Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India

Accepted Manuscript Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India Farheen Bano, Vandana Sehgal PII: DOI: Reference:

S2451-9049(18)30555-9 https://doi.org/10.1016/j.tsep.2018.11.004 TSEP 262

To appear in:

Thermal Science and Engineering Progress

Received Date: Revised Date: Accepted Date:

26 September 2018 7 November 2018 10 November 2018

Please cite this article as: F. Bano, V. Sehgal, Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India, Thermal Science and Engineering Progress (2018), doi: https://doi.org/10.1016/j.tsep.2018.11.004

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TITLE PAGE Paper Title: Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India Author: Farheen Bano, Assistant Professor, Faculty of Architecture, Dr. A.P.J. Abdul Kalam Technical University, Tagore Marg, Lucknow 226007 E-mail: [email protected] Co-Author: Dr. Vandana Sehgal, Professor, Faculty of Architecture, Dr. A.P.J. Abdul Kalam Technical University, Tagore Marg, Lucknow 226007 E-mail: [email protected]

Finding the gaps and methodology of passive features of building envelope optimization and its requirement for office buildings in India Abstract: Developing countries, such as India, face the challenge of increased energy consumption in buildings. One of the reasons for high energy consumption is the large glazed facades of buildings that cause overheating. The energy consumption problem can be solved by optimizing passive design features of the building envelope at an early design stage according to the climate of that building location. This study determined the gaps in the building envelope optimization of office buildings in India and its requirement. Moreover, it reviewed variables of the building envelope design, current trends for simulation and optimization methodologies, criteria for selection of simulation, and optimization tools. More than 100 studies were reviewed, particularly studies performed in India on related topics. The results are based on statistical charts generated using the reviews. The results include the methodology for the research and development of a base case model, selection of building design variables, and simulation and optimizations tools. Keywords: Building envelope optimization; Passive strategies; Calibration; Simulation tools; Optimization tools; Modeling 1. Introduction In 2009, the building sector in India accounted for one-third of the total electricity consumption in the country [1]. India has a population of more than one billion inhabitants, more than one-quarter of which are unable to meet their basic requirements [2]. India faces a tough challenge in meeting the energy requirements of its inhabitants in a sustainable manner and at a reasonable price. The reasons for high energy consumption include the overheating of buildings due to wrong orientation and a large glazing area with no shading devices. The increased Energy consumption can be solved by optimizing the building envelope at an early design stage according to the climate of the building location. The building envelope or skin comprises the outer elements of a building, including the foundation, walls, roof, windows, doors, and floors [3]. Building envelopes provide a thermal division between the indoor and outdoor environment and are crucial in determining how effectively a building can utilize natural lighting, ventilation, and heating and cooling resources. [4] Thus, the intelligent configuration and molding of a built form and its surroundings can considerably minimize the level of discomfort inside a building and reduce energy consumption required to maintain comfortable conditions. An intensive literature review is essential to any research. Therefore, various relevant studies were systematically reviewed in this research. This study aimed to investigate the envelope design variables (passive design features) influencing the energy consumption in office buildings, methodology for building envelope optimization (BEO), and criteria for tool selection by reviewing previous related studies and research. Tables 1, 2, and 3 present the detailed literature review of more than 100 BEO studies from 2001 to 2017. The tables include information regarding the thermal performance of the building envelope in different climate zones, methodologies used, and conclusions. The literature indicates that air conditioning and lighting are the main components of electrical energy consumption in high-rise office buildings in India. The airconditioning and lighting consumption can be reduced by incorporating climate-responsive passive features in the design [5]. A gap was observed in the relevant technical literature in the Indian context. 2. Methodology for BEO The energy consumption and thermal performance of the building must be estimated for designing an energyefficient building envelope. The thermal performance of buildings is based on the process of modeling the energy transfer between the building interface and the surroundings. [6]. Energy is consumed when heating or cooling the interior space of the building to make it thermally comfortable [7]. The thermal performance and energy loads of a building can be calculated using three methods, namely mathematical, experimental, and simulation methods [8, 9]. Mathematical models involve the numerical calculation of the heating and cooling of a building with respect to the outdoor air temperature multiplied by the corresponding number of hours [10, 11]. The task is not trivial in complicated situations, such as varying outdoor air temperature and heat gain. In the experimental method, complicated situations can be handled by creating live full-scale models to measure the thermal performance of buildings [12, 13]. The experimental method becomes unfeasible in case of numerous design variables. Kumar [7] executed their research in two parts. First, parametric simulation was performed on DesignBuilder for walling, roofing, and glazing assembly. Second, the optimal option among 300 options was used to develop a prototype of

a full-scale model. This methodology is unsuitable for large multistoried buildings and large sets of building design variables. Moreover, the experimental method becomes uneconomical and time-consuming.

Table 1: Literature review of studies related to BEO. Author(s) and year (reference)

Objectives

Carbonari et al. (2001) [14]

To determine the optimal building orientation in relation to the type of shading devices and their control logic for adjustable ones

Chungloo et al. (2001) [15]

Abaza (2002) [16]

Zhai (2003) [8]

Saridar (2004) [17]

Cheung et al. (2005) [18]

To reduce the overall thermal transfer value (OTTV) To suggest general design guidelines for improving the energy performance of buildings and enhancing thermal comfort Energy simulation and CFD coupling for energy efficiency and a thermally comfortable building To assess the daylighting performance of the office building façade configuration and plan morphology that contributes to the optimal use of daylighting

To reduce the cooling load

Climate and location

4A: Moderately continental (Venice, Italy)

Building type

Base case model

Methodolo Simulatio gy n tool

Conclusions

-

Energy consumption due to heating, cooling, and lighting.

Parametric simulation: One factor at a time (OFAT)

ENER_LU X

Calibrated using annual energy consumption

(3) Glazing materials, wall-towindow ratio (WWR), shading devices

Overall thermal transfer

Parametric simulation: OFAT

DOE-2

Maximum energy savings with low-E glass, 102 % saving with shading devices.

House was field monitored to calibrate building energy simulation.

(2) Thermal mass, insulation, solar heat gain coefficient (SHGC)

Energy consumption in heating and cooling

Parametric simulation: OFAT

EnergyPl us

Thermal insulation was the factor that most influenced building thermal performance. Direct night-time ventilation was the only ventilation cooling strategy.

Virtual: Oneoccupant office

-

(1) Wall assembly

Thermal comfort, indoor air quality, and energy analysis

Parametric simulation: OFAT

EnergyPl us

Developed an integrated simulation tool and method of coupling CFD and energy simulation.

Office

Real/virtual: Derived from 35 office building case studies.

Calibrated using annual energy consumption

(4) Building shape, facade configuration, office dimensions, WWR, and shading devices

Lighting energy, daylighting

Parametric simulation: OFAT

DOE-3

The compact building with atrium and linear building had superior daylight performance. Depth of a room should be limited to 14 m, and the WWR should be limited to 40%.

Residential

Real: The public rental flats were developed by the housing authority

Calibrated using the actual data of the building (within 8% per annum of

(6) Insulation, thermal mass, glazing type, window size, color of external wall, and

Annual cooling energy consumption and peak cooling load

Parametric simulation: OFAT

ENERGY -10

The strategies for improving the thermal performance of the external wall were more effective than those for windows.

Office and hospital

Real/virtual: Average of the audited and surveyed data of office and hospital buildings

4A: Humid subtropical (Blacksburg, Virginia, USA)

Residence

Real floor area of Beliveau House is approximately 580 m²

5A: Cool humid continental (Boston, USA)

Office

3A: Subtropical (Hong Kong, China)

Analysis criteria

(2) Shading devices and orientation

Office

2A: Hotsummer Mediterranea n (Beirut, Lebanon)

(No. of) Parameters considered

Orientation was east–west. Automatic control of the louvers’ tilt angle with a seasonal logic was in general the optimal configuration. The fixed louvers were marginally more convenient than using a seasonal logic for orientation between south and east.

Real: A room of 6 m × 5 m, 3.3 m higher than the existing office building

1A: Tropical (Thailand)

Calibration

Author(s) and year (reference)

Objectives

Climate and location

Building type

Base case model

Calibration published survey data)

Ibrahim and Zain-Ahmed (2006) [19]

Pan et al. (2008) [20]

To formulate a simple design tool for predicting the impact of wall envelope design options on the potential energy savings due to daylighting To yield energy cost savings by reducing the lighting and HVAC system loads

1A: Tropical (Malaysia)

Office

Virtual: Centralized core office block, 10 stories in height with a 25 m × 25 m footprint

3A: Humid subtropical (Shanghai, China)

Office (data center, 24 h occupancy)

Virtual: ASHRAE-90.12004-compliant budget model

Tuhus-Dubrow and Krarti (2010) [21]

To optimize the building shape and building envelope features

5B: Temperate (Boulder, CO, USA)

Da-Silva and Ssekulima (2011) [22]

To study the effect of building envelope designs and orientations on the energy consumption of buildings

1B: Tropical hot and dry (Nairobi, Kenya, and Kampala, Uganda)

Dhaka et (2011) [23]

To utilize energy conservation measures (ECMs) recommended by the National Energy Conservation Building Code (ECBC)

al.

Didwania and Mathur (2011) [24]

To optimize the WWR for various sizes and orientations

1A: Composite climate (Hyderabad, India)

1A: Composite (New Delhi, India)

Residence

Virtual: Building America Research Benchmark model

Institute

Real: Four-story institutional building in a selected city

Hostel

Real: Part of a building’s fourth floor, with a room size of 3.6 m × 2.4 m

Office

Virtual: G + 1 and other inputs as per the ECBC

(No. of) Parameters considered external shading devices

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

Parametric simulation: Multivariat e

IES-VE

The results were correlated with the key envelope design variables to establish a correlation equation, which was later used to formulate the design tool.

