AN INVESTIGATION ON USAGE OF IES SOFTWARE TO IMPROVE ENERGY EFFICIENCY IN BUILDING DESIGN

Author’s Name

Name of the University

Course Name/Number/Assignment Number

Date

CHAPTER 1: INTRODUCTION

1.1 Background

Concerns over energy efficiency in buildings design have grown overtime leading to adoption of various ways to improve the efficiency during the design stages. The importance of understanding energy efficiency of a building cannot be overlooked as the decisions made during the design stage often bear significant impacts of energy efficiency and a building’s internal environment (Kontokosta, 2016).  One of the ways that this has been used is the application of software. IES is one of the software that is applied in designing and operating energy efficient buildings. The software makes it possible to simplify the complex building principles and detailed calculations concerning building thermal system into easy to follow model simulations (Attia et al., 2012). IES is vital to architects and engineers in designing buildings that consume less energy thus bringing sustainability into design (Taleb, 2014). The sustainability and efficiency is made possible by comparing various options of energy consumptions by varying materials and dimensions including low carbon and renewable technologies using the IES software (Rashid et al., 2011).  With the rising concern over sustainability in designs, IES becomes critical in ensuring that building factors adopted are viable moving into the future. The viability is considered based on the energy efficiency levels achieved.

The software is an important component during design in aiding the reduction of energy consumption in buildings. In the construction sector, designers generally accept the fact that a thorough approach must be adopted to balance energy needs of a building and come up with an energy efficient building. Energy consumption reduction must begin at the concept stage thus requiring engineers, architects and contractors to simulate energy factors before concept adoption. Very specific answers must be availed to energy needs of a building right from the concept stage in order to achieve the desired levels of efficiency (Kontokosta, 2016). The software becomes critical in modeling thus providing the needed observations during concept generation. The software also presents various options from which the best choice can be adopted.

IES can be handy to designers in predicting thermal behavior of buildings before their construction. Simulations between the amount of energy lost and energy cost can be done. Additionally, buildings designs can also be manipulated so that specific designs that cater for specific needs are achieved by adopting different materials usage, altering dimensions and placements. Moreover, various building parameters including indoor temperatures, heating and cooling needs, levels of ventilation, natural lighting and comfort can also be altered and their effect simulated in real time (Yigit & Ozorhon, 2018). The possibilities makes it possible for the establishment of retrofitting measures that can be adopted in the building while also considering the existing building regulations. IES can also be used to achieve energy modellings and indoor air quality analysis of a building. The modeling are important in achieving the comfort of occupants. Further, adoption of active retrofits as modelled by IES can also help achieve enhanced building performance due to reduced energy consumption. A detailed analysis of the performance of various materials can be observed from the IES models is critical in choosing the right combination of materials. The choice is made during the design stage thus ensuring that the right balance is achieved between cost and energy needs. The use of modelling software in building design can be traced to the early 1960s when the initial computer models were being analyzed for different applications. However, there was no relevant IT infrastructure and the graphical user interface was new and needed further development. It was until 1975 that the first use of building modelling system was proposed by an architect named Charles Eastman in which he joined distinct geometrical shapes to create objects in the computer. After several developments, the software Revit created in the year 2000 was the major milestone in building modelling systems since it merged building concepts with intelligent solutions in design (DSTDC, 2020). The development of Revit opened way for various modelling systems which are more efficient and customized for a specific need such as IES. The IES software allows for variation of different factors thus enabling variation in building factors to be done during design and the effect determined.  IES has been used for a long time to observe energy requirements of buildings and model for efficiency. Data obtained from IES models form the basis of materials choices, buildings locations and ventilation to be incorporated right from design to construction.

1.2 Statement of the problem

Energy efficiency in buildings design is a key concern to designers and buildings users. Investigating the use of software in designing for energy efficient buildings will make it possible to understand buildings modelling to achieve desirable energy consumption patterns. Besides, it may help identify effective materials to be adopted to reduce building energy needs. Previous studies have concentrated on achieving energy efficiency during the construction stage (Heravi & Qaemi, 2014).

