be more suitable for the project requirements.

Galapagos, created by David Rutten, is an evolutionary solver used to develop processes of mono-objective optimisations within Grasshopper. This algorithm can minimize an only objective function, the so-called fitness function, with regard to different project parameters called "genomes".

Octopus, as opposed to Galapagos, is a plug-in for multi-objective optimisation able to work with evolutionary algorithms applied to different functions. This solver is part of a series of tools developed by Robert Vierlinger within the University of Applied Arts of Wien in collaboration with the engineering firm Bollinger Grohmann Engineers. Octopus can identify the set of solutions laying on the Pareto's frontier and allows the designer to select the most suitable configuration according to the relative weight of the parameters and to the project requirements.


5. Case Study

The case study has been developed with an Italian engineering company, specialised in the field of engineering and project management for the building sector with special focus on sustainability and energy issues.

The object of analysis is a building designed to host offices and exhibition spaces, built on a single level with a rectangular footprint and characterized by glazed elevations.

Thanks to the combined use of the tools described in the previous paragraphs, an integrated framework for multi-objective optimisation process for energy and comfort simulation has been developed.

With reference to the case study the workflow has been divided into seven stages and, as it can be noticed, it starts and finishes inside Archicad in order to ensure the integration of the simulation and optimisation phases within a BIM-based process.



















Modelling of the building with the BIM authoring software Archicad

The first phase of the workflow consists in the informative modelling of the building. The BIM is an informative database, able to include all the multidisciplinary information useful for the design and the management of the construction, and it is the ideal starting point to activate different kinds of analyses on the building.

Definition of a simplified model suitable for the performance analyses

While the informative model of a building contains a plurality of multidisciplinary information, the one requested to implement energy and comfort analyses is a simplified version of it. The energy model should include geometrical and physical data related to building element, materials and spaces (Zanchetta, Paparella, Borin, Cecchini, & Volpin, 2014). In order to obtain it, a Model View Definition (MVD) is applied to the central model. This consist in a filter able to select the information that are relevant for a specific scope. For the purpose of this study a model able to satisfy simultaneously the requirement of a thermo-hygrometric and of a lighting analyses has been identified. Materials and constructions has been assigned according to the ASHRAE Handbook of Fundamentals (2005), as it can be seen in Table 2.























Information exchange from Archicad to Rhinoceros

The flow of information is achieved using Rhinoceros Export Add-On, which allows to save Archicad file in the native format of Rhinoceros (.3dm). In this passage the only geometrical data are preserved because Rhinoceros cannot support the informative structure typical of a BIM model. For instance, the physical properties of materials, essential for the purpose of energy and comfort simulation, are lost and will need to be restored later. This is one of the interoperability issue that has not yet been solved in order to achieve an effective integrated process.

Information exchange from Rhinoceros to Grasshopper and parametrization

The passage from the modelling software to the VPL environment is automated and don't cause the loss of any information.

Dynamic simulation and mono-objective optimization

To better understand the results in this experimental phase of work it has been decided to carry out both the two processes of mono-objective optimization separately before to start the multi-objective one. However, once the results will be validated, this stage can be avoided.

The dynamic analysis to determine the variation on thermo-hygrometric and visual comfort is developed with the combined use of the plug-ins Ladybug and Honeybee for Grasshopper, while the mono-objective optimization is achieved thanks to Galapagos.

With the aim to activate a process of optimization linked to the model, some quantities have to be become parametric so that the calculation will be able to modify them and register the level of performance related to their state. In the case of this study the rate of glazing surface in relation to the dimension of the external walls is parametrized by the definition of a distinct variable for each orientation of the building. In this way, the result of the optimization process will show the ideal percentage of transparent area separately for the north, east, south and west elevation.

Thermo-hygrometric comfort

In order to make the model ready for the simulations all the properties of materials, building element and energy zones have to be set again because the ones coming from the informative model have been lost during the translation from Archicad to Rhinoceros. The integration of these data is implemented directly inside the VPL environment, thanks to the environmental plug-in Honeybee.  The output of the simulation, according to Fanger's model, represents the values of PMV and PPD in relation to the input conditions, while for the optimization process the rate of time in which the conditions of comfort is verified is evaluated. To solve the mono-objective optimization problem with Galapagos, the variables, which is the window-to-wall ratio, are considered as genomes, while the percentage of time in which the comfort conditions occurred is the fitness function. The result of the calculation will be the rate of transparent area to be installed in each elevation in order to get the number of hours in the year that satisfy the given comfort condition (t_C) in the absence of any HVAC system. This analysis has itself some features of a multi-objective one, even though it is characterized by a single fitness function, because it should consider the advantage and the disadvantages coming from openings in the whole annual cycle whether it is necessary to heat or cool the building.




