Parametric simulation: OFAT

EnergyPl us

Proposed design reduced the annual cost by 27%.

-

(2) WWR, glass type

Annual energy consumption and daylight aperture (DA)

-

(4) Wall and roof Ufactor, glazing type, and sunshades

Lighting system, HVAC system, and energy and cost savings

-

(6) Building shapes, wall, roof, and foundation type, window type, window area

-

(4) Wall and roof Ufactor, glazing type, and sunshades

The south-facing trapezoid was optimal for annual heating energy use, whereas the northfacing trapezoid was optimal for cooling. According to the energy and LCC, square is the optimal shape.

Climate impact, energy utility rate, and life cycle cost (LCC)

Parametric simulation: OFAT

DOE-2 and genetic algorithm

Energy consumption

Nonparame tric Simulation: Multivariat e

Ecotect

Energy costs can be reduced by 30% compared with those of a conventional building.

Calibrated using the actual data of the building

(4) Wall and roof Ufactor, glazing type and set point temperature

Energy efficiency and thermal comfort

Nonparame tric simulation: OFAT and multivariate

EnergyPl us

Recommendations of the ECBC for cool roof, roof insulation, wall insulation, glass U-value, and glass SHGC. The building becomes comfortable for an additional 313 h in a composite climate, 133 h in a hot and dry climate, and 192 h in warm and humid climate.

-

(2) WWR and glazing type

Energy consumption in heating, cooling, and lighting

Parametric simulation: Multivariat e

EnergyPl us/GenO pt

WWR% from high to low: N, E, W, and S. Glass type: Double low-E glass is preferable.

Author(s) and year (reference)

To optimize the LCC of various building envelope configurations To investigate how different types of roof construction, window glasses, and sunshield types affect the energy consumption

Kumar et al. (2011) [10]

Lai & Wang, (2011) [25]

Srinivasan et al. (2011) [26]

Manu et (2011) [27]

Dhaka et (2012) [28]

Gulati [29]

Objectives

al.

al.

(2012)

To minimize the heating and cooling loads and LCC

To link the ECBC prescriptive requirements with the EPI

To evaluate improvement in the energy efficiency of an air-conditioned building block by employing ECMs recommended by the Indian ECBC To optimize the building envelope for minimizing the heat gain and

Climate and location

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

1A: Composite (New Delhi, India)

Office

Real: Three floors

-

(1) Wall material

Heat transfer coefficient and LCC

Nonparame tric simulation: OFAT

Mathemat ical model

Use of ash blocks can enable energy-efficient operation.

Residence

Real: Four-story building

Calibrated using annual monthly energy consumption trends

(2) Shading device and window glazing

Annual electricity consumption

Parametric simulation: OFAT

eQUEST

By using a low-E glass and 1.5 m × 1.5 m box shading (e.g., balcony), annual electricity consumption savings of approximately 15.1% were achieved.

Office

Real: Existing office building at Narragansett

-

(3) Glazing, masonry wall insulation, and windows

Energy efficiency and LCC

Parametric simulation : OFAT

THERM, DOE-2/ CONRA D

Energy analysis indicated that insulation used in the masonry wall was the largest contributor to the building energy flow.

Office

Real: The business-as-usual derived from surveys, results published in various walk-through and investment-grade audits and industry practices

EnergyPl us

Overhang shading can be a very effective and simple strategy for reducing heat gain through the envelope. Additional in-depth analysis must be conducted on the quantifiable impact of insulation on energy consumption in commercial buildings in India.

Hostel

Real: Part of building’s fourth floor, room size of 3.6 m × 2.4 m

Nonparame tric simulation: OFAT and Multivariat e

DesignB uilder

By using adaptive thermostat settings, the energy consumption of small buildings can be reduced by 40% and up to 16% savings in energy can be achieved.

Parametric simulation: OFAT

eQuest

Percentage reduction in the heat load through the envelope was approximately 71%.

1A: Humid subtropical (Tainan, Taiwan) 5A: Temperate (Narragansett, RI, USA) 1B: Hot and dry (Jodhpur, India), 1A: warm and humid (Kolkata, India), 1A: composite (New Delhi, India), 3A: moderate (Bangalore, India), and 5A: cold (Guwahati, India) 1A: Composite (Hyderabad, India) 1A: Composite (New Delhi, India)

Residence

Virtual

-

Calibrated: MBE and CVRMSE

-

(6) Wall and roof Ufactor, glazing type and sunshades, lighting power density (LPD), and HVAC

(4) Wall and roof Ufactor, glazing type, and set point temperature

(6) Building orientation, WWR, roof, wall, and glass and shading devices

Building envelope thermal performance and lighting and HVAC system efficiency.

Energy efficiency

Energy efficiency

Parametric simulation: OFAT

Author(s) and year (reference)

Objectives

Climate and location

Building type

Base case model

Calibration

(No. of) Parameters considered

-

(4) Wall and roof insulation, infiltration rate, and glazing type

-

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

resultant energy demand Karaguzel and Lam (2012a) [30]

To minimize the heating load

4A: Humid continental/su btropical (Philadelphia, USA)

Karaguzel and Lam (2012b) [31]

To minimize the heating load

4A: Humid continental (Philadelphia, USA)

Dhaka et al. (2013) [32]

Goia et (2013) [33]

al.

Iwaro and Mwasha (2013) [34]

Tulsyan et al. (2013) [35]

To propose the required thermal comfort conditions and determine the energy savings potential by considering the adaptive thermostat control strategy To determine the optimal transparent percentage in a façade module for low-energy buildings To investigate the impacts of sustainable envelope design on building sustainability by using the integrated performance model To analyze the potential impact of the ECBC of India on six types of buildings in Jaipur

Energy efficiency

Parametric simulation: OFAT

EnergyPl us/ GenOpt

(4) Wall and roof insulation, infiltration rate, and glazing type

Energy efficiency

Parametric simulation: Multivariat e

EnergyPl us/GenO pt

(1) Thermostat setting

Energy savings and thermal comfort

Nonparame tric simulation: Multivariat e

eQuest

A majority of the occupants experienced thermal comfort at the proposed room temperature of 27.5°C and energy savings of 7.86%.

Parametric simulation: OFAT

EnergyPl us

Optimal WWR was in the range of 35%–45% irrespective of orientation.

Parametric simulation: Multivariat e

GraphiSo ft

The results indicated that the high energy efficiency performance of a building an envelope design alternative corresponds with high sustainable performance.

Nonparame tric simulation: Multivariat e

eQuest

Envelope measures exhibit maximum savings potential.

-

Virtual: Building 661

-

Virtual: Building 661

1A: Composite (Jaipur, India)

Office

Real: 5-star-rated five-story building

5C: Temperate oceanic (Frankfurt, Germany)

Office

Virtual: 3.6 m (w) × 5.4 m (l) × 2.7 m (h)

Sensitivity analysis

(3) WWR, orientation, and shading devices

Residence

Real: Housing development corporation, single-family units

Calibrated using the actual data of the building

(7) Roof, wall, windows, external doors, floor, ceiling, and envelope area

Energy, LCC, LCA, life cycle energy analysis, and multicriteria analysis (MCA)

(3) Wall and roof Ufactor and glazing type

Thermal performance of the building envelope and lighting and HVAC system performance

3C: Mediterranea n (San Fernando, CA, USA)

1A: Tropical composite (Jaipur, India)

Retail mall, private office, government office, hospital, hotel, and institute

Real: Case study buildings

Calibrated: MBE and CVRMSE

MBE and CVRMSE

Maximum savings of 30.5% in space heating and 22.2% in the WWR. R-30 for wall and roof insulation and double low-E glazing for the windows is recommended. 22% WWR, 0.1 infiltration, and 91.1% reduction in space heating. R-30 for wall and roof insulation and double low-E glazing for the windows is recommended.

Energy efficiency

Author(s) and year (reference)

Objectives

Climate and location

Velan (2013) [36]

To obtain an energyand-cost-optimal solution for an IT building

2A: Subtropical (Chennai, India)

Andarini (2014) [37]

To reduce the energy consumption in a high-rise office building by integrating the thermal building simulations during the design phase

Bandara and Attalage (2014) [38]

To optimize the building LCC through an efficient building envelope design

1A: Tropical (Colombo, Sri Lanka)

Charde et al. (2014) [13]

To analyze the effect of the designed static sunshade, brick cavity wall with brick projections, and both the static sunshade and brick cavity wall on the indoor air temperature

1A: Tropical composite (Pilani, Rajasthan, India)

Fang et al. (2014) [12]

To demonstrate that the use of an external wall insulation system can improve a building’s energy efficiency

1A: Tropical (Jakarta, Indonesia)

3A: Humid subtropical (Chongqing, China)

Building type

Office

Office

Office

Base case model Virtual-ASHRAE baseline case (Scenario 6)

Virtual: 22 floors with a total gross floor area of 20,000 m2

Real: 8 m × 12 m × 8.9 m threestory building

-

Real: Habitable room with a size of 3 m × 4 m × 3 m

Residence

Real: An “energyefficient” chamber was constructed using a thermal insulation system for the external wall, and a “basic” chamber was constructed according to the general design for residential

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

-

(4) Wall, roof, glazing type, and shading devices

Energy performance and operational cost reduction

Parametric simulation: OFAT

eQuest

The optimal solution for the façade includes an insulation on the external surface of the wall, 75 mm XPS above the roof, deep recessed windows, and a WWR of 30%.

(4) Wall U-value, SHGC, VLT, and infiltration

Thermal performance

Nonparame tric simulation: Multivariat e

DesignBu ilder

The results indicated that for a new office design, the annual energy consumption decreased by 43% through BEO and by using a high-efficiency HVAC system and high-efficiency office equipment.