Accordingly, the current study is purposed to establish how IES can be used to adopt energy efficient buildings. Specifically, the study will investigate the use of the software during the design stage to enable designers achieve efficient energy balance. The study will also establish how modellings can be done using the software thus come up with effective materials for various construction needs.

1.3 Aims and objectives

1.3.1 Aim

The main of the study is to explore the effect of energy efficiency in buildings design and construction using IES. Additionally, the research will also aim at determining if IES is the best viable option in buildings design for energy efficiency considering the current global crisis.

1.3.2 Objectives

The main objectives of the study include;

• To study the effects that various variables have on a building’s energy efficiency.
• To conduct a review of previous studies on IES application in design and construction.
• To create models and simulations of buildings using IES.
• Carry out a comparison and analysis of results obtained from simulation models.

1.4 Significance of the Research

Energy efficiency has increasingly become a key concern in the design and construction of buildings. The increased concern has also seen an increase in the interest to come up with software that enable for simulation of energy needs of a building prior to construction. Legislations related to energy efficiency in buildings also make it a requirement for designers to demonstrate energy efficiency of a proposed building prior to its construction (Pérez-Lombard et al., 2011). Furthermore, the effect that a building’s energy needs will have on the occupants make it a necessity that an understanding be made to the general users of energy needs of the building during the design and construction stages. However, most studies have concentrated on only a limited aspect in achieving energy efficiency such as using green materials in construction.  Therefore, the purpose of this study is to determine how IES can be used to predict and improve the energy efficiency of a building during the design stage. The study will also form the basis for building designers to identify factors to consider during design to ensure that desired energy needs are achieved using IES.

1.5 Outline of Dissertation

The current study consists of six main sections. In the first section, a brief background on the study topic is presented. Moreover, the purpose of the study as well as research statement and questions are covered. The second chapter covers review of studies related to improving energy efficiency in building design using IES. The third section involves the strategy and methodology that was used to complete the research. The section covers topics such as research design, participants, and data collection and analysis process. Meanwhile, the forth section will entail the main findings obtained from the study. The information will be summarized in themes and depicted in graphs and charts. Besides, the fifth section will involve discussion of the main findings obtained from the data collected while the sixth section will provide a conclusion and recommendation of this study. Additionally, the sixth section will show the limitations encountered in the current research and how future studies can overcome such limitations.

CHAPTER 2: LITERATURE REVIEW

2.1 Overview

The section presents an analysis of existing study findings with regard to the research topic. Essentially, the purpose of the literature review is to identify the research gaps which are to be fulfilled by the current study. Since energy efficient building designs is a fast changing phenomenon, only the most recent studies were considered for discussion. In this regard, the articles selected were dated not earlier than 2014. From the articles, three themes were identified including principles of energy efficient building designs, challenges of designing and constructing energy efficient buildings, and software usage in promoting energy efficient building designs.

