Balancing the impacts of energy efficiency strategies on comfort quality of interior places: Application of optimization algorithms in domestic housing

Journal of Building Engineering 29 (2020) 101174

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Balancing the impacts of energy efficiency strategies on comfort quality of interior places: Application of optimization algorithms in domestic housing Reihaneh Aghamolaei a, b, *, Mohammad Reza Ghaani c a

School of Mechanical and Materials Engineering, UCD Energy Institute, University College Dublin, Belfield, Dublin 4, Ireland College of Fine Arts, University of Tehran, Tehran, Iran c School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland b

A R T I C L E I N F O

A B S T R A C T

Keywords: Retrofit Thermal comfort Optimization Genetic algorithms Residential buildings

Building retrofitting improves energy performance, however, its interventions affect occupants’ life qualities such as thermal comfort. Although various retrofit scenarios are available, determination of the most optimized sets of retrofit actions dealing with competing objectives of energy consumption and thermal comfort is still a time-consuming challenge. This paper presents a novel methodology for facilitating the optimization process and reducing the required number of calculations by combining the Parametric Sensitivity Analysis (PSA) into this optimization process. As a result of PSA, a minimum set of accurately defined input data will be used in the optimization calculations to achieve two objectives: provision of indoor thermal comfort while not increasing the environmental impacts such as Green House Gases emissions (GHG). The process which is conducted for a validated model with field measurements includes (1) preliminary energy performance assessment of the dwelling archetype (2) proposing retrofit measures (3) PSA for retrofit actions to determine the most efficient ones (4) multi-objective optimization. The PSA results are used to prioritise two main categories of retrofit ac­ tions. The more important category is entered to the optimization process to simultaneously minimise the GHG emissions and interior thermal discomfort. Results indicate that AL, EWI, AR, and RI are the most effective variables and EWI has the most significant impact on reducing energy consumption. On the other hand, the other four parameters of OS, TS, WR, and AL have less impact on energy performance. As such, combining PSA at the early stages of the optimization algorithm assist to facilitate the optimization process. The optimal scenarios of this pilot study provide a useful methodology for decision-makers to handle multi-objective retrofit projects while controlling the possible side effects.

1. Introduction Cities account for approximately 75% of the world’s energy con­ sumption and 80% of Greenhouse Gases (GHG) emissions respectively, even though they occupy only 2% of the total world’s surface [1]. To reduce the overall energy consumption and thereby GHG emission, there is a growing trend for improving energy performance by retrofitting and renovating actions [2,3]. Since the building sector accounts for a noticeable part of the overall energy consumption [4], many studies evaluate the environmental impacts of existing buildings [5]. Retrofit measures have been widely used as a cost-effective approach to reducing building energy consumption and GHG emissions [6,7]. The available retrofit technologies differ by building characteristics, project target, budget plan, regulations and occupancy pattern [3,8].

Although retrofitting has a significant effect on energy saving and GHG emission, its intervention can affect the other metrics of life quality such as indoor air quality, thermal comfort, health and consequently the well-being of occupants [9,10]. Nowadays more people are spending their time inside, for instance, European people spend 60–90% of their time in interior spaces and 16% of whom live in damp and unhealthy buildings [1]. Such conditions nearly double the risk of asthma, treat­ ment for which costs 82 billion Euro across Europe each year [1]. In cases where poor comfort conditions occur, occupants tend to suffer from discomfort, health problems, sick building syndrome, and cogni­ tive degradation with repercussion on social, and management costs [2–4]. With the urgent need to reduce the economic and environmental cost of energy consumption [12], investigating the side effects of retrofit action such as thermal comfort has attracted significant attention.

* Corresponding author.candidate School of Urban planning College of Fine arts University of Tehran, Tehran, P.O.Box: 14155-6619, Iran. E-mail addresses: [email protected], [email protected] (R. Aghamolaei). https://doi.org/10.1016/j.jobe.2020.101174 Received 21 August 2019; Received in revised form 1 December 2019; Accepted 4 January 2020 Available online 8 January 2020 2352-7102/© 2020 Elsevier Ltd. All rights reserved.

R. Aghamolaei and M.R. Ghaani

Journal of Building Engineering 29 (2020) 101174

2. Background and overview

Nomenclature

In this section, the key literature is reviewed in three sections of thermal comfort, retrofit scenarios and retrofit analysis which is pre­ sented as follows.

Parametric Sensitivity Analysis PSA Green House Gases GHG Genetic Algorithms GA Design of Experiments DOE Non-Dominated Sorting Genetic Algorithm II NSGA-II External Wall Insulation EWI Window Replacement WR Domestic Hot Water DHW Floor Insulation FI Roof Insulation RI Temperature Set Points TS Artificial Lighting AL Operation Schedule OS Airtightness AR