(4) Orientation, WWR, position of opening, and overhang size

Energy performance, thermal comfort, environmental degradation, and LCC

Parametric simulation: OFAT

EnergyPl us/GenO pt

The optimal envelope design led to a 7.8% reduction in annual primary energy consumption and 7% saving in the LCC of the office building. The WWR was 9.4%, and the depth of the shading device was 1 m.

Indoor air temperature

Experiment al + Nonparame tric simulation: Multivariat e

Ecotect

The combined effect of building elements is useful for energy conservation in buildings located in a composite climate as per seasonal requirements.

-

The indoor thermal environment of an energyefficient chamber was affected to a limited extent by the outdoor environment and could be maintained at more comfortable conditions with lower energy consumption than in the basic chamber.

Sensitivity analysis

-

-

-

(2) Wall type and sunshades

(1) Wall insulation

Energy consumption and thermal comfort

Experiment al

Author(s) and year (reference)

Objectives

Climate and location

Calibration

(No. of) Parameters considered

Office

Virtual: Room of size 3 m (width) × 5 m (depth) × 3.5 m (height)

Sensitivity analysis

(4) Orientation, WWR, glass type, U-value of the opaque façade, and infiltration rate of the facade

Residence

Real: Two-story high-performance single-family house

The model was calibrated through trial and error by using a set of measured data

(5) Wall, roof, slab, window, and width of window

Building type

Base case model

Methodolo Simulatio gy n tool

Conclusions

Total energy consumption (TEC; heating, cooling, and lighting)

Parametric simulation: OFAT

EnergyPl us/GenO pt

The north-exposed facades exhibited lower energy savings potential with the use of adaptive glazing, particularly for facades with long response times, whereas savings up to 20% for monthly and 30% for daily adaptive facades may be achievable.

Energy and cost

Parametric simulation: OFAT and Multivariat e

TRNSY/ GenOpt/p article swarm optimizat ion

The optimal solution involves a light wooden envelope with triple glazing, large width of the south window, and no window on the roof.

Daysim, EnergyPl us

Low-E glazing provided the optimal performance, whereas double-layer glazing exhibited the worst performance. Window performance was superior in the east and west orientations, whereas the worst performance was observed in the north orientation. Overhangs performed better than blinds.

EnergyPl us/GenO pt

The results indicated that the annual total site energy consumption of the optimized building model was reduced by 33.3% with respect to the initial baseline case. The optimized envelope parameters can yield a 28.7% reduction in the LCC over a 25-year life span, with a

Analysis criteria

buildings of the 1980s and 1990s

Favoino et al. (2014) [39]

To present an inverse method for devising the ideal/optimal adaptive thermooptical performance of a glazed façade

4A: Humid continental (Helsinki, Finland), 4C: oceanic (London, United Kingdom), and 4B: Mediterranea n (Rome, Italy)

Ferrara et al. (2014) [40]

To investigate the possibilities provided by the use of simulation-based optimization methods within the context of application of the cost-optimal methodology

4A: Warm and temperate (RhôneAlpes, France)

Huang et al. (2014) [41]

To evaluate the performance of several popular energy-efficient window designs

1A: Tropical (Singapore), 3A: humid subtropical (Hong Kong, China), 1A: (Miami, USA), and 2A: humid subtropical (Houston, USA)

Karaguzel et al. (2014) [42]

To minimize LCCs for building materials and operational energy consumption in a reference commercial office building model

4A: Humid continental (Philadelphia, USA)

Office

Virtual: A 20floor high, 100 × 100 m² area, and 3.2-m-high office building model

Office

Virtual: Threestory commercial office building with a total conditioned floor area of 4982 m2

-

(4) Double-layer glazing, low-E glazing, interior blind, and overhang

-

(3) Thermal insulation thickness (cm) of the external walls and roofs and glazing unit types with varying Ufactor [W/(m2·K)], SHGC, and visible transmittance

Thermal and daylighting performance

Energy and LCC

Parametric simulation: OFAT

Parametric simulation: OFAT

Author(s) and year (reference)

Objectives

Climate and location

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions simple payback period of 4.2 years.

Kim et al. (2014) [43]

To study the performance of the windows and shading in office buildings

4A: Temperate (South Korea)

McKeen and Fung (2014) [44]

To examine the energy consumption under various aspect ratios

6A: Semicontinen tal cold (Toronto, Canada)

Perera and Sirimanna (2014) [11]

To minimize the thermal load of the building by optimizing the envelope

1A: Tropical wet and dry (Hambantota, Sri Lanka)

Sang et al. (2014) [45]

To minimize the cooling load

3A: Hothumid subtropical (Hong Kong, China)

Stazi et al. (2014) [46]

To determine the energy consumption, comfort levels, and environmental sustainability of adopting three energy-efficient envelopes

Yi (2014) [47]

To discuss the total ecological impact of a building, which encompasses the process energy and environmental cost

4A: Mediterranea n (Ancona, Italy)

4A: Temperate (South Korea)

-

(4) Orientation, WWR, Glazing type, and shading device

Heating and cooling load

Parametric simulation: OFAT

Comfen

The heating and cooling loads were efficiently decreased at the south façade.

Represent typical configurations of new and existing residential buildings; 10storey buildings with a gross floor area of 6000 m2

(1) Aspect ratio

Heating and cooling energy consumption

Parametric simulation: OFAT

eQUEST

The optimal aspect ratio for cooling load reduction is 1:2.

-

Virtual: A singlefloor building with an area of 1000 m²

-

(5) Aspect ratio of the building, WWR, building orientation, roof material, and wall material

Residence

Virtual: A 40floor building with a floor area of 462 ft²

Calibrated using the actual data of the building (6.6% higher than the predicted data)

(5) Wall insulation, overhang, wall color, WWR, and glass type

Office

Virtual: Unit space of 6 × 4.5 × 2.7 m3

Residence

Real/virtual: 10storey buildings with a gross floor area of 6000 m2 and aspect ratio of 1:1, representative of several newly constructed residential buildings

Residence

Real: Proposed north–southoriented fourstorey residential building

House

Real: Singlefamily house with an average size of 101.9 m2

-

-

(2) Wall and floor masonry, cement, and wood

(3) Window size, building orientation, and wall materials

Energy efficiency

Space cooling energy consumption

Mathematic al model

Evolution ary algorithm (GenOpt)

Heat load of the building increases linearly with an increase in the total window area.

Literature review and simulation: OFAT

eQUEST

The cooling load reduced by 46.81%. The SHGC is crucial in reducing the cooling load.

Comfort levels, heating/cooling usage patterns, natural ventilation usage pattern, and costeffectiveness

Parametric simulation: OFAT

Therm (thermal performa nce)/Ener gyPlus (comfort) SimaPro (LCA)

Thick insulations should be combined with high internal masses (masonry).

Energy and cost analysis

Nonparame tric simulation: Multivariat e (multizone)

eQuest and Ecotect

Energy synthesis results indicate that additional investment in indirect energy during construction is required to achieve the least operational energy scenario. Thus, the efficiency gains are likely to be

Author(s) and year (reference)

Balaji et al. (2015) [48]

Congedo et al. (2015) [49]

Echenagucia et al. (2015) [50]

Objectives

Climate and location

represented by the energy To investigate the influence of building envelope materials on indoor thermal comfort and their suitability for different climatic conditions

1A: Tropicalmoderate (Bangalore), composite (Jaipur), and warm-humid (Challaker, India)

To establish a costoptimal design for nearly zero-energy buildings (nZEBs)

4A: Mediterranea n (Lecce, Italy)

To optimize the building envelope for heating, cooling, and lighting energy

4B: Warm and temperate (Palermo, Italy), 4A: humid subtropical (Torino, Italy), 6A: Humid continental (Oslo, Norway), and 5C: temperate oceanic (Frankfurt, Germany)

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions overwhelmed by the increment in material flows.

Calibrated using the actual monthly data of the building (MBE, RMSE, and CVRMSE)

(1) Wall-type AAC, fly ash, cementstabilized soil block (CSSB), and tablemolded bricks

Annual energy consumption and thermal comfort

Parametric simulation: OFAT

DesignB uilder

AAC is recommended for a composite climate, fly ash and CSSB for a moderate climate, and CSSD for a warm and humid climate.

Thermal efficiency and cost

Parametric simulation: Multivariat e

ProCasaC lima2015 /Matlab

The cost-optimal solution involves using precast external walls (W2) with a high ecofriendly score and low cost and windows with wooden frames.

Heating, cooling, and lighting energy performance

Parametric simulation: OFAT

EnergyPl us, NSGAII, and Pareto front

The WWR at the south walls was 25%, whereas WWR range was narrower and close to 10% at the other walls.

Residence

Real: Multifloor house in selected cities

Office

Real: Four floors, SA/v = 0.47, rectangular, floorto-floor height of 2.7 m, and WWR of 12%

Sensitivity test

(5) Walls, frames, generations, HVAC system, and PV system

Open office

Real: First floor of a five-storey building with dimensions of 20 m × 14 m × 4 m

Calibrated using the actual monthly data of the building

(4) Number, position, shape, and type of windows and thickness of the masonry walls

Energy efficiency and thermal and visual comfort

Parametric simulation: Multivariat e

TRNSY/ GenOpt

The energy-optimal solution corresponds to a very insulated massive envelope. The windows are 0.2-m higher and 2.5-m wider, and no horizontal shading is observed, whereas, a 20-cm-wide vertical fin is observed on both right and left sides of the window.