2.2 Principles of Energy Efficient Building Designs

The principles surrounding energy efficient building designs have featured in a number of studies in the recent years (Kylili & Fokaides, 2015; Cao et al., 2016). Specifically, Kylili and Fokaides (2015) examined the concepts governing design and eventual implementation of zero energy buildings which were to give rise to European smart cities. The study noted that zero energy buildings described the effort construct buildings that had zero annual carbon emission. In this regard, the underlying design was focused on practically reducing the building’s energy demand and fully exploiting the renewable energy sources. In this regard, latest technology had to be engaged in harnessing off-grid energy for the buildings from cooling to heating, to transport. The concept was supported in the work of Cao et al. (2016) who investigated the state –of-the-art technologies for zero energy buildings. From the study, three categories of technologies were identified namely, energy-efficient building service systems, passive energy-saving technologies, and renewable energy production technologies. Based on the works of Kylili and Fokaides (2015) and Cao et al. (2016) the main principle in achieving energy efficient buildings encompasses the reduction of energy consumption of buildings from conventional fuel types, and exercising energy saving through renewable sourcing, with the aim of stopping carbon emission and green house effects. Meanwhile, Karimpour et al. (2014) pointed out on yet another principle of energy efficient building designs in terms of energy life cycle. The authors indicated that energy efficient buildings could be achieved by reducing the life cycle of the energy consumed in the buildings right from building material, construction, and operational energy. To realize the objective, equipment efficiency were to be improved and building designs optimized in terms of window area, amount of concrete and insulation levels. Essentially, the optimization focused on the cooling and heating equipment, renewable technologies, and appliances. The present study steps in to show how all the elements can be taken care of in a single software. On the other hand, Yu et al. (2015) noted that obtaining and efficient building design was a multi-objective optimization problem whereby several factors were to be considered such as energy consumption and comfortable indoor environment. From the study, it was realized that while striving to achieve a comfortable indoor atmosphere, heating and cooling came into action, which again increased the building’s energy consumption. Therefore, a multi-objective genetic algorithm was proposed to strike a trade-off between energy consumption and thermal comfort for effective optimization. To carter for comfort in terms of cooling and heating, Liu et al (2017) highlighted the significance of bioinspired materials as building components. The author pointed out that the use of biological materials in building design assisted in obtaining energy efficiency naturally. Therefore, to attain low carbon emission and sustainability, the engineering materials used should also be put into consideration in the design spectrum. Apart from the actual materials, Liu et al (2017) emphasized on the significance of adopting biological gradient orientation in engineering structures for natural temperature comfort of the buildings. In this regard, energy efficiency in buildings convers both renewable energy sources and the materials used in construction. Whereas Cao et al. (2016) focused on passive energy saving techniques for zero energy buildings designs such as advanced building envelopes and passive heating and cooling, Ferrara et al. (2014) highlighted the principle of cost-optimal analysis. Essentially, the study argued that optimization in buildings was based on expenditure in the design and operational process such that cost optimization also formed part of efficient energy buildings. Although the study was only a simulation, it pointed out on an important element of achieve energy efficiency at minimum costs. As part of realizing energy efficiency in buildings, Navarro et al. (2016) expounded on the energy storage concept mentioned by Cao et al. (2016). Specifically, energy storage in the buildings was considered an integrated thermal system which served to enhance the heating, ventilation, and air conditioning systems. The thermal storage could be suspended on the ceiling or floor and integrated in the wall. The presence of stored energy ensured that energy life cycle is reduced and little power is extracted from the sources which translates to energy conservation. According to Navarro et al. (2016), integrating thermal storage functions in buildings is user friendly for architects and engineers at the design stage. The principles of energy efficient building design was also emphasized by Lechner (2014) who stressed on the need for architects to uphold sustainability during design.

The literature indicates that energy efficiency in buildings involves minimizing carbon emission and reducing energy consumption, while still attaining the desired occupant comfort through cooling, heating, and lighting. The scenario presents a study gap for the present research to examine how particular software can be applied in solving the energy efficiency puzzle in building designs. Another literature gap identified was that they did not explore the use of software in designing energy saving buildings. The gap will be addressed in the present study in which the implementation of the technologies will be considered using IES software to analyze designs that ensure optimal energy usage.