2.1. Thermal comfort Thermal comfort is a condition in mind in which satisfaction is expressed with the thermal environment [19]. Parameters such as air temperature, air velocity, relative humidity, mean radiant temperature, clothing insulation and activity level control thermal comfort [20]. Sustainability rating systems such as BREEAM (Building Research Establishment Environmental Assessment Method) have considered thermal comfort as necessary criteria toward achieving a sustainable environment [21]. LEED (Leadership in Energy and Environmental Design) defines thermal comfort as a necessary measure for productiv­ ity, comfort, and well-being of occupants [22]. Various standards and measures have been appeared in recent years dealing with thermal comfort concept. Fanger (1970) as one the pio­ neers of this field developed PMV (Predicted Mean Vote) index for assessment of interior thermal comfort [23]. PPD (Predicted Percentage of Dissatisfied) was used to calculate the level of predicted dissatisfac­ tion among the occupants. ASHRAE Standard 55–2010 uses PMV-PMD model to set the requirements for indoor thermal conditions [19]. The PMV ranges from -3 to þ3 representing too cold environment to too warm environment and the comfort zone is based on the PMV values between -0.5 and þ 0.5 [19]. PMV-PPD model determines the level of thermal comfort based on linear regression analysis [9,12]. LEED asserts that meeting the requirements of ASHRAE standard 55–2010 or both ISO 7730:2005 and (European Committee for Stan­ dardization) CEN is adequate for the qualification of thermal comfort design [22]. Standards define temperature and humidity range for thermal satisfaction of at least 80% of occupants in a space [24]. The discomfort hours index demonstrates the summation of hours a year when one or more zones in the building fail the ASHRAE 55 comfort criteria [25].

Thermal comfort of space users contributes to healthy and productive interior places [13]. Since retrofit scenarios include various activities with different consequences; it is necessary to find an optimal strategy regarding these side effects such as GHG emission and thermal comfort. However, it is difficult for experts to attain the perfect solution by empirical trial-anderror design [14] affirming the necessity for novel methodologies such as multi-objective decision-making methods to ensure the accuracy of results and present all required subjects for decision-makers. Research literature abounds with the assessment of retrofit schemes for building thermal performance while interior thermal comfort of occupants as a consequence of retrofit activities is not fully addressed [15–17]. Reviewing literature shows that new trends are growing in the context of retrofit side effects on GHG emissions and interior thermal comfort which requires using multi-objective decision-making methods [10]. Since incorporating various retrofit actions make the research process complicated, parametric analysis methods have to be used to facilitate the process and identify the sensitivity and contribution of each action [3,18]. Therefore, studies have to consider combinations of energy modelling programs, parametric analysis and optimization tools to provide comprehensive solutions. This paper attempts to address these shortcomings by presenting a novel methodology to optimise a retrofitting method for the provision of indoor thermal comfort without increasing the environmental effects such as GHG emissions. This research develops a multi-objective opti­ mization method using Genetic Algorithms (GA) coupling with sensi­ tivity analysis in a residential archetype through modelling pre-retrofit and post-retrofit cases. Two main objectives of this research are (1) to determine the significant hierarchy of possible retrofitting methods on a residential archetype in hot and dry climate and (2) present the optimal scenarios incorporating side effects of retrofit actions such GHG emis­ sions and indoor thermal comfort. As a result, this research extends the scope of renovation and refurbishment by considering the occupants’ thermal comfort as an integrated part of retrofitting in residential buildings. Consequently, to entail a deeper understanding of the existing literature, section 2 introduces the main concepts and available litera­ ture regarding these issues. In section 3, the methodology is outlined to analyse the thermal condition of dwelling archetype with respect to occupants’ comfort. Section 4 reports the results and discussions of applied methodology to present the optimal solutions based on the ob­ jectives function and finally, section 5 summarizes the main outcomes of the research and further suggestions for future research.

2.2. Retrofit scenario Building retrofitting has a great contribution to reducing energy consumption and GHG emissions. Based on the significant number of residential buildings, most studies have focused on this type [8,26,27]. Most of the retrofit scenarios are implemented in the building envelope as a cost-effective energy-saving method [27,28]. Ciulla et al. (2011) demonstrated that retrofit solutions reduce energy consumption for about 44.6–56.7% [29]. In another study, it has been reported that by retrofitting 50% energy saving happens in residential buildings’ per­ formance [30]. Ma et al. (2012) have summarized the retrofit imple­ mentation as a five-step process starting with setting up a survey, energy audit, defining the retrofit scenario, site implantation and finally vali­ dation and verification of all scenarios [6]. Passer et al. (2016) have also indicated that high-quality refurbishment of thermal envelopes leads to a reduction of energy demand and improvement of potential for elec­ tricity generation [31]. Reviewing the literature shows that retrofit scenarios can be cat­ egorised based on the three main indicators of the type of actions, location, and target of retrofit actions (Fig. 1). Regarding the retrofit type, two categories of constructional or technological actions are recognized. Most of the building retrofit ac­ tions focus on construction characteristics such as walls, windows, and roofs [14,32]. This kind of actions incorporates construction details or replacing and adding new building components. Insulating the build­ ings’ elements is one of the most common retrofit actions in residential buildings [33,34]. The other kind of retrofit scenarios focuses on improving technologies used in lighting, HVAC systems and control, and 2

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Journal of Building Engineering 29 (2020) 101174

objectives [39,40]. This process deals with trade-offs between competing goals such as energy efficiency improvement, environmental impacts, property value increase, air quality, thermal comfort, solar irradiation and other technical, environmental and social concerns [41, 42]. The optimization methods reduce the required time for analysing the retrofitting procedure through iterative procedures [18]. Therefore, coupling an appropriate optimization procedure with a whole building energy simulation tool makes this time-consuming procedure feasible. Optimization algorithms are conducted by various methods such as GAs. The GAs which mimic biological evolution are methods for solving both constrained and unconstrained optimization problems based on a natural selection process [43,44]. GAs are mostly used to solve multi-­ objective issues of building energy performance by combining the en­ ergy performance tools such as EnergyPlus with optimization engines such as GenOpt [45], Grasshopper [46], Optimo [47], Open Studio [48] and other similar engines.

