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

Journal Pre-proof Balancing the impacts of energy efficiency strategies on comfort quality of interior places: Application of optimization algorithms in domestic housing Reihaneh Aghamolaei, Mohammad Ghaani PII:

S2352-7102(19)31762-0

DOI:

https://doi.org/10.1016/j.jobe.2020.101174

Reference:

JOBE 101174

To appear in:

Journal of Building Engineering

Received Date: 21 August 2019 Revised Date:

1 December 2019

Accepted Date: 4 January 2020

Please cite this article as: R. Aghamolaei, M. Ghaani, Balancing the impacts of energy efficiency strategies on comfort quality of interior places: Application of optimization algorithms in domestic housing, Journal of Building Engineering (2020), doi: https://doi.org/10.1016/j.jobe.2020.101174. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

Dear Prof Brito,

Concerning our CRedit author statement, I must to declare the following: Reihaneh Aghamolaei (Corresponding author): Conceptualization; Methodology development; Process supervision; Data and analysis validation; Draft writing; review & editing. Mohammad Reza Ghaani: Software, Data analysis, Data curation,

Kind regards, Reihaneh Aghamolaei

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

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Reihaneh Aghamolaeiab, Mohammad Ghaanic

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a

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

School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, Ireland

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*Corresponding Author: Email: [email protected], [email protected] Reihaneh Aghamolaei Ph.D. candidate School of Urban planning College of Fine arts University of Tehran Tehran, I.R. IRAN P.O.Box: 14155-6619 +98(21) 61113411

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

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Abstract

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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 optimisation 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 optimisation 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 actions. The more important category is entered to the optimisation 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 optimisation algorithm assist to facilitate the optimisation 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.

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Keywords: Retrofit; Thermal Comfort; Optimization; Genetic Algorithms; Residential Buildings

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2

Nomenclature 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

Domestic Hot Water Floor Insulation Roof Insulation Temperature Set Points Artificial Lighting Operation Schedule

WR

Airtightness

55

3

DHW FI RI TS AL OS AR

56

1- Introduction

57

Cities account for approximately 75% of the world's energy consumption and 80% of Greenhouse

58

Gases (GHG) emissions respectively, even though they occupy only 2% of the total world’s surface

59

[1]. To reduce the overall energy consumption and thereby GHG emission, there is a growing trend

60

for improving energy performance by retrofitting and renovating actions [2,3]. Since the building

61

sector accounts for a noticeable part of the overall energy consumption [4], many studies evaluate

62

the environmental impacts of existing buildings [5]. Retrofit measures have been widely used as a

63

cost-effective approach to reducing building energy consumption and GHG emissions [6,7]. The

64

available retrofit technologies differ by building characteristics, project target, budget plan,

65

regulations and occupancy pattern [3,8].

66

Although retrofitting has a significant effect on energy saving and GHG emission, its intervention

67

can affect the other metrics of life quality such as indoor air quality, thermal comfort, health and

68

consequently the well-being of occupants [9,10]. Nowadays more people are spending their time

69

inside, for instance, European people spend 60-90% of their time in interior spaces and 16% of

70

whom live in damp and unhealthy buildings [1]. Such conditions nearly double the risk of asthma,

71

treatment for which costs 82 billion Euro across Europe each year [1]. In cases where poor comfort

72

conditions occur, occupants tend to suffer from discomfort, health problems, sick building

73

syndrome, and cognitive degradation with repercussion on social, and management costs [2]–[4].

74

With the urgent need to reduce the economic and environmental cost of energy consumption [12],

75

investigating the side effects of retrofit action such as thermal comfort has attracted significant

76

attention. Thermal comfort of space users contributes to healthy and productive interior places [13].

77

Since retrofit scenarios include various activities with different consequences; it is necessary to find

78

an optimal strategy regarding these side effects such as GHG emission and thermal comfort.

79

However, it is difficult for experts to attain the perfect solution by empirical trial-and-error design

80

[14] affirming the necessity for novel methodologies such as multi-objective decision-making

81

methods to ensure the accuracy of results and present all required subjects for decision-makers.

