Energy Modelling and Sustainability: A Review on Current ... · Accordingly, this is achieved through modelling the energy performance by varying a set of green factors such as thermal

4th International Conference On Building Energy, Environment

Energy Modelling and Sustainability: A Review on Current Theories, Practices, and Challenges

M. Hajj-Hassan 1, H. Khoury 11 Department of Civil and Environmental Engineering

American University of Beirut, Beirut 1107 2020, Lebanon

SUMMARY Green building and energy modelling tools have been put forward to mitigate the impact of energy consumption in the building sector. Despite the huge advances in building performance simulation and the abundance of sustainable rating systems, buildings are still not meeting the intended levels of energy performance, especially in the case of heating, Ventilation, and Air conditioning (HVAC) systems. In fact, these systems are considered one of the most energy-consuming building entities that are highly affected by various building design parameters as well as the behavior of occupants. Therefore, this paper takes the initial steps and aims at: (1) offering a critical review of published research papers and reports that focus on the different sustainable design and energy simulation approaches targeted at reducing energy consumption in HVAC systems, and (2) presenting a comparative analysis of different energy modelling tools and green rating systems. More specifically, the rating systems and their requirements are examined at the energy level to identify the green factors that highly affect building design parameters. It was found that more efficient HVAC systems can be designed by coupling modelling techniques with optimization algorithms. Finally, the limitations in this research area are identified and some potential breakthroughs include but not limited to: (1) standardize the exchange of simulation data for energy modelling, and (2) developing a simulation environment to study and optimize both the parametric design and behavioral factors.

INTRODUCTION

HVAC systems account for half of the energy consumed in buildings and around 10-20% of total energy consumptions in developed countries. Based on the above considerations, and with the increased demand for thermal comfort, energy consumption associated with HVAC systems will continue to grow (Pérez-Lombard et al. 2008).

Since the “energy crisis” in the 1970’s, different protocols, committees, governmental agencies, and organizations diverted their attention to higher efficiency systems to lessen the damage to the environment. Later in 1987, the Montreal protocol was founded, and the negative impact of refrigerants on the environment surfaced as major contributors to ozone depletion and global warming. By 1990, the world’s first green building certification system was published. From there on, the green building trend has been growing by developing new building regulations and certifications to ensure energy efficiency.

There are many facets to sustainable building design, of which the HVAC impact is of paramount importance since most of the energy consumed by a building is due to heating,

cooling and lighting. However, there are no clear guidelines to follow on how to select the most optimal HVAC system and it is typically carried out by the design engineer who prioritizes different selection criteria in order to achieve the functional requirements associated with the building program and design intent. For instance, ASHRAE (2013) defines 10 different criteria for HVAC system and equipment selection including energy conservation, space limitation, first vs lifecycle costs, maintainability, and controllability. Schwedler (2017) suggests the use of variable primary flow system as compared to primary-secondary chilled water system to save energy. This work also highlights the importance of considering part-load conditions of the building and found out that designing part-load chillers saves additional energy. However, Korolija et al. (2011) showed that it is difficult to cast a judgment about building performance based on only building heating and cooling loads. System configuration and operational parameters are of equal importance. Given the type of HVAC system used, system demand and building demand can vary from over -40% to + 30% for cooling and between -20% and +15% for heating. In addition to optimizing the configuration of a system, Vakiloroaya et al. (2014) examined technologies such as evaporative cooling systems, evaporative-cooled air conditioning systems, ground-coupled HVAC systems, thermal storage systems, and heat recovery systems as optimization techniques for the mechanical design of a traditional HVAC system. Results showed that potential energy savings of up to 60% could be achieved by decreasing the thermal loads on the system. Yet, the energy consumption of the HVAC system was not solely depending on system performance and operational parameters, but also on the thermodynamic behavior of the building. Apart from this set-based approach, another research effort has suggested that performance-based decision making is key to successful design and selection of an energy-efficient HVAC system (Mwasha et al. 2011). Accordingly, this is achieved through modelling the energy performance by varying a set of green factors such as thermal performance and material efficiency. Similarly, Roderick et al. (2009) used IES-VE as a simulation tool to quantitatively benchmark three building environmental assessment schemes: LEED, BREEAM and Green Star. The simulation results indicated that the HVAC system is the most heavily weighted variable in the energy assessment of all schemes. It was also shown that it is not possible to quantify the energy performance of a building based on an assessment scheme. As a matter of fact, the same building received high energy rating score in the Green Star scheme, low energy rating score in the BREEAM scheme and failed to be certified in the LEED scheme. Furthermore, other studies have described different approaches that can reduce HVAC energy consumption. From what has been presented, these approaches can be classified under two categories; (1) Set-based HVAC system approach and (2) Performance-based code compliance