Energy efficiency and visual comfort

Experiment al + parametric

DesignBu ilder

Insulation must be placed at the hotter façade (outside) of the building. The maximum

space

Ferrara (2015) [51]

To develop a replicable methodology for the optimization of the building envelope

4A: Humid subtropical (Turin, Italy)

Institute

Virtual: 7.5 m × 8 m school classrooms

-

(9)Wall and roof construction, window type, wall and roof insulation, reflection coefficient of the roof and window, height and width of the window, and depth of overhang

Kumar (2015) [7]

To develop green retrofit strategies and methodologies

1A: Tropical composite

-

Real: Live test model

-

(2) Walls, windows, and roof

Author(s) and year (reference)

Objectives

Climate and location

for an existing building

(IIT Roorkee, India)

Lin et al. (2015) [52]

To study the relation between the building shape coefficient and building energy consumption

Mayhoub and Labib (2015) [53]

To obtain an optimum balance between minimizing the heat gain and maximizing the daylighting through an economic approach

Pesenti et al. (2015) [54]

To optimize the shading system through visual comfort parameters

Raji et al. (2015) [55]

To obtain energysaving solutions for the envelope design of high-rise office buildings in temperate climates

3A: Humid subtropical (Shanghai, China)

1B: Hot arid (Cairo, Egypt)

4A: Humid subtropical (Milano, Italy)

5A: Temperate (The Netherlands)

Building type

Office

Office

Office

Office

Base case model

Real: 6- and 12floor buildings with a floor-tofloor height of 4 m (envelope taken from actual construction conditions of an energy-efficient building in Shanghai)

Real case studies

Calibration

-

-

Virtual: Mediumsize office room

-

Real: 21-storey building

Sensitivity analysis calibrated using the monthly energy consumption data of the building.

(No. of) Parameters considered

Analysis criteria

(1) Building shape

Heating, cooling, and lighting load

(1) Glazing type

Thermal and daylight performance through useful daylight illuminance (UDI) and mean radiant temperature

Ron Resch origami pattern shading system

UDI, daylight autonomy, and daylight glare probability, and total energy consumption (cooling, heating, and lighting per year)

(4) Glazing type, WWR, sun shading, and roof strategies

Heating, cooling, and lighting loads

Methodolo Simulatio gy n tool

Conclusions

simulation: OFAT

recommended WWR limit is 60%.

Parametric simulation: OFAT

DeST (DOE and ESPr)

To apply passive design technologies in Chinese areas with hot summers and cold winters, architects can select a large shape coefficient to achieve a high passive volume ratio for reducing the energy demand of the building.

Honeybee and Grasshop per

The optimal configuration, which combined clear glass and a light shelf, significantly improved the thermal comfort and daylight uniformity. The optimal configuration also marginally improved the availability of daylight.

Grasshop per– Honeybe e

By generating overlapped pleats and angle variations and using different materials, the direct light transmission inside the building can be altered and a certain degree of diffuse light component can be simultaneously maintained.

DesignBu ilder.

A double-glazed clear glass, WWR of 50%, operable blinds, electrochromic glazing, and 10cm green roof is recommended. The high-performance envelope design offers considerable energy savings of approximately 42% for total

Parametric simulation: OFAT

Parametric simulation: OFAT

Parametric simulation: OFAT

Author(s) and year (reference)

Objectives

Climate and location

Ascione et al. (2016) [56]

To determine the building envelope design variables that minimize winter and summer energy demand without compromising on thermal comfort

4A: Mediterranea n climate (Madrid, Nice, Naples, and Athens)

Chernousov and Chan (2016) [57]

To evaluate the advantages of PCMs

3A: Hothumid subtropical (Hong Kong, China)

Goia (2016) [58]

To determine the optimal WWR for each of the main orientations in four different locations

Gupta et al. (2016) [59]

To assess the impact of internal loads and the operational pattern of a mixedmode building

4A: Humid subtropical (Torino, Italy), 6A: humid continental (Oslo, Norway), 5C: oceanic (Frankfurt, Germany), and 4A: hotsummer Mediterranea n (Rome and Athens) 1A: Tropical composite (Roorkee, India)

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions energy use, 64% for heating, and 34% for electric lighting. Aerated concrete blocks or bricks with integrated insulation should be selected. Brickconcrete roof with external insulation is recommended. Optimal WWR changes greatlyon the selection of triple glazing windows with selective coating and external shading. PCM adoption of melting temperature of 25°C on the inner side allows a reduction of the cooling demand in each city. The PCM layer should be placed at the inner building envelope, that is, close to the office interior. A combined window and building envelope with PCMs can be an excellent alternative to fully-glazed curtain walls.

Residence

Virtual: Singlestorey building, with a rectangular shape and net conditioned building area of approximately 140 m2

-

(10) Thermal properties of the building envelope, adoption of phasechange materials (PCMs) with different melting temperatures, cool roof solutions, several WWR values, and some external and internal shading systems

Office

Virtual: Typical office building (5story section)

-

(1) Thermal mass using PCMs

Office

Virtual: Seven floors with an area of 45.9 m × 14.4 m and floor-tofloor height of 2.7 m

Defined in some other paper

(1) WWR

Heating, cooling and lighting loads

Parametric simulation: OFAT

EnergyPl us

The optimal WWR ranges from 30% to 45%. The optimal WWR for cold climate is 60% (south). The optimal WWR for warm climate is 20% (south).

Institute

Real: Two-floor building with an area of 1280 m2.

Calibrated using existing building data

(5) Volume, thermostat setting, window operation, lighting type, and lighting control

Energy consumption and thermal comfort

Parametric simulation: OFAT

EnergyPl us

The reduction of approximately 38% and 10% in the annual cooling and heating energy consumption, respectively. Annual lighting energy

Annual heating, cooling energy demand, and thermal comfort

Parametric simulation: OFAT

DesignBu ilder/Gen Opt (based on the NSGA-II algorithm )

Peak load and annual energy consumption

Parametric simulation: OFAT

EnergyPl us

Author(s) and year (reference)

Objectives

Climate and location

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions consumption was reduced by 72.4%.

Harmati et al. (2016) [60]

To obtain preferable and applicable solutions for energy performance improvement in current inefficient office buildings

Kirimtat et al. (2016) [61]

To demonstrate that architectural design problems are essentially realparameter multiobjective constrained optimization problems

4A: Mediterranea n (Izmir, Turkey)

Košir et al. (2016) [62]

To obtain an optimized building envelope configuration

4A: Humid continental (Ljubljana, Slovenia, and the United Kingdom)

Lau et al. (2016) [63]

To investigate the potential of three types of shading devices on 16 types of cooling energy savings when applied at different facade orientations

1A: Tropical (Kuala Lumpur, Malaysia)

Luca et al. (2016) [64]

To determine the most efficient window layout (horizontal or vertical) for shading device optimization

6A: Coldhumid continental (Tallinn, Estonia, and the United Kingdom),

4C: Marine (Novi Sad, Serbia)

Office

Office

-

Office

Office

Real: Existing office building

Virtual: Nine office rooms (7.3 m × 9.9 m)

Calibrated using the TEC and breakdown

-

(2) WWR, wall type, and glazing type

Annual heating and cooling demand

Parametric simulation: OFAT

EnergyPl us (open studio) Radiance

The WWR reduced to 30%. Further scope for research could be in window geometry, WWR with 5% steps and exterior wall insulation properties.

DIVA-for Rhino (radiance ), Ladybug + Honeybe e, and NSGA-II (Pareto front)

The optimal form of horizontal louvers was obtained using a computational optimizationbased approach. The design problem was formulated as a two-objective problem by maximizing the UDI and minimizing the TEC.

(3) Density, width, and angle of horizontal louver

Maximum daylighting metric (UDI) and minimum TEC

Parametric simulation: OFAT

Parametric simulation: Multivariat e

EnergyPl us

Virtual: Volume 10 m ×10 m × 10 m

-

(3) Building form orientation and WWR

Indoor air temperature and cooling and heating loads

Real: High-rise building

Calibrated using the actual monthly data of the building

(3) Orientation, shading devices, and glazing type

Annual building energy consumption and annual cooling 401 energy consumption

Parametric simulation: OFAT and multivariate

IES (VE)

-

(2) Shape of the windows and shading devices

Annual energy consumption and efficiency of the shading device

Parametric simulation: Multivariat e

Ladybug and Honeybe e

Virtual: Side lit (4 m × 6 m ×3 m)

Elongated building forms with large glazing portions in the longer façade and appropriate shading have a higher energy efficiency than compact buildings with small or medium glazing portions. Energy savings were between 5% and 9.9% when the shading devices were applied to all orientations. The energy savings increased to 5.6%– 10.4% when façade glazing was replaced by single clear glazing. Shading devices achieved cooling energy savings more efficiently than highperformance glazing. The horizontal window with the surround-type shading device was preferable in the south, east, and west quadrants, whereas the vertical layout was preferable in the north.

Author(s) and year (reference)

Objectives

Climate and location

Building type

Base case model

Calibration

(No. of) Parameters considered

-

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

(10) Window number, window length, window width, type of sunshade board, sunshade board length, window glass material, wall material, roof material, and glass curtain material

Thermal performance and cost saving

Parametric simulation: Multivariat e

TRNSY/ NSGA-II

The optimal office building envelope design model provided an acceptable building envelope design at 41% lower cost than the cost of the original design proposed by architects.

-

(1) Shading devices

The building’s thermal performance and user’s thermal comfort

Parametric simulation: OFAT

EnergyPl us

Horizontal fins were more efficient than vertical fins for all the analyzed solar orientations.

-

(5) Glazing, insulation (wall and roof), infiltration rate, and shading devices

Total annual energy demand of heating, cooling, and artificial lighting

Parametric simulation: OFAT

EnergyPl us/adapti ve radiation optimizat ion

Adaptive radiation can improve the efficiency of the simple genetic algorithm by considerably reducing the computation time.