82

Research literature abounds with the assessment of retrofit schemes for building thermal

83

performance while interior thermal comfort of occupants as a consequence of retrofit activities is

84

not fully addressed [15–17]. Reviewing literature shows that new trends are growing in the context

85

of retrofit side effects on GHG emissions and interior thermal comfort which requires using multi-

86

objective decision-making methods [10]. Since incorporating various retrofit actions make the

87

research process complicated, parametric analysis methods have to be used to facilitate the process

88

and identify the sensitivity and contribution of each action [3,18]. Therefore, studies have to

89

consider combinations of energy modelling programs, parametric analysis and optimization tools to

90

provide comprehensive solutions. 4

91

This paper attempts to address these shortcomings by presenting a novel methodology to optimise a

92

retrofitting method for the provision of indoor thermal comfort without increasing the

93

environmental effects such as GHG emissions. This research develops a multi-objective

94

optimization method using Genetic Algorithms (GA) coupling with sensitivity analysis in a

95

residential archetype through modelling pre-retrofit and post-retrofit cases. Two main objectives of

96

this research are (1) to determine the significant hierarchy of possible retrofitting methods on a

97

residential archetype in hot and dry climate and (2) present the optimal scenarios incorporating side

98

effects of retrofit actions such GHG emissions and indoor thermal comfort. As a result, this research

99

extends the scope of renovation and refurbishment by considering the occupants' thermal comfort as

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an integrated part of retrofitting in residential buildings.

101

Consequently, to entail a deeper understanding of the existing literature, section 2 introduces the

102

main concepts and available literature regarding these issues. In section 3, the methodology is

103

outlined to analyse the thermal condition of dwelling archetype with respect to occupants’ comfort.

104

Section 4 reports the results and discussions of applied methodology to present the optimal

105

solutions based on the objectives function and finally, section 5 summarizes the main outcomes of

106

the research and further suggestions for future research.

107

2- Background and overview

108

In this section, the key literature is reviewed in three sections of thermal comfort, retrofit scenarios

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and retrofit analysis which is presented as follows.

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2-1-

111

Thermal comfort is a condition in mind in which satisfaction is expressed with the thermal

112

environment [19]. Parameters such as air temperature, air velocity, relative humidity, mean radiant

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temperature, clothing insulation and activity level control thermal comfort [20]. Sustainability rating

114

systems such as BREEAM (Building Research Establishment Environmental Assessment Method)

115

have considered thermal comfort as necessary criteria toward achieving a sustainable environment

116

[21]. LEED (Leadership in Energy and Environmental Design) defines thermal comfort as a

117

necessary measure for productivity, comfort, and well-being of occupants [22].

118

Various standards and measures have been appeared in recent years dealing with thermal comfort

119

concept. Fanger (1970) as one the pioneers of this field developed PMV (Predicted Mean Vote)

120

index for assessment of interior thermal comfort [23]. PPD (Predicted Percentage of Dissatisfied)

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was used to calculate the level of predicted dissatisfaction among the occupants. ASHRAE Standard

122

55-2010 uses PMV-PMD model to set the requirements for indoor thermal conditions [19]. The

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PMV ranges from -3 to +3 representing too cold environment to too warm environment and the

124

comfort zone is based on the PMV values between -0.5 and +0.5 [19]. PMV-PPD model determines

125

the level of thermal comfort based on linear regression analysis [9,12].

Thermal comfort

5

126

LEED asserts that meeting the requirements of ASHRAE standard 55-2010 or both ISO

127

7730:2005 and (European Committee for Standardization) CEN is adequate for the qualification of

128

thermal comfort design [22]. Standards define temperature and humidity range for thermal

129

satisfaction of at least 80% of occupants in a space [24]. The discomfort hours index demonstrates

130

the summation of hours a year when one or more zones in the building fail the ASHRAE 55

131

comfort criteria [25].

132

2-2-

133

Building retrofitting has a great contribution to reducing energy consumption and GHG emissions.

134

Based on the significant number of residential buildings, most studies have focused on this type

135

[8,26,27]. Most of the retrofit scenarios are implemented in the building envelope as a cost-effective

136

energy-saving method [27,28]. Ciulla et al. (2011) demonstrated that retrofit solutions reduce

137

energy consumption for about 44.6–56.7% [29]. In another study, it has been reported that by

138

retrofitting 50% energy saving happens in residential buildings’ performance [30]. Ma et al. (2012)

139

have summarized the retrofit implementation as a five-step process starting with setting up a survey,

140

energy audit, defining the retrofit scenario, site implantation and finally validation and verification

141

of all scenarios [6]. Passer et al. (2016) have also indicated that high-quality refurbishment of

142

thermal envelopes leads to a reduction of energy demand and improvement of potential for

143

electricity generation [31].

144

Reviewing the literature shows that retrofit scenarios can be categorized based on the three main

145

indicators of the type of actions, location, and target of retrofit actions (Fig. 1).