Fig. 1. This diagram illustrates three main criteria of typical retrofit actions in residential buildings.

3. Methodology

management equipment. This category incorporates a wide variety of activities such as replacement of old and low efficient electrical and mechanical equipment, application of the demand control mechanism and regular balancing of the management and monitoring systems [3, 35]. The second sorting measure is about the location in which these actions are used such as exterior layers, building envelopes and interior parts [31]. The third measure is about the specific objectives that retrofit actions are focused on including reducing energy consumption, elec­ tricity generation and mixed solutions [36,37].

This research is conducted in four main steps Fig. 2: (1) The base case model is developed according to the real data and surveying the archi­ tectural, constructional and mechanical characteristics of dwelling archetype; (2) Retrofit techniques are applied based on the literature review and building characteristic and requirements; (3) A framework is defined to reduce the number of required calculations for the optimi­ zation process in which the more effective retrofit actors alter in the iterative optimization process and the less important retrofit actions are considered fixed. Therefore, the parametric sensitivity analysis is con­ ducted to determine the significance of retrofit actions. In this stage, a range of values and intervals are defined for each input parameter. The process of defining these ranges and intervals will be outlined in section 4.3, (4) In the final stage, the multi-objective optimization process is conducted to present the optimized retrofit scenarios minimising the side effects of retrofit application. These steps are explained in more detail in Fig. 2.

2.3. Retrofit analysis To make a decision between retrofit scenarios and investigate among the side effects on GHG emission and thermal comfort of occupants, different methods and tools such as sensitivity analysis, and optimiza­ tion algorithms are beneficial [3,14,38]. The optimization is a complex method finding the solution among a set of alternatives and competing

Fig. 2. This process diagram outlines the overall methodology of research consisting of base case development, retrofit application, parametric sensitivity analysis, and optimization procedure. 3

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Journal of Building Engineering 29 (2020) 101174

3.1. Base case development

3.4. Optimization procedure

In the first step, the base case archetype model is developed based on real data collection. This research focuses on guidelines for typical dwellings of an Iranian city -Yazd-located in a hot and dry climate. A typical type of residential building with poor energy performance is modelled as the base case for pre-retrofit energy performance in which the retrofit modifications would be applied. This step consists of pre­ liminary data collection regarding the energy consumption of the base case and then surveying architectural, mechanical, and operational characteristics of dwelling archetype. To ensure the accuracy of the research, an archetype model that is representative of a considerable part of residential buildings is required to be modelled. This model will be referred to as the base-case archetype model. It is imperative that the values used for all input parameters for the base-case model are representative of the “most probable” values for these kinds of local dwellings. Therefore, the sample case is one of these significant cases which is based on this preliminary study and is deemed to be representative of a considerable portion of the residential part of the city. The robust field study data from real residential buildings make the foundation for a reliable model. In this regard, in order to calibrate the archetype development, the actual energy performance of the building from the utility bills was compared with the calculated energy con­ sumption. As this model has been extensively calibrated against measured data, it is considered to sufficiently represent the actual per­ formance of the residential building type. The discrepancies between the predicted and actual energy savings are mainly due to the uncertainty of the input data such as the weather files and the behaviours of the oc­ cupants. However, the results indicated less than 15% discrepancy, which made the simulation method reliable for the objectives of the study [3].

The results of the parametric analysis are categorised based on their significance for entering the optimization process. Retrofit projects are usually addressed by multi-criteria decision-making methods since they involve several conflicting objectives and constraints. As a result, the less important retrofit actions are fixed in the average level and the more effective factors are involved in the optimization calculations. The objective function incorporates two main consequences of retrofit actions including minimising the environmental impacts with a focus on GHG emissions and improving the thermal comfort by reducing the unsatisfied hours of occupants. The multi-objective optimization is applied to extract the Pareto front for the dwelling archetype by NonDominated Sorting Genetic Algorithm II (NSGA-II) algorithm. NSGA-II as a modified version of the genetic algorithm is introduced as one of the most efficient tools to solve multi-objective optimization of energy performance problems [18]. This multi-objective optimization meth­ odology is executed by JEPlus and JEPlus-EA to achieve feasible solu­ tions with respect to the constraints of context and interaction of variables. The optimization process is conducted in JEPlus-EA as a powerful Graphical user interface (GUI) for EnergyPlus and TRNSYS. JEPlus-EA that uses highly efficient and versatile multi-objective opti­ mization algorithms (based on the popular NSGA-II) is employed to work on all types of optimization problems. Like any other GA, this is based on the evolution of a population of individuals, each of which is a solution to the optimization problem. EnergyPlus is used to simulate the building energy behaviour and then the results are imported to the optimization algorithm in the JEPlus and JEPlus-EA. Fig. 3 shows the overall optimization process from beginning to reporting the optimal scenarios (Fig. 3). 4. Result and discussion 4.1. Base case development