ISBN: 978-0-646-98213-7 COBEE2018-Paper260 page 781


approach. However, a comprehensive study that describes, lists and compares a wide range of different strategies for HVAC energy saving remains a gap in the existing body of research. Therefore, the objective of this paper is to summarize published work on different design and simulation approaches that aim at reducing energy consumption in relation to HVAC systems in commercial and residential buildings.

The review is divided in two main sections, namely energy modelling and sustainable building design. The first section starts by comparing Load Calculation and Energy Modelling and concludes that the latter acts as an enabler tool that can shift the design process from a prescriptive to a performance-based one. The modelling features and reporting results of five simulation programs are then contrasted and a generic eight-step modelling structure is proposed. On the other hand, the second section examines three green rating schemes, together with their requirements, at the energy level to identify the green factors that highly impact building design parameters. These factors are, in turn, categorized under different modelling elements in the light of future coupling of the simulation engine with optimization techniques.

ENERGY MODELLING

In its simplest form, an energy model is a calculation engine that takes as input building geometry, system characteristics, and operations schedules and produces performance comparison reports as output. In this case, the two most prominent calculation methods used are the CIBSE (2006) Admittance Method (2006), and ASHRAE (2013) Heat Balance method (2013). The equations governing both these methods are not included in this review. Instead, the software programs incorporating these methods are analysed and compared. According to AIA (2012), one of the greatest benefits of energy modelling consists of integrating interrelated design issues which enables better decisions concerning building parameters and systems. Yet, before delving into available energy modelling techniques, the difference between load calculation and energy modelling warrants some discussion.

Load Calculation and Energy Modelling

The dialogue surrounding load calculation and energy modelling has been interpreted differently by researchers and practitioners. Load calculations form the basis for the design and selection of HVAC equipment. By simulating the thermodynamic behavior of the building at extreme conditions, the HVAC engineer can determine the peak loading values that help in estimating equipment capacities, volumetric air flow requirements and supply temperatures. Depending on the application at hand, the ASHRAE (2013) handbook of fundamentals provides different sorts of design conditions as a basis for the load simulation. For example, the Cooling Dry Bulb (DB) and the Mean Coincident Wet Bulb (MCWB) are used in sizing chillers. The evaporative Wet Bulb (WB) and the MCWB are alternatively used in sizing cooling towers and outdoor-air systems. Table 1 provides a summary of different annual cooling design conditions together with corresponding applications.

On the other hand, energy modelling is a design enabler tool that can be utilized throughout the design process to evaluate various design options and optimize the overall

performance of building parameters. More specifically, Building Energy Modelling (BEM) predicts energy performance based on a Typical Meteoroidal Year (TMY) and not on extreme design conditions. In this case, the modeller can: (1) predict the complete hourly load profile for the whole year, (2) estimate monthly and annual energy costs, and (3) compare and contrast different energy efficient options. Furthermore, according to the American Institute of Architects (AIA 2012), energy modelling enhances design flexibility and team integration by shifting an overall prescriptive-based design process to a performance-based one.

Table 1. Applications for selected cooling design conditions (ASHRAE 2013)

Climatic Design Information Application

Ann

ual C

oolin

g D

esig

n C

ondi

tions

(Hot

test

Mon

th)

Cooling DB

MCWB

Used in sizing cooling equipment such as chillers or air-conditioning units.

Evaporative WB

MCWB

Used in sizing of cooling towers, evaporative coolers, and outdoor-air ventilation systems

Enthalpy

MCDB

Used for calculating cooling loads caused by infiltration and/or ventilation into buildings.