2.3 Challenges of Designing and Constructing Energy Efficient Buildings

The ambition of making buildings greener is faced by a number of challenges as reported by researchers over time (Yan et al., 2015; Hong et al., 2016). In particular, Yan et al. (2015) highlighted the problem of occupant behavior. The authors noted that it was difficult to get the real picture of the expected occupant activities during the design simulation stage leading to errors in the final prototypes. Essentially, the missing data included the number of people expected to stay in the building at any particular time and the related activities with the windows, doors, ventilation, and the heating and cooling systems. The issue of occupant behavior was reflected in the study of Hong et al. (2016) who examined energy–related occupant behavior in buildings. In the same breathe, Hong et al. (2016) pointed out that it was a problem to predict and simulate models of occupant conduct with regard to energy use optimization resulting to misleading approximations. Generally, Yan et al. (2015) and Hong et al. (2016) showed that building designers could not accurately quantify the impact of occupants on building energy consumption. Sorrell (2015) explained that the problems deepens with the increased in wealth which has seen energy demands rise significantly. In this regard, the building design simulation models are encountered with a serious challenge of incorrect simplification of lifestyle and subsequent energy demands (Hong et al., 2016). Moreover, a perceived increase in energy consumption among the rich leaves architects with the grand challenge of determining potential energy demand reduction points. The posted challenges open the way for testing the effectiveness of IES software for green building designs. To meet the increasing energy demand pointed out by Sorrell (2015), Lund et al. (2014) proposed the integration of smart thermal grids into sustainable building designs. However, the smart grids faced a major challenge of integrating with low-temperature heat sources which are the blueprint of energy efficient buildings. At the same time, the new thermal grids were only suitable for high-energy buildings. Therefore, designing low-energy buildings under smart thermal grid presented difficulties. Meanwhile, Nguyen et al. (2014) highlighted the computer based challenges in the design of greener buildings. The authors noted that buildings presented many optimization problems which called for many solution algorithms for efficient energy consumption. To this end, the challenges faced at the computer simulation scale included dealing with discontinuous data in the problems. Moreover, the study reported the challenge of selecting an appropriate algorithm among the many present for an effective solution. Consequently, the multi-objective building optimization problem was noted to persist unless a single objective approach is utilized. The presence of many configurations for a single optimization problem was also highlighted by Vakiloroaya et al. (2014) who concentrated on energy saving of heating and cooling. Essentially, that the HVAC systems featured many technologies and approaches with each showing different potentials in energy conservation. In this regard, a considerable amount of time is spent in the selection process and simulations when deciding on the most appropriate HVAC approach. Moreover, each technology has distinct design requirements which makes models to be altered each time a new method is simulated. On the other hand, Chau et al. (2015) focused on the challenge with life cycle energy assessment for buildings. The study reported that three streams of life assessment studies are present namely, Life Cycle Assessment (LCA), Life Cycle Carbon Emissions Assessment (LCCO2A), and Life Cycle Energy Assessment (LCEA). Although the streams had a similar objective, Chau et al. (2015) noted that they differed in methodology and evaluation focus. As a result, the findings of the three shows discrepancies which limits their significance in sustainable building design decision making. From the literature it was realized challenges exists in energy efficient building designs, some of which the present application of software attempts to solve. Also, the literature mainly focusses on examining how occupants’ behavior influence energy usage in buildings. In this regard there is a gap on how inherent building designs including ventilation levels, natural lighting, and design of indoor spaces influence energy efficiency in buildings. The gap is addressed in this study by investigating how software can be used at the design stage to ensure energy is optimally used when the building is operational.