3.2. Retrofit application

In this section, dwelling archetype is developed based on the real data collection and surveying the building characteristics of the selected case study. As the retrofit investigation is subjected to the existing buildings with poor energy performance, this archetype is the most appropriate case based on the high rate of energy consumption. The considerable population of this district affirms the urgent attention for reducing the energy consumption. However, the processes and meth­ odology used are highly repeatable and applicable in different building categories situated in any given location. During these decades, this city has experienced rapid growth and intensive urbanization [53]. The urban morphological analysis shows that three types of residential archetypes can be recognized as the main dwelling types [54]. The selected archetype model which is represen­ tative of a significant portion of the dwelling stock is constructed more than 30 years ago (Fig. 4). The dwelling archetype is located in the Yazd city in the centre part €ppen classification, Yazd is categorised as a hot of Iran. Based on the Ko desert climate (BWh) [55]. The latitude of this city is 31.89, and the longitude is 54.35. The constant high temperature during summer and cold weather in winter with the high difference between the maximum and minimum temperature during a day are the most important char­ acteristics of this climate. Low rate of precipitations and high rate of evaporation in summer are of two important factors which make this province one of the driest areas of Iran [56]. The results of hourly weather data analysis show that the maximum and minimum tempera­ ture points happen on the 18th of August for 42.3 � C and 3rd of January for -7 � C respectively (Fig. 5). This dwelling archetype is a south-facing single-family house attached to two houses with similar height in the west and east sides. The total net floor area of the base case model is 260 m2 (Table 1). The dwelling archetype with an average window to wall ratio of 0.4, 0.3, 0.0,

After the base case development, by observing the defects from the first step and retrofit techniques summarized from the literature review, possible methods and retrofitting scenarios have been selected to be evaluated via sensitivity analysis. All the applicable retrofit measures are identified based on the availability and characteristics of the context. 3.3. Parametric sensitivity analysis As it is complicated to consider all retrofit actions in the optimization calculations, it is beneficial to apply the most important retrofit actions. The PSA is conducted to determine the most effective retrofit actions. The PSA method is employed on data describing how the output (building energy consumption) changes as the inputs are varied (retrofit actions for building properties), thus providing a weighted representa­ tion of the influence of each input parameter. The results of the PSA are categorised based on their significance for entering the optimization process. The more effective retrofit actors alter in the iterative optimi­ zation process and the less important retrofit actions are considered fixed at the defined value. For performing the sensitivity analysis, the data reduction technique of Design of Experiments (DOE) is applied. DOE is an accurate approach for performing a sensitivity analysis to choose the minimum amount of parameter combinations by the method of choice and the input uncer­ tainty [49,50]. In this research, Mixed-level-fractional-factorial-design as a quantitative approach of DOE was used to conduct the sensitivity analysis for retrofit scenarios. DOE provides the significance of param­ eters and their major interaction by eliminating redundant observations and tests [51,52]. To statistically verify the significance of each variable, we used student t-test with confidence level of 90%. As a result, the most effective retrofit actions are identified for the optimization algorithm. 4

R. Aghamolaei and M.R. Ghaani

Journal of Building Engineering 29 (2020) 101174

Fig. 3. The optimization algorithm framework illustrating the consequential combination of energy modelling, parametric analysis, and optimization tools.

cooling system is provided by low efficient evaporative coolers with ducted ventilation used for about 4 months of the year. The buildings heating and cooling systems were controlled by heating set point: 22 � C and heating setback 12 � C and cooling setpoint: 24 � C and cooling setback of 28 � C. The heating schedules are set on for November to March and the cooling schedule is on during June to September from 8 a. m. to 6 p.m. These values were considered to be the same over the analysis period (Table 1). Occupancy pattern and schedules for HVAC and lighting system are based on real data from building reference case. The fluorescent lighting system is defined for the living room and bedrooms (Table 1). In order to consider the heat gain from home appliances and other equipment, their operation schedule is assumed to the default definition of each function [57]. After data gathering and building survey, the modelling process is done by Designbuilder V5 as a multi-zone residential building including all typical spaces [57]. Once the modelling process is complete, the model is exported to EnergyPlus to execute the annual energy con­ sumption for each retrofit scenario. The annual energy usage of the residential archetype is divided to four main subcategories: heating system, Domestic Hot Water (DHW), electric usage for the cooling sys­ tem and minor equipment and finally lighting system (Fig. 6). The results adopted by energy simulation shows that consumption is heavily dominated by space heating and cooling requirements. The re­ sults of energy simulation are compared to the real data from bills to be assured of the accuracy of the research. In the next step, the most applicable retrofit actions are selected to be implemented in building with a focus on the improvement of energy consumption and GHG emissions.

Fig. 4. Various existing residential archetypes in the inner and outer layers of the city. The illustrated coloured areas show the selected district for executing the energy simulation.

and 0.0 in south, north, east, and west respectively was constructed more than 30 years ago. The base case model has single-layer windows and non-insulated exterior and interior walls, only the roof has been insulated, resulting in poor energy performance. Windows have one single layer of 4 mm flat glass with metal frames resulting in U value of 3.1 (W/m2 K) (Table 1). The heating system is provided locally by hot water radiators working with natural gas boilers. The overall seasonal COP/efficiency of the heating system is about 0.8 without automatic regulations. The

4.2. Retrofit scenario application The definition of a retrofit scenario relies upon available actions, special characteristics of the context and the existing constraint for 5

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Journal of Building Engineering 29 (2020) 101174