Dehumidification Dew Point (DP)

MCDB

Humidity Ratio

Used for humidity control applications and outdoor air ventilation systems

Building Energy Modelling Software Programs

With today’s increasing focus on sustainability, there is a pressing need to understand the technologies that predict, test, and quantify the energy performance in a building. Ever since the Energy crisis in the 1970’s, sustainability has gained a lot of attention and as such, the energy simulation industry has developed rapidly with several building energy modelling programs (BEMPs) adopted worldwide. These include but are not limited to: EnergyPlus, Hourly Analysis Program (HAP), Ecotect, Trace 700, and TRNSYS. Each of these programs, in particular EnergyPlus, HAP, and Ecotect, are briefly explained through details provided by the program developers and hands on experience.

EnergyPlus is a free tool developed by the Department of Energy (DOE) for energy simulation, load calculation and building performance. It is a data compiler that takes as input a text file, runs a solution algorithm, and displays results textually. As such, since the software doesn’t have any graphical capabilities, interfaces such as DesignBuilder and OpenStudio are typically used to model complex geometrical shapes. The loads are calculated using ASHRAE’S heat balance method at user defined time steps.

Hourly Analysis Program (HAP) is used for designing HVAC systems with energy analysis capabilities. HAP is known among practicing engineers as an efficient tool to estimate loads, design HVAC systems and evaluate energy performance. HAP uses the Transfer Function Method (TFM) which is a simplified form of the heat balance used in EnergyPlus.



TRACE 700 is a load and energy calculation program that uses the Radiant Time Series (RTS) method for thermal modelling. Similar to HAP, it is popular among HVAC professionals as both are developed by HVAC equipment and control manufacturers. Additionally, TRACE is considered a user-friendly software and creating a visual image of the building is not needed. All surfaces are input in a two-dimensional format and schedules can be easily modified. The software output is displayed in the form of reports that predict loads imposed on the building and equipment, energy consumption, and life cycle costs.

Ecotect is a visual architectural design and analysis tool that uses the CIBSE Admittance methods as the calculation engine for thermal modelling. The software also handles a wide range of performance analysis functions including energy, lighting, shading, and acoustics. Ecotect can as well simulate complex geometries and display analytical results interactively within the context of the model.

TRNSYS is a graphically-based software used to simulate the behavior of transient systems. It has a modular structure composed of a visual interface and a programmable simulation engine, making it one of the most flexible tools used by researchers and speciality consulting firms. More specifically, building data is input through the visual interface and the engine then simultaneously solves all HVAC-system components and building thermal envelopes at each defined time step. Although this tool is very powerful and flexible, it is not approved for code compliance.

Hence, given the extensive list of BEMPs, several comparative surveys have been carried out. For instance, Crawley et al. (2008) contrasted the capabilities of twenty major building energy simulation programs including the aforementioned ones. The survey included an extensive summary comparing features such as building envelope, HVAC systems, results reporting, economic evaluation, and user interface. EnergyPlus was found to be one of the most mature tools covering almost all the listed categories. Chinnayeluka (2011) used Ecotect, EnergyPlus, and IES-VE to compare the CIBSE Admittance method with the ASHRAE Heat Balance Method. The annual energy consumption values for each of these energy software programs were compared with actual data collected. Results showed that EnergyPlus produced only +1.37% deviation from actual data, while both Ecotect and IES-VE underestimated energy consumption by 29.8% and 32.41% respectively. On the other hand, Zhu et al. (2013) studied the impact of using three simulation engines – EnergyPlus, DeSt, and DOE-2.1E - on the building’s thermal load calculation. In another study,Zhou et al. (2014) used the same engines to identify themain elements that contribute towards discrepancies insimulation. Two important conclusions were reached fromthe last two research efforts: (1) Matching user inputs is keyto reducing discrepancies in simulation results and (2) HVACcontrol strategies used can cause major discrepancies insimulation results.