2.4 Software Usage in Promoting Energy Efficient Building Designs

To promote energy efficient building designs, software tools have been incorporated in the energy demand and consumption monitoring as reported in a number of studies (De Boeck et al., 2015; Albatayneh et al., 2017). Specifically, De Boeck et al. (2015) noted that various energy simulations are normally carried out which brings to light the necessity of appropriate software programs. Basically, the essence of simulations is to obtain the optimal design variables owing to the many parameters under consideration. At the same time, software simulations were utilized in implementing the parameters, thus eliminating attempts of trial and error. Therefore, software use in sustainable building design has a primary role of reducing inherent complexities and attaining accuracy and precision. In this breathe, Albatayneh et al. (2017) presented the findings of using energy-based and temperature-based approach software programs in building thermal assessment. The authors showed that tools such as Australian AccuRate Software effectively assessed the heating and cooling loads and calculated an approximate energy consumption of buildings. Notably, the software combined parameters such occupants’ behavior, temperature, and external climate to derive the energy solutions. Through the software it was possible to determine the lifetime energy usage of a building and plan on optimization. The use of software in simulation was further supported by Sadeghifam et al. (2015) who examined energy assessment in tropical buildings. In particular, the study used Revit Architecture software to combine optimization elements such as walls, windows, ceilings, roofs, and floors with air quality factor to derive an energy efficient design for the tropic buildings. To understand energy consumption patterns, energy analysis software was used. In this regard, software usage contributes to useful analysis for deigning the optimal energy efficiency plans in buildings. The concept was reflected in Shoubi et al. (2015) who focused on information modelling tools. Specifically, the authors reported that energy usage during the lifecycle of a building can be analyzed virtually using Building Information Modeling (BIM) in the design stage. The particular BIM tools utilized were Autodesk Ecotect Analysis software and Revit architecture 2012. Through the software tools, the operational energy of double story building was assessed and sustainable solutions proposed. Essentially, the software saves time and errors of manual determinations. Meanwhile, Mylonas et al. (2019) showed that software tools could be used as a support for hardware energy sensing infrastructure. In their study, Mylonas et al. (2019) utilized Augmented Reality (AR) software to obtain real-time data from IoT installed in school buildings. With the help of AR, IoT data was easily obtained, analyzed and the functionality status of the buildings with regard to energy consumption reported. Apart from AR technology, Brundu et al. (2016) reported on the usage of IoT software. Essentially, the tool was programed to assess the real-time energy values from the sensors in relation to the environmental data. The program was based on BIM and allowed detailed energy simulations of the various energy profiles collected from the sensors. At the same time, IoT software was capable of comparing building energy requirements with the smart energy grid models for sustainability. According to Østergård et al. (2016) building simulations are essential for decision making purposes in the early stages of energy efficient design. Through programs such as performance software and CAD software, the architects and engineers are able to execute intelligent proactive, intelligent and experienced building designs without much straggle. Moreover software tools are useful in testing the inter-operatabilty of various energy optimization solutions through simulations. Based on the literature, software tools are shown to promote energy efficient building designs by monitoring energy profiles, simulating optimizations, and proposing the most appropriate sustainability approaches. However, a gap exists on the usage of IES to develop energy efficient building design. As a result, the present study will assess the application of IES software in green building design and hence confirm the benefits noted.

2.5 Conclusion

Previous studies which have been analyzed indicate an overview in underrating the concept of energy efficient building designs right from the principles, to challenges, to the involvement of software tools. As such, the review allows for the present study to test the functionality and efficiency of IES software in enhancing the existing principles of design. A major literature gap is that existing literature has mainly focused on examining energy efficiency from the perspective of targeted occupants of a building without considering the designs, materials, and dimensions which ca influence energy efficiency.  Additionally, the studies which have examined use of software to analyze energy efficiency have focused on Building Information Modelling and Revit thereby leaving a gap on analysis of IES effectiveness in promoting energy efficiency. Moreover, the technology will attempt to solve some of the challenges and compare with the already discussed software in the studies. Essentially, the present research will add to the body of knowledge regarding software usage in promoting energy efficient building designs with particular reference to one software tool.

CHAPTER 3: METHODOLOGY

3.1 Introduction

The current chapter outlines the steps taken in collecting on the application of IES to improve energy efficiency in buildings. The chapter is subdivided into several sections such as research approach, data collection techniques, research design, and data analysis. The research design section utilizes analysis of previous work done using the software from similar studies on IES. The data gathering process involved obtaining data from IES modelling to increase energy efficiency from models developed. Data analysis was done by observing trends from data obtained from the models.

3.2 Research Approach

The two main approaches use in research include inductive and deductive approach. On the one hand, deductive approach involves developing a hypothesis then conducting research to prove the hypothesis (Saunders et al., 2015). On the other hand, inductive approach involves developing a new theory based on the data patterns identified from research. For this study, a deductive approach was employed (Saunders et al., 2015). The main principle adopted involved using the IES modelling software to create various simulations and observe the behavior of the building in terms of energy efficiency under the varying conditions. As such, the software was used to check accuracy of the hypothesis that building information software can help in improving energy efficiency of a building.

3.3 Research Philosophy

Research philosophy is the belief oh how a group of data should be collected and synthesized to address a specific research question (Rahi, 2017). The two main philosophies commonly used in research include positivist and interpretivist. Typically, interpretivist philosophy is employed in situations where interview and survey responses are used in a research. In such cases, the researcher is expected to interpret the data from the perspective of the respondents to better understand the intended meaning. However, for the current research, a positivist philosophy will be used. Essentially, positivist theory explains that a researcher can better learn about a topic by observation and reason (Stratford and Bradshaw, 2016). Positivist philosophy posits that knowledge can be best obtained by observation or conducting experiments to note the interactions between different elements in nature (Stratford and Bradshaw, 2016). For this particular research, simulations done in IES software were observed and compared using quantitative methods to determine the most appropriate models in maximizing energy efficiency in buildings.