Visual comfort

Such as in the previous case, before the implementation of the lighting analysis some information have to be reintegrated with the method already described. In order to evaluate the internal visual comfort, the average Daylight Factor (DFM) is calculated. With this aim, inside Galapagos the objective function is represented by the DFM the objective function indicates that there are advantages only in the expansion of the glazed area. However, the optimization process, with its heuristic approach, will identify the minimal percentage of windows able to maximize the value of the average daylight factor. This means that values under the theoretical limit of 100% indicates that the maximization of DFM has already been reached and further enlargement of the openings will not improve the visual comfort.




























Dynamic simulation and multi-objective optimisation of the two functions

Starting from the same model that has been prepared for the two mono-objective optimizations, the multi-objective optimization process is performed in order to identify the Pareto's frontier of the specific problem. Operationally the structure of the two algorithms defined before remains the same, but they have been jointed together in an only script to be processed with Octopus. The output of this computation is a spare graph that represent the position of all the tested solution in a bi-dimensional space defined by PMV and LDM. The result, in this case, is not an ideal configuration, but a set of them able to maximize simultaneously the two fitness functions.

Information exchange from Grasshopper to Archicad

After the optimization stages have been processed and the ideal configuration has been chosen by the designer, the corresponding informative model have to be restored inside the authoring software. In this stage, by using the Live Connection tool, all the information related to the geometry are directly transferred into Archicad. With regard to the physical properties of material, however, a little work around has provided to be necessary, but in conclusion an effective BIM has been returned to the authoring environment.



























6. Results

In order to achieve thermo-hygrometric optimisation, an initial population of 20 individuals was set.  For the determined optimal configuration, 1120 hours of comfort per year are reached in the absence of any HVAC system, equal to the 24,5% of the total time. The visual comfort optimisation based on simulation, instead, started from a population of 15. The value of the fitness function DFM corresponding the optimal configuration identified is 6,35 which ensure an excellent level of visual comfort.

The results of the two processes of mono-objective optimisation are reported in Table 3.







As it can be seen, in accordance with the hypothesis, the two sets of results identify a pair of very different optimal configurations due to the impossibility of maximize the two objective functions together.

The multi-objective simulation algorithm tested 179 possible configurations.





















At the end of the calculation 15 solutions belonging to the Pareto's frontier have been identified.






















Starting from these results, and on the basis of the relative weights of the two objective functions, the designer can choose which configuration to select. In particular, from Figure 9 three alternatives are highlighted. Alternative n. 6 provides the highest value of DFM, but shows a poor number of comfort hours according to Fanger's model, on the contrary alternative n. 14 corresponds to the best thermos-hygrometric comfort, but is inacceptable due to its low DFM. Alternative n. 5 is balanced for both the objective functions and should be chosen if they have the same weight in the decision-making process.

 Conclusions and future works

Performance optimisation based on simulation while integrated in a BIM process shows a great potential in the field of comfort-oriented high-efficiency building design. The framework that integrates algorithmic and informative modelling can be implemented to optimise multiple objectives related to different disciplines and its result represents the ideal starting point for designers to choose between several design alternatives supported by scientific rigour.

However, an effective integrated workflow is prevented by a series of obstacles fundamentally linked to interoperability. A BIM model, in fact, could contain all the data needed to activate multidisciplinary analyses, but at the state of the art the opportunity of a perfect exchange of information is not effective. To build a functional workflow with present tools, the problem must be discretized, broken down and then reassembled. In a solid integrated framework, the flow of information should be automated without any need of intervention by the designer in order to both prevent the possibility of errors and make the process accessible to a larger pool of professionals. Nowadays, in fact, the difficulties of operation typical of multi-objective optimisation make it very far from the needs of the actors involved in the building process, which don't have any experience of programming, and confine the related experimentation to the academic world. In order to overcome this problem, the presented workflow, such as the others of the same kind, should be included into a more user-friendly plug-in able to activate both simulation and optimisation processes and directly linked to the authoring software with a graphic interface.

With this idea, then, the workflow could be extended to support more than two objective functions because, as explained above, the achievement of internal comfort would request the consideration of a wider set of aspects. Indeed, even though with the present tools the multi-objective optimisation of two functions is a good result, for the future it is desirable to develop frameworks able to deal with a plurality of parameters in the spirit of a real integrated sustainable process.


REFERENCES

Alanne, K., Salo, A., Saari, A., & Gustafsson, S. I. (2007). Multi-criteria evaluation of residential energy supply systems. Energy and buildings, 39(12), 1218-1226.

An, J., & Mason, S. (2010). Integrating Advanced Daylight Analysis into Building Energy Analysis. IBPSA-USA Journal, 4(1), 310-320.