(5) Wall and roof constructions, window systems, lighting fixtures, and daylighting control

Energy and life cycle cost analysis (LCCA)

Parametric simulation: Multivariat e

DesignBu ilder/NS GAII/Pareto font

The framework offers design solution packages by considering the first cost and operation costs for each solution.

DesignBu ilder

The optimal design for maximum energy savings included 15 cm of insulation for the walls and roof, a double low-E selective tinted glazing, and 100 cm of overhang shading.

4A: temperate (Milan, Italy), and 1B: hot arid (Cairo, Egypt)

Lin et al. (2016) [65]

Santos et al. (2016) [66]

Shan (2016) [67]

Xu et al. (2016) [68]

Al-Saadi and Al-Jabri (2017) [69]

To minimize the envelope construction cost and building envelope energy load (ENVLOAD)

To determine the efficiency of external shading devices throughout the year To obtain the optimal solutions of the facade design variables (such as infiltration, WWR, shading geometry, glazing types, and wall insulation) To optimize the first and operation costs of building designs with respect to the building envelope, electrical lighting, and HVAC system design To determine the optimal solution for the building envelope to save energy and cost

1A: Marine tropical (Taiwan)

2A: Humid subtropical climate (Sao Paulo, Brazil) 3C: Warmsummer Mediterranea n climate (San Francisco, California, USA) 4A: Humid continental (Pittsburgh, Pennsylvania, USA)

1A: Hot and humid (Muscat, Oman)

Office

Virtual: An office building located at an altitude of 30 m in Chiayi city, west Taiwan

-

Virtual: Clear sky described by ASHRAE (no specific Model)

Mid-rise office building

Real/virtual: Prototype of a typical mid-rise office building with five floors

Office

Real: Four floors, a penthouse floor, and a basement with a total floor area of 3700 m2

Residence

Real: Typical villa

Sensitivity analysis calibrated using real utility readings

(4) U-value, thermal mass, SHGC, and air infiltration

Thermal and cost analysis

Parametric simulation: OFAT

Author(s) and year (reference)

Objectives

Dino and Üçoluk (2017) [70]

To address problems related to building performance and integrate early design decisions regarding the building form, spatial layout, orientation, and envelope articulation

Kang et al. (2017) [71]

To evaluate the functional relations between the envelope design factors and the building heating/cooling load

4A: Temperate (K orea)

Lim, et al. (2017) [72]

To explore the application of BIM and GA to support design decision making and optimization for sustainable building designs

1A: Tropical (Malaysia)

Virtual: Fourstory building with windows at each orientation

Loukaidoua et al. (2017) [73]

To optimize the thermal features of the building envelope to achieve NZEBs

3A: Mediterranea n (Limassol, Nicosia, and the mountainous area of Saittas, Cyprus)

Maltais and Gosselin (2017) [74]

To evaluate daylighting performance

6A: Humid continental (Montreal, Canada)

Nevesa and Marquesb (2017) [75]

To determine the effect of envelope parameters on the

Climate and location

the

-

2A: Humid subtropical (Sao Paulo,

Building type

Base case model

Calibration

(No. of) Parameters considered

Analysis criteria

Methodolo Simulatio gy n tool

Conclusions

Parametric simulation: OFAT

OpenStu dio/Multi objective Architect ural Design Explorer (MADE)

The building form, layout, and envelope openings had a considerable effect on the energy and daylighting performance. The combined optimization of the aforementioned factors in early design processes can lead to well-informed decisions for achieving high performance. The SHGC, WWR, and WDI were significant design factors that affected the cooling load, whereas the WDI and IR were the influential factors for the heating load.

Fitness evaluation

(3) Special layout, orientation, and envelope articulation

Calibrated using the heating and cooling loads from the review results

(10) Floor area, floor aspect ratio, ceiling height, plenum height, window insulation (WDI), SHGC, WWR, IR, and orientation

Heating and cooling loads

Parametric simulation: AT

DOE, TRNSYS , multiobje ctive optimizat ion, and Pareto front

-

(3) Orientation, wall type, and window type

OTTV and construction cost

Parametric simulation: OFAT

Revit (BIM)/ GenOpt

A comprehensive integrated BIM-GA optimization tool can be developed for different design variables and objective functions.

-

Real/virtual: Average data of existing case study buildings and European standards

Calibrated using data of existing buildings

(5) Compactness ratio; thermal insulation on the wall, roof, and ground floor; and the optimal window properties

Thermal transmittance and LCC

Parametric simulation: Multivariat e

EnergyPlus

The thermal transmittance coefficient and SA/v ratio have a linear relationship with each other.

Office

Real: LEEDcertified five-story building with an area of 2083 m²

Sensitivity analysis

Annual glaring index and annual energy requirement for lighting

Parametric simulation: Multivariat e

Daysim/Pa reto front

Office

Real/virtual: Average data of 10 office buildings to

Calibrated using the heating and cooling load

Thermal and energy performance

Parametric simulation: OFAT

EnergyPl us

-

Virtual

Office

Virtual: Base case (BC) model reflecting the laws, guidelines, and latest technology trends

(6) WWR, sill, and VLT for all the four directions; overhang depth; orientation; and aspect ratio (4) U-value of the exterior walls, SHGC of windows, and exterior shading

Energy daylighting

and

The WWR was highest for the west façade and lowest for south. A sill close to the floor in the west and high VLT is recommended. The use of shading devices had the most positive effect in decreasing the cooling demand and consumption.

Author(s) and year (reference)

Objectives thermal loads and energy efficiency

Raji et al. (2017) [76]

Shandilya and Streicher (2017) [77]

Toutou and AbdElrahma (2017) [78]

To achieve energyefficient heating, cooling, lighting, and fans

To obtain an integrated passive design approach for reducing the heating and cooling demand by using an improved thermal envelope and high-efficiency windows To determine a suitable plane shape and building configuration for multiobjective optimization of the daylighting levels and energy consumption

Climate and location

Building type

Brazil)

5A: Temperate (Amsterdam, The Netherlands), 3A: subtemperate tropical (Sydney, Australia), and 1A: tropical (Singapore)

1A: Tropical composite (New Delhi, India)

1B: Arid desert (Cairo, Egypt)

Base case model represent the typical model of an office building in Sao Paulo

Office

Real/virtual: Floor area of 60,000 m² distributed over 40 stories

Residence

Real: One-floor single-family house constructed in 1980, with a floor area of 12 × 9 m2 and height of 3m

Office

Virtual: 12-story office building (40 m × 50 m)

Calibration from review results

Calibrated using the TEC and breakdown

-

-

(No. of) Parameters considered devices

Analysis criteria

(4) Plan shape, aspect ratio, orientation, and WWR

Annual energy consumption

(3) Wall, roof, and glazing type

Heating load, cooling load, net present value (NPV), and discounted pay back

(5) Plan shape, WWR, wall materials, glass materials, and shading devices

Daylight and energy consumption

Methodolo Simulatio gy n tool

Conclusions

Parametric simulation: OFAT

DesignBu ilder

For tropical climate, octagonal structures were the most efficient followed by square and rectangular structures. The optimal aspect ratio was 1:2, and the optimal WWR was 30%. The preferred window orientation was south followed by north.

Parametric simulation: Multivariat e

TRNSYS

The U-values of the wall, roof, and windows were 0.18, 0.20, and 1.69, respectively. The optimal U-values of the wall, roof, and windows were 0.26, 0.24, 1.69, respectively, with a payback of 3 years.

Parametric simulation: OFAT

Ladybug and Honeybe e; Pareto front for multiobje ctive optimizat ion

The optimal solution included a hollow cement wall, double low-E glass, and horizontal overhang of 400 mm.

Building envelope parameters causing energy consumption in a building include architectural features and building materials. The relation between the building envelope design and energy consumption data can be identified through iteration by changing the variables [15, 18]. Computer modeling and simulation have become increasingly significant for predicting the energy and environmental performance of buildings and systems that serve them. Computer simulation increases the speed of the prediction process, which enables the comparison of a broad range of design variants and provides an optimal design. By using reasonable physical assumptions and a calibrated simulation model, computer simulation provides more accurate and informative results than manual calculation [8]. Consequently, computer simulation provides a superior understanding of the consequences of design decisions and satisfies different requirements of complexity and accuracy. Therefore, simulation modeling is adopted to obtain the optimal design of the building envelope for office buildings in Lucknow. Parametric simulation refers to performing iterative simulation for a given range of values of an individual design variable, whereas simulation performed using only one value of a design variable is termed as nonparametric simulation [23]. Several studies have been conducted on the energy and thermal performance of the building envelope. Table 1 lists the significant studies in the literature and includes the year, author, objectives, climate and location, building type, base case (BC) model and calibration, number of parameters considered, analysis criteria, methodology adopted, simulation tool used, and significant conclusions. The graphics in figures 1–11 (except figure 8) were developed according to the literature survey presented in Table 1. The different studies in Table 1 have considered several parameters related to the building envelope for different climate types. Therefore, each study has different conclusions.

METHODOLOGY TYPE Experimental 2% Experimental +simulation 3% Mathematical 3%

Parametric 81% Simulation 92%

Both, 5% Multivariate, 23% OFAT, 100% OFAT, 64%

Non-parametric 11% 1

Figure 1. Percentage of different methodology types used in the reviewed literature.