3.4 Research Design

Research design describes a planned structure outlining strategies taken to collect and analyze data (Tight, 2016). For this particular research, a descriptive research design was employed. Typically, a descriptive research design entails analyzing ‘what’ a topic entails rather than answering the question ‘why’. However, rather than describing responses from interview or survey, the current paper answers that question of what IES software can assist in during building design to promote energy efficiency. For the current research, software modelling was applied to vary key components of energy requirements in a building to observe the combinations that offer greatest energy efficiency. IES software models were created in design computers where various factors could be simulated and outcomes observed. The method was chosen due to its suitability in observing energy requirements of a building prior to their construction. The modelling helped in observing the relationship between energy efficiency and various building variables. However, a key limitation of the method is that developing the models may sometimes overlook key building factors.

3.5 Data Collection Procedure

For the current study, IES software was the primary source of collecting simulated data. IES software was used to create models from which manipulation of the building models was used to produce different simulations of variables.  Simulations of a building’s energy aspects can be used to come up with occupant modelling to observe energy requirements and how to minimize them (Jia, Srinivasan & Raheem, 2017). For the current study, various factors were simulated from the models. One of the factors was energy lost. Energy lost was manipulated by varying a building’s location and environmental conditions which influences energy loss (Yuksek & Karadayi, 2016). The topography of the location, which affect solar incidence angle, was also varied and various values of energy lost obtained. Further, the building envelopes such as wall, ground, ceiling windows and doors were also varied and energy performance of the building observed. Additionally, indoor temperature were also modelled. Indoor temperatures were varied using ventilations, window and door opening sizes and color. Various values against variants were observed and recorded to determine levels of energy efficiency. Cooling and heating needs of the building were also observed from the models. Heating and cooling needs were modelled by changing environmental conditions such as temperature and humidity. Other factors modelled by changing a buildings openings were ventilation and natural lighting all of which also play a key role in energy efficiency of a building. Occupant comfort was also modelled by varying various internal and ambient conditions of a building and observing energy consumption. The various data on buildings conditions were recorded for analysis.

3.6 Data Analysis

For the current study, data obtained from the models developed were grouped into specific data sets for easy interpretation. First, the main concepts of energy efficiency were created from which the data was applied to observe patterns. The second step involved coding the data by highlighting key phrases and then grouping data into themes and sub-themes. The data is then scanned for observable trends and presented using data presentation and statistical analysis tools for easy observation. From the tools, a trend is developed which can be used to make recommendations. The themes were reviewed to ensure they have specific patterns which accurately represent the data (Houghton et al., 2015). The variations in energy factors were observed and results recorded in tables. The trends observed were then compared against observations made by other researchers to come up with conclusions and recommendations. The results of literature synthesized and model case study are presented in the next chapter.

3.7 Methodological Limitations

The main limitation of the modelling procedure is its possibility for errors to occur during the modelling process. Errors encountered during modelling can result into wrong data which negatively influences the results observed. The models are only as good as information used to build them thus wrong information would give wrong results.  Additionally, models are merely simplifications of reality and thus cannot replace reality.

3.8 Ethical Considerations

There were several ethical considerations which were observed during the simulation process. Firstly, the principle of honesty was obtained in which the data employed was based on actual simulations conducted in IES software. Additionally, integrity was achieved by properly referencing all information extracted from literature to avoid issues of plagiarism. The other ethical principle observed involved reporting the actual data drawn from the software modelling without trying to manipulate or provide false information.

3.9 Summary

This chapter focused on the steps taken to achieve the research objectives. The study utilized observations made from IES models. The models simulated various factors in a building that influences its energy efficiency. The data gathering method was not only cost effective and safe but also convenient and time saving. The model simulations also enabled for observations on real effect of each factor thus obtaining results of varied factors. The research analyzed results from various studies on energy efficiency design in buildings using IES software. Data analysis was then done to help come up with observable trends from data obtained. The process of data analysis was aided by using data analysis tools which makes it easy to observe trends. From the analysis and trends established, conclusions and recommendations concerning energy efficiency in buildings’ design was done. Data from previous studies assisted in validating the trends observed.