Fig. 5. The diagram depicts the most important features of the weather condition of base case archetype. Fig. 3a shows maximum, minimum and average outdoor dry bulb temperature (� C), 3b depicts the amount of direct normal solar and diffuse horizontal solar gain, Fig. 3c shows the wind speed (m/s). Table 1 Short description of input data for the reference archetype. Building function

Orientation

Number of floors

Building length [m]

Building width [m]

Building height [m]

Plot ratioa

U value (W/m2 K)

Residential– Single family house

South

1

19

15

3

0.65

Wall 0.51

a

Window 3.1

Roof 0.72

Plot Ratio: the total built floor area to the plot area.

resources. In order to identify the refurbishment scenarios, seven cate­ gories of retrofit actions are identified including exterior walls, floor, windows, roof, HVAC system, airtightness, and lighting system (Table 2). The energy simulation is conducted in the EnergyPlus as opensource free software, developed by the US Department of Energy [25] with IWEC (International Weather for Energy Calculations) weather files [58].

important retrofit scenarios among all available retrofit actions. In this regard, only the most effective retrofit measures are entered into the optimization process to reduce the required tests and save time. Consequently, less significant factors are eliminated form optimization algorithm [14]. In this research, DOE is used to conduct the sensitivity analysis for retrofit scenarios. In this analysis, energy consumption is considered as main objective and retrofit alternatives are input variables. Annual energy simulation conducted by EnergyPlus determines the energy performance of dwell­ ing for each scenario based on the hourly weather condition files. For calculating the impact of design variables in DOE, a number of levels

4.3. Sensitivity analysis for retrofit scenarios In this section, the sensitivity analysis is used to determine the most 6

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Journal of Building Engineering 29 (2020) 101174

should be defined for each run of the experiment. Each level explains the input value of the retrofit alternative for separate experiments. In order to obtain a sufficient resolution that how to retrofit scenarios affect the energy performance of residential buildings, each input parameter is simulated at specific ranges (Table 2). By performing analysis, the dif­ ferences between response variables would be assigned to the design variables and their interaction [59]. Where nþ and n are the numbers of values in the upper and lower parts of the domain of the input variable; Xþ and X are the means of the values for the response parameters in the upper and lower parts of the domain of the input variable (Eqs. (1) and (2)). Where x and xþ are the upper and lower output variables, respectively, and S2þ and S2 are the variances of the population for the response output variable in the upper and lower parts of the domain of the input variable [60] (Eqs. (3) and (4)). The results of student t-test reflect the magnitude, direction, and contribution of each action to the objective. Fig. 6. Monthly energy consumption profile of the archetype shows the sig­ nificant portion of space heating and cooling in energy consumption.

t¼

jX þ X j ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi� ffiffiffi sffi� 1 1 Sp þn nþ

1

Table 2 The range of possible values for defined levels of input variables for application in DOE analysis. Category

Location

Description

Abbreviate of Action

Altering Factor

Levels/Ranges

Construction

Exterior walls

Thermal performance improvement with sufficient insulation

External Wall Insulation (EWI)

Dimension: Thickness

Floor

Implementation of mixed layers of insulation

Floor Insulation (FI)

Dimension: Thickness

Windows

Utilization of double glazed window- aluminium window frame with thermal break

Window Replacement (WR)

Element: Replacement

Roof

Application of roof insulation

Roof Insulation (RI)

Dimension: Thickness

Building envelope

Infiltration reduction

Airtightness (AR)

Element: Replacement/Adding new item

HVAC system

Changing the ideal temperature of occupants

Temperature Set Points (TS)

Building Management system (BMS): Change schedule

HVAC system

Improving occupants’ behaviour

Operation Schedule (OS)

Building management system (BMS): Change schedule

Lighting system

Application of High-performance lighting system

Artificial Lighting (AL)

Element: Replacement/Adding new item

No EXIi 1-EWI1 2-EWI2 3-EWI3 4- EWI4 5- EWI5 6- EWI6 High EXIiþn No FIi 1-FI1 2-FI2 3-FI3 4- FI4 5- FI5 6- F6 High FIiþn No WRi 1-WR1 2-WR2 High WRiþn No WRi 1-RI1 2-RI2 3- RI3 4- RI4 5- RI5 High WRiþn Very Poor ARi 1-AR1 2-AR2 3- AR3 Excellent ARiþn Ideal status TSi 1-TS1 2-TS2 Economy Status TSiþn Ideal status OSi 1-OS1 2-OS2 Economy Status OSiþn Low Standard ALi 1-AL1 2- AL2 3- AL3 High Standard Aliþn