2

Figure 1 indicates that most of the studies (64%) used parametric simulation by considering one factor at a time (OFAT). Therefore, simulation tools have been prevalently used in previous studies. Although the OFAT simulation method has been prevalently used, multivariate simulation has been performed in recent studies (from 2016 onwards) to determine the effect of one design variable on another. Mathematical and experimental modeling methods have been used in 3% and 2% of the studies, respectively, because of their limitations. The remaining (2%) reviewed studies have used both experimental modeling and simulation. The methodology derived for this research is broadly classified into five stages after reviewing the literature [44, 45, 73, 75, 76, 15]. The methods, tools, and techniques used in the five stages are discussed in Section 3. The five stages are as follows:     

Data collection: The primary and secondary data are identified and collected. The energy optimization factors for a green building, and the parameters and options for each factor are identified. BC generation: The BC derived from case studies, bylaws, and standards is modeled and calibrated. Modeling process: Scenarios are generated with possible combinations of factors and options. Each scenario is evaluated with respect to energy efficiency by using an energy modeling technique. BEO for energy and cost: The scenarios for optimal energy-efficient design and cost optimization are analyzed. The actual data are validated with the design results.

3. Building type Figure 2 illustrates that the maximum number of reviewed studies were performed on office buildings (53%) followed by residential (23%) and institutional buildings (6%). Office buildings have attracted substantial research

attention because of their considerable energy savings potential. Limited studies are available on hotel and hospital buildings. Figure 3 displays the publication timeline of the papers published in refereed journals, conference proceedings, and Doctor of Philosophy (PhD) theses. The number of BEO-related studies began increasing from 2010 and increased exponentially until 2017. Therefore, the trend indicates that simulation modeling for BEO has attracted considerable research attention. Researchers have begun to realize the benefits of using simulation modeling to optimize the building envelope for the given objectives. Yearly distribution of studies

Building type

Hospital 3%

14

None 14%

Institute 6% Residence 23%

12

Office 53%

Frequency

Hotel 1%

16

10 8 6 4 2 0 2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Publishing year

Figure 2. Building types used in the BEO studies. Figure 3. Yearly distribution of studies related to BEO. 4. Design variables of building envelope BEO pertaining to a set of performance criteria is a process that involves selecting optimal solutions from a set of available alternatives for a given building envelope design [79]. Optimization objectives may include minimizing the cost, energy, or environmental impact or maximizing the thermal comfort. Therefore, optimization involves determining the optimal solution with respect to the objective functions subjected to the constraints of the dependent variables. The variables in BEO studies are either energy related or economic related. Researchers have performed a sensitivity analysis to identify which input has the largest impact on an objective [33, 39, 49, 55, 74]. Another method involves referring to previous studies (Table 1) for identifying influential input variables. The influential variables are listed as follows:         

Building orientation [14, 29, 33, 38, 39, 43, 11, 47, 63]. Building geometry: Aspect ratio and compactness (SA/V) ratio [44, 11, 62, 71, 74, 76, 80]. Building envelope construction materials [8, 18, 20, 21, 22, 23, 26, 27, 30]. Geometric position and density of fenestrations [wall-to-window ratio (WWR)] [17, 19, 24, 29, 33, 36, 38, 39, 43]. Solar shading devices [14, 15, 17, 18, 25, 27, 51, 54, 56, 75]. Type, size, and schedule of the heating, ventilation, and air conditioning (HVAC) system [20, 68]. Energy storage management. (thermal storage with phase change materials) [56, 57]. Lighting schedules and interior lighting power density (LPD) [53, 54, 81]. Location and climate [22, 27, 48, 50, 58, 64, 73, 76].

Design variables set for optimization Location and climate Internal gains Energy storage management Type, size, and schedule of the HVAC system Solar shading devices Geometric position and density of fenestrations Building envelope construction- materials Building geometry Building orientation 0

5

10

15

20

25

Frequency

Figure. 4: Set of design variables for BEO.

30

35

40

Figure 4 indicates that the most prevalently optimized design variable was building envelope construction materials (53%) followed by shading devices (34%) and window configuration (30%). The building orientation and geometry were optimized by 25% of the researchers, and the location and climate type were optimized by 11% of the researchers. HVAC systems were optimized by 9% of the experts, and 5% of the researchers optimized the internal gains and thermal storage. Thermal storage has been achieved by the use of phase of phase change materials by the researchers. A phase change material (PCM) is commonly placed as a layer at a particular position in the building envelope to provide better efficacy in hot summer conditions [57]. It is substance, capable of storing and releasing large amounts of energy due to its high heat of fusion which, melt and solidify at a certain temperature [56]. Consequently, absorbs and releases thermal energy in order to maintain a regulated temperature. The significance of this cannot be ignored as it is one of the trending topic among researchers for BEO. The literature review indicates that the selection of the design variable was based on the innovation of the design project and complexity of a particular design variable. The highest number of variables used for BEO was 10. The average number of variables used in the reviewed studies was 3.7, with a standard deviation of 2.1. Therefore, scope exists to perform research by increasing the number of variables and their influence on each other. Sadineni et al. [82] reviewed the variations in the building envelope components in the wall, roof, and fenestrations (Table 2). Moreover, they reviewed the effect of the infiltration rate on the heating and cooling loads of the building. Lee and Tiong [83] found that building envelope has a significant impact on the initial and running costs of the building. The building envelope considerably affects the energy efficiency and indoor environment of the building. Lee and Tiong suggested that the economic benefits of energy efficiency should be determined. According to Jakob and Madlener [84], refurbishing or retrofitting a building envelope is costlier than developing a new efficient building envelope. Table 2. Literature review of studies on building envelope components. Author

Objective

Sadineni et al. (2011) [82]

To perform an exhaustive technical review of the building envelope components and respective improvements from an energy efficiency perspective To investigate how the building envelope can impact the sustainability of the building

Lee and Tiong (2007) [83]

Jakob and Madlener (2004) [84]

To analyze the technological progress and marginal cost developments for energy efficiency measures related to the building envelope

Building envelope components Walls (light-weight concrete, cavity wall, and insulated wall) Fenestration (glazing type and frames) Roofs (Masonry, lightweight, vaulted, domed, reflective, green roof, photovoltaic roof, insulated roof, and evaporative roof cooling) Infiltration rate

Conclusions

Remarks

Passive energy-efficiency strategies are highly sensitive to meteorological factors and therefore require a designer to have a broad understanding of the climatic factors. Building-energy-modeling computer codes play an important role in selecting the best energy efficiency options for a given location.

Components specific to climate must be investigated. Determine easy and cost-effective techniques.

The impact of the building envelope on the building sustainability is examined

The building envelope has a significant impact on the initial and running costs of the building. Moreover, the building envelope considerably affects the energy efficiency and indoor environment of the building. Refurbishing or retrofitting the building envelope is costlier than creating a new efficient building envelope.

Economic benefits of energy efficiency must be determined.

Wall insulation and glazing type

Planning an energy-efficient building envelope for a new building at an early design stage is advantageous.

5. Location and climate The comprehension of the combined effect of various elements on the energy consumption and thermal performance of a building in different climate zones is crucial. A few studies (Table 3.1) have reported the impact of the location and climate type on the thermal performance of the building envelope [22, 27, 50, 58, 64, 73]. Figure 5indicates that USA, India, and Italy are most recognized study locations followed by the United Kingdom, China, South Korea, Egypt, Germany, Canada, Netherlands, Taiwan, Singapore, Brazil, Greece, and Sri Lanka. The least selected locations are the UAE, France, Thailand, Lebanon, Turkey, and Indonesia.

Studies on Indian locations and tropical climate are preferred in the literature review to assess the gaps in BEO and its requirement. The Indian studies include published research in reputed journals and conference proceedings as well as PhD theses from the Indian Institute of Technology (IIT). Dhaka et al. [23], Tulsyan et al. [35], Kumar [7], and Shandilya and Streicher [77] performed simulation for building envelope construction measures by adding insulation to the wall and roof and using high-performance glass in windows. They observed that the envelope measures exhibited significant favorability for energy savings. Didwania and Mathur [24] studied the effect of the WWR and glass type on the heating, cooling, and lighting loads. Kumar et al. [10], Charde et al. [13], and Balaji et al. [48] analyzed the effect of different walling types on the energy consumption and thermal comfort of the building. Manu et al. [27] and Gulati [29] investigated the effect of shading devices and building envelope materials on energy efficiency. Thus, the combined effect of all the design variables (selected for simulation in the previous section) has not been considered in any previous study performed in India. Moreover, the window size and position have not been considered. Existing studies in India have only considered a limited number of cities for simulation. Therefore, the combined effect of the passive design variables related to the building envelope on the thermal performance can be determined for BEO in India. 16 14 12 10 8 6 4 2 0

USA India Italy UK China Malysia Korea Egypt Korea Germany Canada Netherlands Taiwan Singapore Brazil Greece Sri Lanka UAE France Thailand Lebanon Africa Turkey Indonesia

Frequency

Studied location

Locations Figure 5. Location distribution of the reviewed studies.

The majority of the reviewed studies were simulated for a tropical climate, followed by those for a subtropical, temperate, and Mediterranean climate (Koppen climate classification) [85]. Moreover, types 3 and 4 of the ASHRAE climate classification were studied most apart from type 1. [3]. In the aforementioned climates, energy is consumed for the heating and cooling of the building. However, the cooling load is high in tropical and dry climates (type 1 according to ASHRAE climate classification), and the heating load is high in continental climate (climate types 5 and 6 according to ASHRAE climate classification). India has a tropical and 1A climate, and thus, intensive studies related to BEO can be performed. Climate type studied - (ASHRAE climate classification)

Climate type studied - (Köppen climate classification) 30 Continental

25

Mediterranean

20

Frequency

Temperate Subtropical Dry (Hot-arid) Tropical (hot-humid)

15 10 5

0

5

10

15

Frequency

20

25

0 1A

1B

2A

3A

4A

4B

4C

5A

5B

5C

6A

Figure 6. Distribution of studies according to climate type. 6. Methodology for developing BC The BC is a generic reference building used in parametric computer simulation to determine the thermal effect of different parameters. The BC has three types, namely real, virtual, and both real and virtual (Table 1 and

Figure 7). Real BC buildings (47% of the reviewed studies used this method) are derived from real case studies of a particular building type, whereas virtual BC buildings (44% of the studies used this method) are derived from codes or standards or according to convenience. The combination of real and virtual BC buildings (6% of the studies used this method) is generated using the average of the audited and surveyed data (case studies specific to a building type).