Energy modelling workflow

After examining the five aforementioned simulation programs and comparing their general modelling features and reporting results, a generic structure was thereby determined for a typical BEMP. The structure comprises eight steps that can be used for load calculation or energy modelling purposes as follows:

1- Input geographical location and associated weatherdata. Most programs include a built-in library of

weather data for many locations and if not available, a weather data file can be generated or imported. For example, Meteonorm uses interpolation models to generate accurate weather file for any place on earth.

2- Generate schedules describing occupant densities/functions, lighting densities, and equipment load. These schedules are used to calculate the internal loads generated.

3- Define the required outdoor airflow and indoorconditions set points. Standards such as ASHRAEStandard 62.1 are used to calculate the ventilationrequirements for an acceptable indoor air quality.

4- Specify the characteristics of construction materialssuch as the thermal properties of walls, roofs andwindows.

5- Input a detailed description of each thermal zone.This is the most time-consuming step. In HAP andTRACE, for example, the surfaces are input in atwo-dimensional format in assigned commandwindows. EnergyPlus, alternatively, has differentinterfaces which allow a direct and virtualconstruction of the building and import of thesurfaces and thermal zones.

6- Define the characteristics of the HVAC air-sideequipment. System types include terminal fan coilunits, split systems, and unit heaters.

7- Select the HVAC water-side equipment (e.g.chillers, boilers) together with configurations suchas pumping arrangement, schedule of operation,and control scheme.

8- Add the energy consumption rates, utility rates, andall necessary economic data. For energy modelling,ASHRAE Standard 90.1 provides minimum energyperformance requirements that the model mustmeet in order to obtain a permit or be qualifiedunder one of the green rating systems.

Optimization-Based Selection

Traditionally, parametric studies were conducted to resolve building optimization problems. Al-Homoud (2005) studied the effect of thermal insulation on the selection of the HVAC system while holding other effective design parameters constant. Multi-objective optimization tools that use evolutionary algorithms have been created to handle a large number of design variables. Norford (2003) applied genetic algorithms to simultaneously optimize the building envelope and the design and operation of an HVAC system. Fortunately, interfaces that couple simulation engines with an optimization algorithm exist and these are: Matlab, GenOpt and BeOpt

Matlab is the multi-paradigm computing programing tool that can be used for algorithm development, data analysis, and graphical user interface design. It has a specific application toolbox for optimization that can be easily coupled with a simulation engine. For instance, Hamdy et al. (2011) applied a multi-objective optimization approach using Matlab combined with a simulation program in order to design low-emission cost-effective dwellings. The analysis demonstrated the simultaneous influence of the design parameters on the building emission, investment cost, and thermal comfort.

GenOpt is a generic optimization program that can be coupled with simulation programs that have text-based inputs and outputs, e.g. EnergyPlus and TRNSYS. It allows a multidimensional optimization by systematically varying design parameters to minimize or maximize an objective function. Asadi et al. (2012) reported an optimization case



study involving TRNSYS, GenOpt and a multi-objective optimization algorithm developed in Matlab. Three objective functions were employed to optimize the retrofit cost, energy savings and thermal comfort of a residential building. Results showed that this approach provided retrofit actions that improve energy efficiency based on a predefined set of parameters and constraints.

BeOpt is another building energy optimization software that uses EnergyPlus as a simulation engine. It uses a sequential search optimization technique to evaluate single building designs, parametric sweeps, and cost-based optimizations. W. J. Cole et al. (2014) used BeOpt to model 60 community-scale homes. However, the obtained EnergyPlus model was computationally slow and a custom Matlab script had to be written to process the input and output data from the EnergyPlus model.

With the abundance of these user-friendly optimization tools, several studies have been conducted to test the most suitable algorithm or tool. Bichiou and Krarti (2011) developed a simulation environment to optimally select both the building envelope and HVAC system design and operation. Three optimization algorithms were used including a genetic algorithm (GA), a particle swarm optimization (PSO) algorithm and a sequential search algorithm (SS). A comparative analysis of these algorithms in terms of computational effort showed that GA outperformed SS by 70%. Moreover, the optimal selection of building parameters and the HVAC system can reduce life cycle costs by 10-25% depending on the geographical location and type of building. Alajmi and Wright (2014) effectively coupled EnergyPlus with GA and found out that small population sizes can be used to solve unconstrained optimization problems by around 250 buildings simulation calls.