146 147 148

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

Retrofit Scenario

6

149

Regarding the retrofit type, two categories of constructional or technological actions are recognized.

150

Most of the building retrofit actions focus on construction characteristics such as walls, windows,

151

and roofs [14,32]. This kind of actions incorporates construction details or replacing and adding

152

new building components. Insulating the buildings’ elements is one of the most common retrofit

153

actions in residential buildings [33,34]. The other kind of retrofit scenarios focuses on improving

154

technologies used in lighting, HVAC systems and control, and management equipment. This

155

category incorporates a wide variety of activities such as replacement of old and low efficient

156

electrical and mechanical equipment, application of the demand control mechanism and regular

157

balancing of the management and monitoring systems [3,35].

158

The second sorting measure is about the location in which these actions are used such as exterior

159

layers, building envelopes and interior parts [31]. The third measure is about the specific objectives

160

that retrofit actions are focused on including reducing energy consumption, electricity generation

161

and mixed solutions [36,37].

162

2-3-

163

To make a decision between retrofit scenarios and investigate among the side effects on GHG

164

emission and thermal comfort of occupants, different methods and tools such as sensitivity analysis,

165

and optimization algorithms are beneficial [3,14,38]. The optimization is a complex method finding

166

the solution among a set of alternatives and competing objectives [39,40]. This process deals with

167

trade-offs between competing goals such as energy efficiency improvement, environmental impacts,

168

property value increase, air quality, thermal comfort, solar irradiation and other technical,

169

environmental and social concerns [41,42]. The optimization methods reduce the required time for

170

analysing the retrofitting procedure through iterative procedures [18]. Therefore, coupling an

171

appropriate optimization procedure with a whole building energy simulation tool makes this time-

172

consuming procedure feasible.

173

Optimization algorithms are conducted by various methods such as GAs. The GAs which mimic

174

biological evolution are methods for solving both constrained and unconstrained optimization

Retrofit Analysis

175

problems based on a natural selection process [43,44]. GAs are mostly used to solve multi‐objective

176

issues of building energy performance by combining the energy performance tools such as

177

EnergyPlus with optimization engines such as GenOpt [45], Grasshopper [46], Optimo [47], Open

178

Studio [48] and other similar engines.

179

3- Methodology

180

This research is conducted in four main steps (Fig. 1): (1) The base case model is developed

181

according to the real data and surveying the architectural, constructional and mechanical

182

characteristics of dwelling archetype; (2) Retrofit techniques are applied based on the literature

183

review and building characteristic and requirements; (3) A framework is defined to reduce the 7

184

number of required calculations for the optimisation process in which the more effective retrofit

185

actors alter in the iterative optimization process and the less important retrofit actions are

186

considered fixed. Therefore, the parametric sensitivity analysis is conducted to determine the

187

significance of retrofit actions. In this stage, a range of values and intervals are defined for each

188

input parameter. The process of defining these ranges and intervals will be outlined in section 4.3,

189

(4) In the final stage, the multi-objective optimisation process is conducted to present the optimised

190

retrofit scenarios minimising the side effects of retrofit application. These steps are explained in

191

more detail as follows.

8

192 193

Figure 2- This process diagram outlines the overall methodology of research consisting of base case development, retrofit application, parametric

194

sensitivity

analysis,

and 9

optimization

procedure.

195

3-1-

Base Case Development

196

In the first step, the base case archetype model is developed based on real data collection. This

197

research focuses on guidelines for typical dwellings of an Iranian city -Yazd- located in a hot and

198

dry climate. A typical type of residential building with poor energy performance is modelled as the

199

base case for pre-retrofit energy performance in which the retrofit modifications would be applied.

200

This step consists of preliminary data collection regarding the energy consumption of the base case

201

and then surveying architectural, mechanical, and operational characteristics of dwelling archetype.

202

To ensure the accuracy of the research, an archetype model that is representative of a considerable

203

part of residential buildings is required to be modelled. This model will be referred to as the base-

204

case archetype model. It is imperative that the values used for all input parameters for the base-case

205

model are representative of the “most probable” values for these kinds of local dwellings.

206

Therefore, the sample case is one of these significant cases which is based on this preliminary study

207

and is deemed to be representative of a considerable portion of the residential part of the city

208

The robust field study data from real residential buildings make the foundation for a reliable model.

209

In this regard, in order to calibrate the archetype development, the actual energy performance of the

210

building from the utility bills was compared with the calculated energy consumption. As this model

211

has been extensively calibrated against measured data, it is considered to sufficiently represent the

212

actual performance of the residential building type. The discrepancies between the predicted and

213

actual energy savings are mainly due to the uncertainty of the input data such as the weather files

214

and the behaviours of the occupants. However, the results indicated less than 15% discrepancy,

215

which made the simulation method reliable for the objectives of the study [3].