Equipment/ Technology

7

R. Aghamolaei and M.R. Ghaani

S2p ¼

1ÞS2 þ ðnþ 1ÞS2þ n þ nþ 2

ðn P

S2þ ¼ P S2 ¼

Journal of Building Engineering 29 (2020) 101174

2

ðxþ nþ

X þ Þ2 1

3

ðx n

X Þ2 1

4

As all the retrofit actions improve energy performance since the di­ rection is positive for these actions. DOE results obtained from student ttest indicate that AL, EWI, AR, and RI are the most effective variables (Table 3). The results of Table 3 affirm that EWI has the most significant impact on reducing energy consumption. Changing the value of EWI variable from range EW1 (the lowest level of retrofit action) to EW6 range (the highest level of retrofit action) results in the most significant changes in the response variable. While RI alters from RI1 (the lowest level of insulating) to RI5 (the highest level of insulating), the building performs better significantly from the energy performance point of view. The impact of AL on the archetype energy performance is about 68% of EWI effect and 1.13 time more significant in comparison to FI. These four parameters account for about more than 50% of the energy performance of the base case model (Fig. 7). This large contribution of EWI is related to the two points: (1) There a is a considerable number of outdoor walls with the poor condition in south and north façade of this building; (2) The poor thermal conduc­ tivity condition of outdoor walls in the base case model. Other studies have also affirmed the impact of EWI in reducing energy loss and improving the rate of building in energy assessment ranking [61,62]. The energy performance of the building is highly influenced by the insulation of exterior walls especially in extreme cold/hot climates [63]. The results indicate that RI is the next important parameter in this hierarchy. Retrofitting the roofs results in higher saving rates of energy [64], especially in a one-story building. Since the reference model has one level, all the ceilings have outdoor surfaces leading in more energy loss. In addition to the significant contribution of EWI and RI, they are considered as cost-effective methods for residential buildings [63,65]. The third effective factor is AR which involves different parts of buildings. The level of airtightness is defined by the crack template of EnergyPlus for building envelope including walls, openings, roofs and also interior elements such as partitions [25]. Different studies have affirmed the significant impacts of airtightness on ventilation rates and energy consumption. Results have revealed that AR is highly affected by heating and cooling controlling systems and weather condition [63]. In a retrofit study, the contribution of AR to the reduction of energy con­ sumption is reported by one third [65]. FI as another parameter with significant impact on the response variable [66] has similar specifica­ tions to FI, EWI, and RI because of the insulation regulations and con­ struction details. According to statistical analysis, the four parameters of AL, OS, WR, and TS have less contribution to the response variable. However, their significant size is not neglectable (Table 3). For instance, AL is an

Fig. 7. This diagram illustrates the significance of retrofit measures in response to energy performance.

important parameter in the hierarchy of contribution to an energy reduction of this archetype. In other literature, replacing the efficient lighting system is one of the most effective methods to improve the energy performance of the buildings [67,68]. However, replacing old low standard lighting systems with more high standard systems such as fluorescent or LED requires a significant budget and consequently in­ creases the cost of scenario [69]. Improving the energy efficiency by means of lighting system incorporates taking advantage of day-lighting, sun shading system and using high-performance lighting system with intelligent control systems. 4.4. Optimization procedure In the previous step, all the parameters were analysed through a sorting process resulting in two categories of inputs. The first category includes four parameters of EWI, RI, FI, and AR as altering factors controlling the optimization process toward the objective function. The second group includes parameters of AL, WR, OS, and TS with less sig­ nificance. These parameters are assigned typical values and considered constant for entering the optimization process for calculating the ther­ mal comfort quality. As mentioned above, the optimization algorithm is conducted by a combination of three tools including EnergyPlus, JEPlus, and JEPlus-EA [70]. JEPlus is used for running parametric analysis with EnergyPlus IDF files and the optimization algorithm is performed in JEPlus-EA which receives the variables from JEPlus parameter tree (Fig. 3). For running the multi-objective genetic optimization of NSGA-II, the population size of 20 and maximum generation of 30 are selected based on the previous studies to achieve the best trade-off between Pareto front and compu­ tational time [18]. 4.4.1. Objective function The objective function is written in the command script files of JEPlus project. Two objectives of the environmental impacts of the base case model and thermal comfort of occupants are considered as the response variable for the optimization process. The first objective is defined to be minimized and thermal comfort as the second objective has to be maximized. In the objective function, the balancing factor between these two objectives controls the optimization algorithm [14]. In this model, the simultaneous optimization of CO2 emission and thermal comfort hours is sought. The CO2 emission and total discomfort ours of the building are directly assessed by EnergyPlus. The CE and DS are defined to represent the amount of Carbon Emission in kilogram and the total amount of discomfort hours. In this expression, the cðxÞ values are based on the energy consumption of different parts of building such as domestic hot water, heating and cooling, etc. The metric used to

Table 3 The results of DOE illustrate the significance of each parameter for improving the energy performance of the residential archetype. Factor

Delta

T-Value

Ranking

AL AR FI TS OS EWI RI WR

16343 14478 9782 1212 7208 15860 11886 4566

1.28 1.14 1.13 0.14 0.82 1.89 1.38 0.52

3 4 5 8 6 1 2 7

8

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Journal of Building Engineering 29 (2020) 101174

assess thermal discomfort is the standard Predicted Mean Vote (PMV), based on Fanger’s mode. This value is calculated for each individual zone (dðzÞ) and summed up to represent the total discomfort hours (DCðxÞ) X CEðxÞ ¼ ci ðxÞ

basic action based on the easy applicability and availability of this method in the context (Fig. 8). The range of input variables for the definition of retrofit alternatives is presented in Table 4. To ensure the reliability of the results, the steps are defined in various ranges to cover all possible solutions (Table 4). The results of the optimization algorithm are analysed in different ways based on the specific approaches and goals of the research meth­ odology. In fact, extracting the general trends is one of the important outputs to understand the pattern and behaviour of the model. Fig. 9 illustrates the general trend of the optimization progress. During this process, based on the selected population size and generation number,

i¼1

X DCðxÞ ¼ dz ðxÞ z¼1

In these formulas, ‘i’ is related to the sources of energy consumption and ‘z’ shows different zones in the building. The final goal of the optimization problem in this phase is to find the optimise condition where both carbon emission and the total amount of discomfort hours are minimum. An optimization algorithm is used to assess this multiobjective-optimization problem and identify the set of non-dominated solutions. For this purpose, we have defined a new objective where is a combination of CE and DS for each retrofit scenario (x), weighted equally (pi ​ and ​ pj ) in our calculation.