REFERENCES

Albatayneh, A., Alterman, D., Page, A. and Moghtaderi, B., 2017. The significance of temperature based approach over the energy based approaches in the buildings thermal assessment. Environmental and Climate Technologies, 19(1), pp.39-50.

Attia, S., Gratia, E., De Herde, A. and Hensen, J.L., 2012. Simulation-based decision support tool for early stages of zero-energy building design. Energy and buildings, 49, pp.2-15.

Brundu, F.G., Patti, E., Osello, A., Del Giudice, M., Rapetti, N., Krylovskiy, A., Jahn, M., Verda, V., Guelpa, E., Rietto, L. and Acquaviva, A., 2016. IoT software infrastructure for energy management and simulation in smart cities. IEEE Transactions on Industrial Informatics, 13(2), pp.832-840.

Cao, X., Dai, X. and Liu, J., 2016. Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and buildings, 128, pp.198-213.

Chau, C.K., Leung, T.M. and Ng, W.Y., 2015. A review on life cycle assessment, life cycle energy assessment and life cycle carbon emissions assessment on buildings. Applied Energy, 143, pp.395-413.

De Boeck, L., Verbeke, S., Audenaert, A. and De Mesmaeker, L., 2015. Improving the energy performance of residential buildings: A literature review. Renewable and Sustainable Energy Reviews, 52, pp.960-975.

DSTDC, 2020. A history of building information modelling. [Online]. Available at: https://www.digitalschool.ca/history-bim/

Ferrara, M., Fabrizio, E., Virgone, J. and Filippi, M., 2014. A simulation-based optimization method for cost-optimal analysis of nearly Zero Energy Buildings. Energy and Buildings, 84, pp.442-457.

Heravi, G., & Qaemi, M. 2014. Energy performance of buildings: The evaluation of design and construction measures concerning building energy efficiency in Iran. Energy and Buildings, 75, 456-464.

Hong, T., Taylor-Lange, S.C., D’Oca, S., Yan, D. and Corgnati, S.P., 2016. Advances in research and applications of energy-related occupant behavior in buildings. Energy and buildings, 116, pp.694-702.

Houghton, C., Murphy, K., Shaw, D., & Casey, D. 2015. Qualitative case study data analysis: An example from practice. Nurse researcher, 22(5).

Yuksek, I and Karadayi, T., 2016.  Energy-Efficient Building Design in the Context of Building Life Cycle. Available: https://www.intechopen.com/books/energy-efficient-buildings/energy-efficient-building-design-in-the-context-of-building-life-cycle

Jia, M., Srinivasan, R.S. and Raheem, A.A., 2017. From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency. Renewable and Sustainable Energy Reviews, 68, pp.525-540.

Karimpour, M., Belusko, M., Xing, K. and Bruno, F., 2014. Minimising the life cycle energy of buildings: Review and analysis. Building and Environment, 73, pp.106-114.

Kontokosta, C. E. (2016). Modeling the energy retrofit decision in commercial office buildings. Energy and buildings, 131, 1-20.

Kylili, A. and Fokaides, P.A., 2015. European smart cities: The role of zero energy buildings. Sustainable Cities and Society, 15, pp.86-95.

Lechner, N., 2014. Heating, cooling, lighting: Sustainable design methods for architects. John wiley & sons.

Lin, J. J., Han, K. K., & Golparvar-Fard, M. 2015. A framework for model-driven acquisition and analytics of visual data using UAVs for automated construction progress monitoring. In Computing in Civil Engineering 2015 (pp. 156-164).

Liu, Z., Meyers, M.A., Zhang, Z. and Ritchie, R.O., 2017. Functional gradients and heterogeneities in biological materials: Design principles, functions, and bioinspired applications. Progress in Materials Science, 88, pp.467-498.