Asadi, E., Da Silva, M. G., Antunes, C. H., & Dias, L. (2012). Multi-objective optimisation for building retrofit strategies: a model and an application. Energy and Buildings, 44, 81-87.

Asl, M. R., Zarrinmehr, S., Bergin, M., & Yan, W. (2015). BPOpt: A framework for BIM-based performance optimisation. Energy and Buildings, 108, 401-412.

Bahara, Y. N., & Nicolle, C. (2017). Building Energy Optimization through Thermal Efficiency Determination using Digital Mock-up Simulation for Heritage Building of Cluny Abbey. Journal of Buildings and Sustainability, 2(1).

Carlucci, S., Cattarin, G., Causone, F., & Pagliano, L. (2015). Multi-objective optimization of a nearly zero-energy building based on thermal and visual discomfort minimization using a non-dominated sorting genetic algorithm (NSGA-II). Energy and Buildings, 104, 378-394.

Deb, K. (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary computation, 7(3), 205-230.

Diakaki, C., Grigoroudis, E., Kabelis, N., Kolokotsa, D., Kalaitzakis, K., & Stavrakakis, G. (2010). A multi-objective decision model for the improvement of energy efficiency in buildings. Energy, 35(12), 5483-5496.

Evins, R. (2013). A review of computational optimisation methods applied to sustainable building design. Renewable and Sustainable Energy Reviews, 22, 230-245.

Flager, F., Basbagill, J., Lepech, M., & Fischer, M. (2012). Multi-objective building envelope optimisation for life-cycle cost and global warming potential. Proceedings of ECPPM, 193-200.

Garber, R. (2009). Optimisation stories: The impact of building information modeling on contemporary design practice. Architectural Design, 79(2), 6-13.

Griego, D., Krarti, M., & Hernández-Guerrero, A. (2012). Optimisation of energy efficiency and thermal comfort measures for residential buildings in Salamanca, Mexico. Energy and buildings, 54, 540-549.

Harmathy, N., Magyar, Z., & Folic, R. (2016). Multi-criterion optimization of building envelope in the function of indoor illumination quality towards overall energy performance improvement. Energy, 114, 302-317.

Haupt, R. L., & Haupt, S. E., 2004. Practical genetic algorithms. John Wiley & Sons.

Jakubiec, J. A., & Reinhart, C. F. (2011). DIVA 2.0: Integrating daylight and thermal simulations using Rhinoceros 3D, Daysim and EnergyPlus. Proceedings of building simulation, 20(11), 2202-2209.

Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimisation using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992-1007.

Lagios, K., Niemasz, J., & Reinhart, C. F. (2010). Animated building performance simulation (ABPS)-linking Rhinoceros/Grasshopper with Radiance/Daysim. IBPSA-USA Journal, 4(1), 321-327.

Lin, S. H., & Gerber, D. J. (2014). Evolutionary energy performance feedback for design: Multidisciplinary design optimization and performance boundaries for design decision support. Energy and Buildings, 84, 426-441.

Machairas, V., Tsangrassoulis, A., & Axarli, K. (2014). Algorithms for optimisation of building design: A review. Renewable and Sustainable Energy Reviews, 31, 101-112.

Nguyen, A. T., Reiter, S., & Rigo, P. (2014). A review on simulation-based optimisation methods applied to building performance analysis. Applied Energy, 113, 1043-1058.

Roudsari, M. S., Pak, M., & Smith, A. (2013). Ladybug: a parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design. In Proceedings of the 13th international IBPSA conference held in Lyon, France Aug.

Tettamanzi, A. G. (2005). Algoritmi evolutivi: concetti ed applicazioni. Mondo Digitale, 2005(1), 3-17.

Welle, B., Haymaker, J., & Rogers, Z. (2011). ThermalOpt: A methodology for automated BIM-based multidisciplinary thermal simulation for use in optimisation environments. Building Simulation, 4(4), 293-313.

Wright, J. A., Loosemore, H. A., & Farmani, R. (2002). Optimisation of building thermal design and control by multi-criterion genetic algorithm. Energy and buildings, 34(9), 959-972.

Zanchetta, C., Paparella, R., Borin, P., Cecchini, C., & Volpin, D. (2014). The role of building energy modeling to ensure building sustainability and quality in a whole system design process. Recent Advances in Urban Planning, Sustainable Development and Green Energy (USCUDAR '14). 87-94.

Home 		Submit Paper   Articles 		Reviewer 		Guidelines 		Process 		About 		Contact

PDF

Journal of Buildings and Sustainability - 2018 - Volume 1, Issue 1


INSIGHTCORE ® - Open Access & Scholarly Source of Buildings and Urban Sciences

Author Guidelines     Privacy Policy      Contact      About

Creative Commons License
© This article is licensed under a Creative Commons Attribution 4.0 International License.