Table 4 Features of retrofit scenarios for executing the optimization algorithm with altering parameters for each variable.

ObjðxÞ ¼ pi CEðxÞ þ pj DSðxÞ Where pi þ pj ¼ 1 and. pi ¼ pj ¼ 0:5

N

Design Variable

Abbreviate of Action

Altering parameters for the definition of variable

Minimum and Maximum of each variable

Altering step

1

External wall insulation Roof Insulation Floor insulation

EWI

- Insulation thickness (m)

0.05:0.60

0.005

RI

0.034: 0.639

0.005

0.100: 0.700 0.130: 0.490

0.005 0.003

Airtightness

AR

- Insulation thickness (m) - 2 types of Insulations - Insulation thickness - Flow coefficient of 3 types of elements (Kg/ s) (WindowWall-Roof)

0.02: 0.00014 0.0022: 0.0001 0.00037: 0.0001

0.001 0.0001 0.00001

2

4.4.2. Design variables To calibrate the design scenarios, a framework is defined in which the less important retrofit actions are fixed in the average level and the more effective factors alter through the optimization process (Fig. 8). The design variables are a set of retrofitting alternatives of EWI, RI, FI, and AR which are considered as the altering parameters for different scenarios (Fig. 8). Four parameters of OS, TS, WR, and AL are fixed as preliminary actions with typical values. OS and TS are fixed on their post-retrofit conditions to improve energy performance. AL is also fixed on the average level. WR for double glazed windows is applied as the

3

4

FI

Fig. 8. This diagram depicts the optimization process applied to propose the most efficient solutions to minimise GHG emissions and total hours of discomfort via appropriate retrofit scenarios. In this framework, the less important retrofit actions are fixed in the typical level while the more effective factors alter through the optimization process. 9

R. Aghamolaei and M.R. Ghaani

Journal of Building Engineering 29 (2020) 101174

the algorithm finds the building conditions with the best performance regarding the selected objectives. Thus, as it can be seen, the number of selected points with lower carbon dioxide emission (less than 8000 Kg CO2) is significantly higher in comparison with other simulated points (Fig. 9). In addition, the result proves that the minimum value of total discomfort hours is always coupled with the lowest possible values for CO2 emission. It means that a building with low carbon emission simultaneously can improve thermal comfort condition. In other words, the increasing trend of thermal discomfort with the higher value of the CO2 production depicts the coupling between these two objectives. Fig. 10 displays some of the optimized solutions in the nondominated frontier resulting in different GHG emission level and ther­ mal comfort conditions. The optimization analysis shows that the whole range of discomfort hours varies from 13867 to 19139 and the produced CO2 changes from 7109 to 17749 kg in one year of simulation (Fig. 10). Further increasing in annual carbon emission leads to a higher rate of total discomfort hours confirming that these objectives can be mini­ mized concurrently. Additionally, the slope of the curve in Fig. 10 de­ picts that the total discomfort hour is much more effective in the objective function. Choosing each solution results in various trade-offs between GHG emissions and thermal comfort of residents. The most important func­ tion of this algorithm is to control the balance between the allowance of fresh air entrance and thermal transmittance of construction variables. Fresh air entrance results in improvement of occupants’ thermal com­ fort; while thermal transmittance of construction variables affects the energy performance and GHG emissions. Therefore, if more retrofit ac­ tions are applied, the result will not be in the acceptable range for oc­ cupants’ thermal comfort. However, the level of energy consumption is significantly increased because of the poor level of airtightness. There­ fore, it seems that other parameters such as mechanical ventilation systems can play a paramount role in changing this balance [71]. It is worthwhile to mention that the limited number of optimized solutions in the Pareto front is based on the two points. First, the range of input variables is defined based on the expert wisdom showing the possibility for application in the real base case model. Consequently, impossible solutions are automatically eliminated from the procedure. Second, the similar correspondence of objectives results in a lower number of generated solutions in comparison to those with many con­ flicts and different characteristics [14].

Fig. 10. The application of NSGA-II for the maximum number of iterations based on the value of generations and populations to predict the conflict or convergence between objectives.