As there are many user-friendly multi-objective optimization tools, efforts can be made toward the sustainable design of building parameters, with additional consideration for both HVAC system and occupant behavior.

SUSTAINABLE BUILDING DESIGN

Green rating systems are considered to be one of the most effective tools for improving performance of buildings as well as transforming market expectations and demand. These rating systems have also become a major business; generating significant revenues through the certification process. However, the main concern remains the ability of these systems to nurture and support culturally and climatically appropriate design practices(R. Cole and Valdebenito 2013). As such, three green certification schemes and their requirements are examined at the energy level to identify the green factors that highly affect building design parameters. The three schemes are: (1) Building Research Establishment’s Environmental Assessment Method (BREEAM), (2) High Environmental Quality (HQE), and (3) Leadership in Energy and Environmental Design (LEED). Needless to say, other schemes have been developed such as CASBEE for Japan, Green Globe for Canada, Green Star for Australia, and QSAS for Qatar.

BREEAM

BREEAM is the world’s first sustainability assessment method for master planning projects, infrastructure and buildings. It was published in the UK by the Building Research Establishment (BRE) in the 1990. To date, there

are more than 561,476 BREEAM certified developments, and 2,263,526 building registered for assessment. There are four technical standards that can be used depending on the type of project and 10 assessment categories. Among these categories, energy, health, and management have the highest percentage weight.

Given the scope of this paper, the interest lies in exploring BREEAM New Construction. At the energy level, Ene 01 – Reduction of energy use and carbon emissions is the credit that targets minimizing operations such as “Energy Demand” and “Primary Energy”. To achieve this credit, BREEAM offers two options: (1) Modelling the building energy demand using simulation tools to predict the heating and cooling demand and consumption, or (2) Using energy-efficient features for building services; the building envelope and the HVAC system.

HQE

HQE was developed by Association HQE (ASSHQE) in 1996 as the green building standard in France. Currently, HQE is a global certification program with more than 380,000 projects certified. There are three schemes for certification with 4 main categories of assessment; energy, environment, health, and comfort. These categories are divided over 14 different targets with target 4 being the one addressing energy.

For this review, the assessment scheme for the environmental performance of residential and non-residential buildings was considered. At the energy level, HQE distinguishes between “Energy Demand” and “Energy Consumption”. Reducing energy demand is considered a pre-requisite and is achieved by improving the building bioclimatic design – architectural aspect of the building. Energy consumption, on the other hand, is associated with heating, cooling, lighting and ventilation. To achieve this credit, a dynamic thermal simulation tool is needed to verify a required percentage of savings.

LEED

LEED was launched by the U.S. Green Building Council (USGBC) in 2000 as a certification program for buildings and communities. Since then, it has grown to become the most recognized green building rating system with over 37,300 certified commercial projects. Starting with a technical standard for new constructions, USGC expanded the rating system to include operations and maintenance, commercial interiors, core and shell, neighbourhood development, and homes. Similar to other certification schemes, LEED addresses 8 different categories. Out of these categories, Energy and Atmosphere (EA) and Indoor Environmental Quality (IEQ) are the most prominent in terms of contributing credits.

EA category approaches energy from a holistic perspective, addressing energy reduction and energy efficient design strategies. Reducing energy demand focuses on design issues such as building orientation, glazing selection and building materials. Whereas efficient strategies include passive heating and cooling, natural ventilation, and the use of efficient HVAC systems coupled with energy management control systems (EMCS). Additionally, a minimum energy performance is a prerequisite in EA. The intent is to reach an optimized building design that significantly reduces energy use. Two approaches are described to satisfy this prerequisite; (1) a prescriptive-based approach that includes a strict set of system choices and performance characteristics for simple buildings with typical energy systems, and (2) a performance-based approach that uses a



whole building energy modelling to evaluate the interactive effects of the efficiency measures.