Lund, H., Werner, S., Wiltshire, R., Svendsen, S., Thorsen, J.E., Hvelplund, F. and Mathiesen, B.V., 2014. 4th Generation District Heating (4GDH): Integrating smart thermal grids into future sustainable energy systems. Energy, 68, pp.1-11.

Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M., 2018. Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578.

Mylonas, G., Triantafyllis, C. and Amaxilatis, D., 2019. An augmented reality prototype for supporting IoT-based educational activities for energy-efficient school buildings. Electronic Notes in Theoretical Computer Science, 343, pp.89-101.

Navarro, L., De Gracia, A., Colclough, S., Browne, M., McCormack, S.J., Griffiths, P. and Cabeza, L.F., 2016. Thermal energy storage in building integrated thermal systems: A review. Part 1. active storage systems. Renewable Energy, 88, pp.526-547.

Nguyen, A.T., Reiter, S. and Rigo, P., 2014. A review on simulation-based optimization methods applied to building performance analysis. Applied Energy, 113, pp.1043-1058.

Østergård, T., Jensen, R.L. and Maagaard, S.E., 2016. Building simulations supporting decision making in early design–A review. Renewable and Sustainable Energy Reviews, 61, pp.187-201.

Pérez-Lombard, L., Ortiz, J., Coronel, J. F., & Maestre, I. R. (2011). A review of HVAC systems requirements in building energy regulations. Energy and buildings, 43(2-3), 255-268.

Rahi, S. 2017. Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1-5.

Rashid, Y.R., Sulaiman, M.S., Aziz, A., Selamat, H., Yani, A.H.M. and Kandar, M.Z., 2011. Greening government’s office buildings: PWD Malaysia experiences. Procedia Engineering, 21(0), pp.1056-1060.

Sadeghifam, A.N., Zahraee, S.M., Meynagh, M.M. and Kiani, I., 2015. Combined use of design of experiment and dynamic building simulation in assessment of energy efficiency in tropical residential buildings. Energy and Buildings, 86, pp.525-533.

Saunders, M. N., Lewis, P., Thornhill, A., & Bristow, A. 2015. Understanding research philosophy and approaches to theory development.

Shoubi, M.V., Shoubi, M.V., Bagchi, A. and Barough, A.S., 2015. Reducing the operational energy demand in buildings using building information modeling tools and sustainability approaches. Ain Shams Engineering Journal, 6(1), pp.41-55.

Sorrell, S., 2015. Reducing energy demand: A review of issues, challenges and approaches. Renewable and Sustainable Energy Reviews, 47, pp.74-82.

Stratford, E. and Bradshaw, M., 2016. Qualitative research design and rigour. Oxford University Press, Ontario, Canada, pp. 69-80.

Suleymanova, L. A., Lesovik, V. S., Kara, K. A., Malyukova, M. V., & Suleymanov, K. A. (2014). Energy—Efficient Concretes for Green Construction. Research Journal of Applied Sciences, 9(12), 1087-1090.

Taleb, H.M., 2014. Using passive cooling strategies to improve thermal performance and reduce energy consumption of residential buildings in UAE buildings. Frontiers of Architectural Research, 3(2), pp.154-165.

Tight, M., 2016. Examining the research/teaching nexus. European Journal of Higher Education, 6(4), pp.293-311.

Vakiloroaya, V., Samali, B., Fakhar, A. and Pishghadam, K., 2014. A review of different strategies for HVAC energy saving. Energy conversion and management, 77, pp.738-754.

Yigit, S., & Ozorhon, B. (2018). A simulation-based optimization method for designing energy efficient buildings. Energy and Buildings, 178, 216-227.

Yan, D., O’Brien, W., Hong, T., Feng, X., Gunay, H.B., Tahmasebi, F. and Mahdavi, A., 2015. Occupant behavior modeling for building performance simulation: Current state and future challenges. Energy and Buildings, 107, pp.264-278.

Yu, W., Li, B., Jia, H., Zhang, M. and Wang, D., 2015. Application of multi-objective genetic algorithm to optimize energy efficiency and thermal comfort in building design. Energy and Buildings, 88, pp.135-143.