Analysis of optimization results demonstrates that in the optimized solution, the total discomfort hours will be around 13867 h; while the amount GHG emission is 7117 Kg for one year. This result happens in a specific combination of design variables. A deeper view to input vari­ ables shows that value of 0.275 for EWI results in the minimum envi­ ronmental effects and maximum thermal comfort of interior places. Meanwhile, the combined thickness of FI is reported for a total value of 0.120 and 0.142. The RI as another significant parameter is assigned to the value of 0.18. The important role of AR is also based on the fact that this parameter incorporates various elements of building envelope and interior components. In this case, the reported values for crack template of building envelope include three subcategories of air mass flow coef­ ficient for windows, walls and roofs as following: 0.00014 kg/s.m crack @ 1Pa, 0.0001 kg/s.m2 crack @ 1Pa and 0.0001 kg/s.m2 crack @ 1Pa respectively. This balance ensures the provision of a healthy and pro­ ductive environment for interior residential places. 5. Conclusion A novel multi-objective optimization method using GA coupling with sensitivity analysis is applied in a residential archetype. This archetype is located in a hot climate requiring heating and cooling systems for most of the year. The low energy performance of this archetype emphasizes the necessity for the refurbishment project. However, the side effects of retrofit projects have to be monitored especially for improving the health and wellbeing of occupants. This research explores the signifi­ cance of various retrofit scenarios which are common in the context of the project. Application of the proposed approach demonstrates an energy-saving method without any dissatisfying effects on the thermal comfort of occupants. Then, it seems necessary for national energy performance standards to include comfort analysis as a consequence of a retrofit project. Since the required time for the calculation of the GA optimization process is significant, a unique methodology is provided to calibrate retrofit scenarios. At the first step, eight retrofit measures are selected by reviewing the related literature and availability of actions in the context. To reduce the required iterations for the optimization process, a sensi­ tivity analysis is conducted to reduce the retrofit measures by defining the most effective ones based on the climate and morphological condi­ tions. Consequently, less important measures are identified by DOE and then, they are fixed on their typical values. In this regard, four less significant parameters are eliminated and others were entered into the

Fig. 9. The diagram illustrates the simulated points during the optimization process at different building conditions. Each column height represents the number of selected points, where the calculated carbon emission is in the range of appropriate plotted X values (e.g. 7000 < CO2 < 8000 kg). The average total discomfort hours of the selected models are also presented with black diamonds. 10

Journal of Building Engineering 29 (2020) 101174

R. Aghamolaei and M.R. Ghaani

optimization process. The input parameters for finding optimized solu­ tions include EXI, RI, FI, and AR. In this way, the total computational time associated with the GA is reduced considerably for improving two objectives of GHG emissions and thermal comfort simultaneously. The analysis of results demonstrates how physical characteristics of energy efficiency measures simultaneously affect the decision-making objectives. Therefore, by including more objectives in the process, more diverse energy-efficient solutions will be provided that facilitate the decision making the process. Investigation of optimized solutions reveals that airtightness is a significant parameter for controlling the results compared to the three other variables. This balance ensures the provision of a healthy and productive environment for interior resi­ dential places. As a result of this research, combining the PSA at the early stages of this algorithm will assist to facilitate the optimization process and investigate the comfort-related interaction of retrofit actions. The most influential input parameters will be used in order to form a minimum set of accurately defined input data for conduction optimization algorithms. Furthermore, the minimum data set can be used in order to add some objectivity to the decisions made regarding input data assumptions and simplifications, ultimately leading to increased optimization accuracy and/or decreased optimization time. Further research has to focus on investigation for other interventions of retrofit measures on quality of spaces and occupants’ behaviours such as indoor air quality. These metrics should be analysed based on the climate and context specifications and morphological parameters. Implementation of such studies in offices is critical due to the high rate of energy consumption and considerable time of people attending in interior places which requires great attention for comfort-oriented retrofit projects. Furthermore, the results from this research can be used as a necessary input for future studies to investigate the cost of retrofitting actions and accordingly all the dimensions of environment, cost and comfort will be included in the energy-oriented retrofit planning.

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Declaration of competing interest There is no conflict of interest or funding to declare for this specific research. CRediT authorship contribution statement Reihaneh Aghamolaei: Conceptualization, Methodology, Supervi­ sion, Validation, Writing - review & editing. Mohammad Reza Ghaani: Software, Data curation. References [1] C. Ash, B.R. Jasny, L. Roberts, R. Stone, A.M. Sugden, Reimagining cities, Science (80-) (2008) 319. [2] R. Saidur, A.E. Atabani, S. Mekhilef, A review on electrical and thermal energy for industries, Renew. Sustain. Energy Rev. 15 (2011) 2073–2086. [3] J. Egan, D. Finn, P.H.D. Soares, V.A.R. Baumann, R. Aghamolaei, P. Beagon, O. Neu, F. Pallonetto, J. O’Donnell, Definition of a useful minimal-set of accuratelyspecified input data for Building Energy Performance Simulation, Energy Build. 165 (2018), https://doi.org/10.1016/j.enbuild.2018.01.012. [4] UNEP, District Energy in Cities, Unlocking the Potential of Energy Efficiency and Renewable Energy, 2015, p. 137. [5] M. Almeida, M. Ferreira, Cost effective energy and carbon emissions optimization in building renovation (Annex 56), Energy Build. 152 (2017) 718–738, https://doi. org/10.1016/j.enbuild.2017.07.050. [6] Z. Ma, P. Cooper, D. Daly, L. Ledo, Existing building retrofits: methodology and state-of-the-art, Energy Build. 55 (2012) 889–902, https://doi.org/10.1016/j. enbuild.2012.08.018. [7] M. Jaggs, J. Palmer, Energy performance indoor environmental quality retrofit—a European diagnosis and decision making method for building refurbishment, Energy Build. 31 (2000) 97–101. [8] R. Aghamolaei, Evaluation of supply and demand in building energy performance: application of retrofit scenarios in residential building, Energy Eng. 116 (2019) 60–79, https://doi.org/10.1080/01998595.2019.12043339.

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