Methods for generating base case (BC) Real/Virtual 9%

Real 47% Virtual 44%

Figure 7. Percentage-wise division of methods used in reviewed literature to generate BC for parametric simulation. Chungloo et al. [15] used the BC to generate a large number of databases by altering various parameters for understanding complexities of building energy performance. They developed a generic BC from an average of the audited and surveyed data of 10 office buildings in Thailand. Similarly, Nevesa and Marquesb [75] generated a BC from the average data of 10 office buildings situated in Sao Paulo to represent the typical model of an office building in that city. Furthermore, Saridar [17], McKeen et al. [44], Loukaidoua et al. [73], and Raji et al. [76] have used the aforementioned method to develop the BC model. Several studies, including Cheung et al. [18], Srinivasan et al. [26], and Lin et al. [65], have used the current building design as a BC rather than developing a generic design. Numerous studies, such as Huang et al. [41], Sang et al. [45], Perera et al. [11], Ferrara et al. [51], and Santos et al. [66], have not provided any particular reasons for the selection of their BC model. The BC developed by Kang et al. [71] reflects the laws, guidelines, and latest technology trends, whereas Karaguzel et al. [30] used a model prescribed by ASHRAE. The literature review indicates that the BC model can be generated from the average data of the buildings studied, standards, and bylaws of a particular location. Therefore, the BC model is a representative of current and future office buildings in a location. The review of various studies illustrates a stepwise procedure to calibrate the computer model of the building energy performance is as follows: Step 1: Preparation of the initial model in simulation software: The inputs of the BC model are decided after case studies of the conventional office buildings, codes, standards, and bylaws in a particular location. The initial model is created according to the aforementioned input parameters, with a standard core and perimeter zone strategy applied to each floor. The model is then simulated using software with a historical weather data file. Step 2: Calibration and validation: The measured average monthly and annual (usage is categorized as HVAC, lighting, and equipment loads) electrical consumption data is obtained for the case building and compared with the model output. If the model is not in an acceptable range (discussed later), it must be updated. Step 3: Updating the model: Calibration must be continued to improve the model, provided that the model satisfies the acceptance criteria defined for calibration. Therefore, the calibrated simulation model of the BC is obtained. 6.1 Preparation of initial BC model in simulation software The BC model can be generated using the average surveyed data of selected office buildings in a particular region. The BC model represents the prototype of current and future office buildings in a location. First, the selection criteria of the case studies must be defined, which can be done through the sampling method. At least one building should be selected from all strategic locations in the city. Second, a descriptive survey data sheet (Figure 8) must be prepared for data collection, and the researcher should personally perform or appoint someone to perform the survey [17]. Finally, the data must be analyzed and interpreted to formulate the BC model.

Figure 8. Descriptive survey data sheet for collecting data of selected conventional offices in a building [17]. 6.2. Calibration and validation of the BC model The calibration of simulation models is required regardless of the software being used to ensure that the energy simulation data are accurate and usable. The calibration process involves comparing the simulation results with the measured data and tuning of simulation until its results closely match the measured data [86]. Numerous researchers on building simulation have used the aforementioned method to calibrate their BC model. The researchers of the reviewed studies have employed one of the following three approaches for calibrating the BC model:  

Comparing monthly usage simulation predictions for the BC model with the monthly utility bill data [25, 32, 48, 50, 55]. Comparing the annual usage breakdown simulation predictions for the BC model with the data obtained from the utility bill and audit reports [60, 71, 75].

The statistical mean bias error (MBE), root mean squared error (RMSE), and coefficient of variation of the RMSE (CVRMSE) have been adopted in several studies to evaluate the error between the predicted and measured values [23, 48]. The aforementioned parameters can be calculated using the following equations [87, 88]: ∑𝑛

∑𝑛

RMSE =

(𝑄 𝑝𝑟𝑒𝑑, 𝑖 ‒ 𝑄 𝑚𝑒𝑎𝑠, 𝑖))

𝑖=0

MBE month =

𝑛 𝑄𝑚𝑒𝑎𝑠. 𝑎𝑣𝑔 (𝑄 𝑝𝑟𝑒𝑑, 𝑖

𝑖=0

𝑛

‒

𝑄 𝑚𝑒𝑎𝑠, 𝑖)2

Equation 1 Equation 2

CVRMSE = ∑𝑛

Q meas.ave = where

[

𝑅𝑀𝑆𝐸

]

Equation 3

𝑄𝑚𝑒𝑎𝑠. 𝑎𝑣𝑔 𝑄 𝑚𝑒𝑎𝑠, 𝑖

𝑖=0

Equation 4

𝑛

𝑄𝑝𝑟𝑒𝑑,𝑖

: predicted (simulated) electricity or fuel consumption for the ith month

𝑄𝑚𝑒𝑎𝑠,𝑖

: measured electricity or fuel consumption for the ith month

𝑄𝑚𝑒𝑎𝑠. 𝑎𝑣𝑔

: measured average during the month

The acceptable limits for the calibration of the measured data are ±5% MBE and ≤ 15% CVRMSE [89, 86]. 6.3. Upgradation of BC model If statistical indices calculated in the previous step indicate that the model is insufficiently calibrated, the model inputs must be revised, the model should be run, and its prediction results must be again compared with the measured data. Low MBE and CVRMSE correspond with superior calibration [87]. The determination of input parameters that can be changed to calibrate the model is essential. The input parameters (mostly uncertain factors) include lighting, occupancy, equipment and HVAC schedules, changes in the thickness of the walls and roof, LPD, equipment load, coefficient of performance of the HVAC system, and infiltration rate [90]. Moreover, the annual energy end-use by the lighting, office equipment, and HVAC system are used to calibrate the BC model [60]. The building envelope design variables obtained earlier are then altered and individually simulated using the calibrated BC model. 7. Simulation tool and methodology BEO is coupled to the building performance simulation tool and optimization engine. Numerous energy simulation tools have an ability (to different extents) to model thermal and daylight performance in buildings. Selection criteria are required to select an appropriate tool. The criteria are based on the previous studies reviewed in Tables 1 and 3. Figure 9 illustrates that EnergyPlus is the most widely used simulation tool (34%) followed by DesignBuilder (13%). EnergyPlus is the oldest among all software displayed in Figure 9. DesignBuilder uses EnergyPlus as an energy simulation engine and radiance for daylighting. In EnergyPlus, inputs and writes output are text files, whereas DesignBuilder provides a user-friendly graphical interface and generates output in the form of both graphs and text [91]. Doe, eQuest, and TRNSYS were used in 13%, 9%, and 9% of the reviewed studies, respectively. The use of Ladybug + Honeybee, which is a plugin of Grasshopper–Rhino, was observed in studies conducted after 2015. Furthermore, Ecotect, IES (VE), and Daysim were used as simulation software in some studies.

Simulation tools used in the studies Daysim 3% Others 11%

IES(VE) 3% Ecotect 4%

EnergyPlus 34%

Honeybee 7% Trnsys 7% eQuest 9%

Doe 9%

DesignBuilder 13%

Figure 9. Simulation tools used in the reviewed studies. Crawley et al. [92] recommended EnergyPlus as the optimal software among 20 selected tools for evaluating the building envelope performance and economic benefits. Rallapalli [93] recommended EnergyPlus because it aids

in modeling complex systems and producing accurate results. EnergyPlus was the most widely used software among the 16 simulation tools reviewed by Kirimtat et al. [81]. Nguyen et al. [94] and Attia et al. [79] have reviewed simulation tools, optimization algorithms, and multi-objective optimization and are in favor of using EnergyPlus coupled with GenOpt for BEO. Table 3. Review of literature on simulation software. Author. Year

Objective

Target

Simulation tools

Optimization tool

Remarks

-

Multiobjective optimiza tion tool -

Crawley et al. (2008) [92]

To compare the features and capabilities of 20 major building energy simulation programs

Whole building performanc e

Rallapalli, (2010) [93]

To investigate the potential of both the programs (simulation and optimization) for performing the whole building energy analysis

Whole building performanc e

BLAST. BSim, DeST, DOE-2.1E, ECOTECT, Ener-Win, Energy Express, Energy-10, EnergyPlus, eQUEST, ESP-r, IDA ICE, IES , HAP, HEED, PowerDomus, SUNREL, Tas, TRACE, and TRNSYS eQUEST and EnergyPlus

-

-

GenOpt: 40%, particle swarm: 13%, hybrid: 10%, linear programming: 5% Hook–Jeeves: 5%, evolutionary: 4%, simulated annealing: 2%, ant colony: 2%, branch and bound: 2%, other direct search: 2%, and others: 13% Matlab: 36%, GenOpt: 24%, Optplus: 6%, own package: 6%, BE opt: 6%, ECI: 6%, and others: 8%

Pareto optimalit y

eQUEST is easy to use and produces results quickly. EnergyPlus aids in modeling complex systems and producing accurate results; however, the program is timeconsuming. EnergyPlus and TRNSYS are the most widely used building simulation programs in optimization studies. The most widely used optimization engines appear to be GenOpt and Pareto optimality for multiobjective optimization.