COMPARATIVE ANALYSIS: MODELING ELEMENTS AND GREEN FACTORS

This section aims at presenting a comparative analysis of the three certification systems to help showcase the added value on the design process and the resulting energy performance of a certified building. It has been found that although these systems function differently, they all address similar environmental themes. Moreover, the three schemes recognize the use of energy calculation according to ASHRAE 90.1 Standard or a local equivalent. Turner and Frankel (2008) looked at 90 LEED certified buildings and compared their predicted modelling results that used ASHRAE 90.1 as a modelling baseline with the actual energy use of these buildings. Results showed that some buildings reported energy use that would not even comply with the code baseline while others consumed much more energy than predicted. Likewise, Roderick et al. (2009) verified that it is not possible to accurately predict the energy performance of a building based on an assessment scheme. The same building was tested given three different certification schemes and it received a high energy rating score in the Green Star scheme, a low energy rating score in the BREEAM scheme and failed to be certified in the LEED scheme. These discrepancies in predicting energy performance may be due to poor modeling skills or imprecise user input as explained by Zhou et al. (2014). However, there is a positive attribute associated with these schemes; their comprehensive approach, not just to energy, but to the holistic nature of the design, procurement, construction, management, and operation of the project. As such, out of these schemes, several green factors - parameters for assessing building energy - were determined and were found to overlap with the typical structure of the proposed energy model. Table 2 shows the modeling elements and the associated green factors. These factors can form the basis for future optimization techniques that couple a simulation engine with an optimization algorithm.

Table 2. Green factors used for optimizing system selection

Modelling Element Green Factor

Massing & Form Bio-climatic design

Building Function

Envelope Window to wall ratio

Window Characteristics

Material Characteristics

Internal Loads &Schedules

Building occupancy

Lighting Power density

Plug-Load density

HVAC Equipment System size & selection

System Control

Schedule of operation

With reference to Table 2, the envelope as a modelling element, takes window characteristics as a green factor. Characteristics such as the Solar Heat Gain Coefficient (SHGC) U-values and frame type are all part of the design

parameters that can affect the building configuration, in this case the HVAC system, and ultimately energy performance. For instance, data on the optical properties of a single glazed panel was extracted from ASHRAE (2013).Figure 1 shows a comparison of SHGC, visible Transmittance (Tv), and Solar Transmittance (Ts) for clear, bronze, green, grey, and blue-green single glass units. Clear glass has obviously the highest visible and solar transmittance. On the other hand, good visibility can be achieved using green tinted glass(Tv drops by around 13%) whereas solar transmittance is slightly affected (Ts decreases by 39%). It is important to note that the U-value does not change and a high thermal conductance is displayed when compared to a double glazed unit. Furthermore, SHGC, Tv, and, Ts are but a small part of the different green factors that are usually conveyed during the early design stages of a building. Once all these factors are established, the multi-objective optimization tools proceed into determining the optimal parametric configuration of the building and consequently the ideal design and control of the HVAC system.

Figure 1. Solar Optical Property Values for Single (Clear & Tinted) Glazing

CONCLUSION AND FUTURE WORKS

This paper focuses on the different sustainable design and energy simulation approaches targeted at reducing energy consumption in HVAC systems. Research on this subject for the past 15 years is summarized and discussed. Popular building energy modelling programs is presented and compared. Moreover, three different certification schemes and their requirements are examined at the energy level. The information gathered in this review is presented in a way to entice architects, engineers, and researchers to select suitable approaches to optimize both the parametric design of a building and the complementary HVAC system. It has been found that more efficient HVAC systems can be designed by coupling EnergyPlus as a simulation software with genetic optimization algorithms. Moreover, discrepancies among different BEMPs in predicting energy signal poor modelling skills and imprecise user input. As such, there is a need to synchronize the geometric exchange of a full building information model (BIM) into energy modelling programs in the form of parametric inputs. This is accomplished by developing a simulation environment that starts by: (1) optimizing the parametric design of a building, (2) importing the actual behavior of occupants, and (3)setting up a multi-objective optimization model to select theoptimal HVAC system.