Nguyen et al. (2011 ) [94]

To evaluate the performance and selection of simulation tools, optimization algorithms, and multiobjective optimization

Whole building performanc e

EnergyPlus: 37.2%, Transys: 35.3%, Doe-2: 10%, ESP-r: 5.6%, eQuest: 2.7%, Ecotect: 2.7%, and others: 6.5%

Attia et al. (2013) [79]

To outlines major criteria for optimization tools selection and evaluation

Whole building performanc e

EnergyPlus: 30%, IDA ICE: 25%, Transys: 20%, Doe-2: 10 %, ESP-r: 4%, Simulink: 3%, and others: 8%

-

Energy plus is the best software for evaluating the building envelope performance and economic benefits.

GenOpt and Matlab coupled with Energy plus are recommended.

Kirimtat et al. (2016) [81]

To underline the importance of simulation modeling for the shading devices in buildings

Shading devices

EnergyPlus, Radiance, DOE-2, IES-VE, TAS, Transys, ESP-r, Lightscape, and DesignBuilder

-

-

EnergyPlus is the most widely used software among 16 simulation tools.

8. Optimization objectives Optimization can either be a single-objective or multi-objective problem depending on the number of objective functions that define a thesis problem. In single-objective functions, the optimization problems are generally defined as minimizations of a quantity, such as energy usage, cost, and environmental impact. If an optimization problem involves the maximization of an objective function (such as thermal comfort), then minimizing its opposite function (discomfort hours) is convenient. In real settings, more than one objective function must be simultaneously satisfied. Therefore, optimization becomes a multi-objective problem. To optimize a multiobjective problem, we must determine the values of the variants that satisfy all the objective functions defined in the optimization problem [95]. Figure 10 indicates that the researchers of 61 reviewed studies selected energy as the optimization objective, whereas the researchers of 23 studies (33%) used cost as the optimization objective. After energy and cost, daylighting (13) and thermal comfort (12) were the most prevalently used objectives. Recently, environmental impact (1) and indoor air quality (3) have also been used as optimization objectives. Among the reviewed studies, 66% performed multi-objective optimization, whereas 34% performed single-objective optimization. In this Objectives forasoptimization problems study, energy and cost were thoroughly discussed optimization objectives. Environmental Impact Indoor air quality Comfort Daylighting Cost Energy 0

10

20

30

40

50

60

70

Frequency

Figure 10. Optimization objectives used in BEO. 8.1. Energy efficiency Office buildings in Lucknow utilize energy in the form of electricity for mechanical and electrical systems, such as HVAC and lighting systems [96]. Therefore, the energy-usage pattern was examined in terms of the HVAC, lighting, and equipment loads of the buildings. The electricity usage of the selected office buildings was calculated in terms of the energy performance index (EPI), which is prevalently used for comparing the energy consumption of buildings. The EPI (measured in kWh/m²/year) is the annual energy consumption of a building in kilowatthours divided by the gross floor area of the building in square meters (Eq. 3.6) [97]. EPI =

Total energy consumed in a year (kWh) Total floor area of the building (m2)

Equation 5

The energy consumptions of different BCs and actual buildings were compared. The relative energy consumption (REC) for different profiles was determined by dividing the energy consumption in a BC by the energy

consumption in an actual case (Eq. 3.7). A positive REC value indicates energy savings, whereas a negative REC value indicates increased energy usage [98].

REC% = 1 ‒

𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑎𝑓𝑡𝑒𝑟 𝑚𝑜𝑑𝑖𝑓𝑖𝑐𝑎𝑡𝑖𝑜𝑛 𝑖𝑛 the 𝑒𝑛𝑣𝑒𝑙𝑜𝑝𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛 𝑜𝑓 the 𝐵𝐶 𝑚𝑜𝑑𝑒𝑙

Equation 6

8.2. Economic benefits (cost) Designers sometimes are in a dilemma because an energy-efficient design saves the client’s money by reducing electricity consumption; however, it increases the building cost. The question is whether the additional cost of construction is worth the savings in energy consumption. Numerous methods are available for determining whether a particular investment is lucrative [99], some of which are listed as follows:     

Simple payback period (SPP) Discounted payback period (DPP) Simple rate of return on interest (SRRI) Discounted rate of return on investment (DRRI) Net present value (NPV)

The incremental cost of energy-efficient buildings includes the one-time increased construction costs, maintenance costs, and interest. 8.2.1. Simple payback period The SPP is calculated by dividing the additional construction costs by the fuel savings expected at the end of the first year of operation [99]. SPP = C0 ÷ B0 (years) where

C0 B0

Equation 7

= additional construction cost = fuel savings expected at the end of the first year of operation

Eq. 7 can be used to determine the cost-effectiveness of strategies. The disadvantage of using the SPP is that it ignores all future costs and benefits that the owner may incur after the first year of operation. 8.2.2. Discounted payback period The DPP refers to the number of years (k) required for satisfying the following conditions [99]: 𝑘

𝐵𝑡_𝐶𝑡

C0 = ∑𝑡 = 1(1 ‒ 𝑟

𝑑)

𝑡

Equation 8

Bt = (1 + 𝑟𝑒)𝑡 x B0

Equation 9

Ct = (1 + 𝑟𝑒)𝑡 x C0

Equation 10

where

t = time (years) C0 = incremental construction costs of the design being analyzed in the base year (Rs) Ct = incremental cost (maintenance costs) in year t (Rs/year) Bt = incremental benefits (fuel saving) in year t (Rs/year) K = year in which payback occurs rd = discount rate B0 = base-year fuel benefit re = escalation rate The discounted rate is the return on investment typically yielded by an investor or required from their normal investment opportunities. The escalation rate is the annual compounded rate of the increasing price. 8.2.3. Simple rate of return on investment The SRRI is calculated by dividing the fuel savings in the first year of operation by the incremental construction cost associated with a particular design strategy [99].

SRRI = B0/C0 × 100 (percentage/year) 8.2.4. Discounted rate of return on investment The DRRI is the discount rate that satisfies the following condition [99]: ∑𝑛

𝐵𝑡_𝐶𝑡

𝑡 = 1(1 ‒ 𝑟𝑑)𝑡

where

=0

Equation 11

n = number of years of useful life of the asset generating the cash flow

8.2.5. Net Present Value An investment is lucrative if the net value of all the generated cash inflows and outflows is higher than 0, where no annual costs are associated with the design [99]. ∑𝑛

𝐵𝑡_𝐶𝑡

𝑡 = 1(1 ‒ 𝑟𝑑)𝑡

>0

Equation 12

The NPV method is the most rigorous technique to determine the cost-effectiveness under any conditions of cash flow. Moreover, this method is considered most reliable for decision-making [100, 77]. However, the NPV method is least understood by the general public. The DPP method appears to be most appropriate for evaluating the economic benefits and determining the costeffective BEO technique because it incorporates incremental cost (electricity tariff hike and maintenance cost) with the discounted rate. Moreover, the DPP method is easily understood by the general public. The discount rate is generally considered 6% [99, 77]. The maintenance cost varies for different materials. Therefore, the average maintenance cost is considered 2% for sturdy materials and 4% for flimsy materials [101]. Furthermore, the electricity tariff increases by an annual average of 9% in India [96]. 9. Optimization tools Figure 10, 10 researchers used GenOpt, five used the non-dominated sorting genetic algorithm (NSGA-II), and one each used the Matlab and particle swarm optimization tools for single-objective optimization. Attia et al. [79] and Nguyen et al. have recommended GenOpt coupled with the EnegyPlus simulation tool for single-objective optimization (Figure 11). DesignBuilder V5 uses EnergyPlus as a simulation engine and includes an optimization tab, and therefore, this is recommended. The Pareto front optimization tool has been used by researchers for multiobjective optimization [50, 71, 78]. Therefore, Pareto front optimization has been used to design a building Optimization engines used in studies envelope optimized for both energy and cost. Matlab Particle swarm optimization others NSGA-II Genopt 0

2

4

6

8

10

12

Frequency

Figure 11. Optimization tool used in the reviewed studies for single-objective optimization. 10. Building envelope optimization methodology After reviewing the literature in Table 3, a detailed methodology is recommended for optimizing the building envelope of office buildings in India (Figure 12). The methodology attempts to cover the identified gaps in multiobjective (energy and cost) optimization and its requirement.

Figure 12. Methodology recommended for optimizing the building envelope of office buildings in India.

11. Conclusions Considerable research has been conducted for investigating different passive design measures to improve the thermal performance of the building envelope. Established standards and codes are available in several countries, including India. The requirement of today is research and awareness at a local level. No single standard is suitable for all the climatic zones of India and various building types. This research provides a methodology to optimize the building envelope design of office buildings in different regions of India. Moreover, this paper provides an overview of the research developments regarding the methods and tools adopted for BEO. The available related literature from 2001 to 2017 was reviewed to highlight the requirement of BEO for minimizing energy consumption, incremental cost, and environmental impact and maximizing the thermal comfort and indoor air quality. The conclusions from the literature review are as follows:      

The number of studies on BEO have exponentially increased from 2010. Walling, roofing, and glazing construction material are the most prevalently used variables for BEO. EnergyPlus and GenOpt are the most prevalently used simulation and optimization tools, respectively. The USA is the most recognized location for BEO studies, and office buildings are the most commonly used building type. The BC model derived from the average of the data surveyed from the selected case studies is calibrated using statistical measures, such as MBE and CVRMSE. Energy and cost are the most commonly used objective for multi-objective optimization.

In future studies, the function of the building envelope with occupant behavior and dispositions should be considered one of the optimization variables. This variable has a significant effect on the energy consumption. Therefore, the model simulated and optimized at an early design stage will later provide user satisfaction. Moreover, renewable energy techniques (like solar panels, or green roofs, or wind turbines, etc.) in combination with passive techniques can be used to optimize building envelope of office buildings to achieve net/nearly zero energy building in India. 12. References [1]

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