ACKNOWLEDGEMENT The presented work has been supported by Munib and Angela Masri Institute of Energy and Natural Resources and AUB’s University Research Board (URB). The authors

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SHGC Tv Ts

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gratefully acknowledge both Masri Institute and URB support. Any opinions, findings, conclusions, and recommendations expressed by the authors in this paper do not necessarily reflect the views of the Masri Institute or URB.

REFERENCES

AIA. (2012). An Architect’s Guide to Integrating Energy

Modeling in the Design Process. Washington, DC.

Al-Homoud, M. S. (2005). Performance characteristics and practical applications of common building thermal insulation materials. Building and environment,

40(3), 353-366.

Alajmi, A., & Wright, J. (2014). Selecting the most efficient genetic algorithm sets in solving unconstrained building optimization problem. International Journal

of Sustainable Built Environment, 3(1), 18-26.

Asadi, E., da Silva, M. G., Antunes, C. H., & Dias, L. (2012). A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Building and environment,

56, 370-378.

ASHRAE. (2013). ASHRAE Handbook - Fundamentals. Atlanta: American Society of Heating Refrigerating and Air-Conditioning Engineers.

Bichiou, Y., & Krarti, M. (2011). Optimization of envelope and HVAC systems selection for residential buildings. Energy and Buildings, 43(12), 3373-3382.

Chinnayeluka, S. R. (2011). Performance assessment of

innovative framing systems through building

information modeling based energy simulation. (M.S. Thesis), Faculty of the Louisiana State University and Agricultural and Mechanical College.

CIBSE. (2006). CIBSE Guide A Environmental Design: CIBSE Publications.

Cole, R., & Valdebenito, M. (2013). The importation of building environmental certification systems: international usages of BREEAM and LEED. Building Research & Information, 41(6), 662-676.

Cole, W. J., Rhodes, J. D., Gorman, W., Perez, K. X., Webber, M. E., & Edgar, T. F. (2014). Community-scale residential air conditioning control for effective grid management. Applied Energy, 130, 428-436.

Crawley, D. B., Hand, J. W., Kummert, M., & Griffith, B. T. (2008). Contrasting the capabilities of building energy performance simulation programs. Building

and environment, 43(4), 661-673.

Hamdy, M., Hasan, A., & Siren, K. (2011). Applying a multi-objective optimization approach for design of low-emission cost-effective dwellings. Building and

environment, 46(1), 109-123.

Korolija, I., Marjanovic-Halburd, L., Zhang, Y., & Hanby, V. I. (2011). Influence of building parameters and HVAC systems coupling on building energy performance. Energy and Buildings, 43(6), 1247-1253.

Mwasha, A., Williams, R. G., & Iwaro, J. (2011). Modeling the performance of residential building envelope: The role of sustainable energy performance indicators. Energy and Buildings, 43(9), 2108-2117.

Norford, L. (2003). Genetic algorithms for optimization of building envelopes and the design and control of HVAC systems.

Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy

and Buildings, 40(3), 394-398.

Roderick, Y., McEwan, D., Wheatley, C., & Alonso, C. (2009). Comparison of energy performance

assessment between LEED, BREEAM and Green

Star. Paper presented at the Eleventh International IBPSA Conference.

Schwedler, M. (2017). Using Low-Load Chillers to Improve System Efficiency. ASHRAE Journal, 59(2).

Turner, C., & Frankel, M. (2008). Energy performance of LEED for new construction buildings. New Buildings

Institute, 4, 1-42.

Vakiloroaya, V., Samali, B., Fakhar, A., & Pishghadam, K. (2014). A review of different strategies for HVAC energy saving. Energy conversion and

management, 77, 738-754.

Zhou, X., Hong, T., & Yan, D. (2014). Comparison of HVAC

system modeling in EnergyPlus, DeST and DOE-

2.1 E. Paper presented at the Building Simulation.

Zhu, D., Hong, T., Yan, D., & Wang, C. (2013). A detailed

loads comparison of three building energy modeling

programs: EnergyPlus, DeST and DOE-2.1 E. Paper presented at the Building Simulation.


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Energy Modelling and Sustainability: A Review on Current ... · Accordingly, this is achieved through modelling the energy performance by varying a set of green factors such as thermal