216

3-2-

217

After the base case development, by observing the defects from the first step and retrofit techniques

218

summarized from the literature review, possible methods and retrofitting scenarios have been

219

selected to be evaluated via sensitivity analysis. All the applicable retrofit measures are identified

220

based on the availability and characteristics of the context.

221

3-3-

222

As it is complicated to consider all retrofit actions in the optimization calculations, it is beneficial to

223

apply the most important retrofit actions. The PSA is conducted to determine the most effective

224

retrofit actions. The PSA method is employed on data describing how the output (building energy

225

consumption) changes as the inputs are varied (retrofit actions for building properties), thus

226

providing a weighted representation of the influence of each input parameter. The results of the

227

PSA are categorised based on their significance for entering the optimization process. The more

228

effective retrofit actors alter in the iterative optimization process and the less important retrofit

229

actions are considered fixed at the defined value.

Retrofit Application

Parametric Sensitivity Analysis

10

230

For performing the sensitivity analysis, the data reduction technique of Design of Experiments

231

(DOE) is applied. DOE is an accurate approach for performing a sensitivity analysis to choose the

232

minimum amount of parameter combinations by the method of choice and the input uncertainty

233

[49,50]. In this research, Mixed-level-fractional-factorial-design as a quantitative approach of DOE

234

was used to conduct the sensitivity analysis for retrofit scenarios. DOE provides the significance of

235

parameters and their major interaction by eliminating redundant observations and tests [51,52]. To

236

statistically verify the significance of each variable, we used student t-test with confidence level of

237

90%. As a result, the most effective retrofit actions are identified for the optimization algorithm.

238

3-4-

239

The results of the parametric analysis are categorised based on their significance for entering the

240

optimization process. Retrofit projects are usually addressed by multi-criteria decision-making

241

methods since they involve several conflicting objectives and constraints. As a result, the less

242

important retrofit actions are fixed in the average level and the more effective factors are involved

243

in the optimization calculations.

244

The objective function incorporates two main consequences of retrofit actions including minimizing

245

the environmental impacts with a focus on GHG emissions and improving the thermal comfort by

246

reducing the unsatisfied hours of occupants. The multi-objective optimization is applied to extract

247

the Pareto front for the dwelling archetype by Non-Dominated Sorting Genetic Algorithm II

248

(NSGA-II) algorithm. NSGA-II as a modified version of the genetic algorithm is introduced as one

249

of the most efficient tools to solve multi-objective optimization of energy performance problems

250

[18]. This multi-objective optimization methodology is executed by JEPlus and JEPlus-EA to

251

achieve feasible solutions with respect to the constraints of context and interaction of variables. The

252

optimisation process is conducted in JEPlus-EA as a powerful Graphical user interface (GUI) for

253

EnergyPlus and TRNSYS. JEPlus-EA that uses highly efficient and versatile multi-objective

254

optimisation algorithms (based on the popular NSGA-II) is employed to work on all types of

255

optimisation problems. Like any other GA, this is based on the evolution of a population of

256

individuals, each of which is a solution to the optimization problem. EnergyPlus is used to simulate

257

the building energy behaviour and then the results are imported to the optimization algorithm in the

258

JEPlus and JEPlus-EA. Figure 3 shows the overall optimization process from beginning to reporting

259

the optimal scenarios (Fig. 3).

Optimisation procedure

11

260 261 262

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

263

4- Result and Discussion

264

4-1-

265

In this section, dwelling archetype is developed based on the real data collection and surveying the

266

building characteristics of the selected case study. As the retrofit investigation is subjected to the

267

existing buildings with poor energy performance, this archetype is the most appropriate case based

268

on the high rate of energy consumption. The considerable population of this district affirms the

269

urgent attention for reducing the energy consumption. However, the processes and methodology

270

used are highly repeatable and applicable in different building categories situated in any given

271

location.

272

During these decades, this city has experienced rapid growth and intensive urbanization [53]. The

273

urban morphological analysis shows that three types of residential archetypes can be recognized as

274

the main dwelling types [54]. The selected archetype model which is representative of a significant

275

portion of the dwelling stock is constructed more than 30 years ago (Fig. 4).

Base Case Development

12

276 277 278

Figure 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.

279

The dwelling archetype is located in the Yazd city in the centre part of Iran. Based on the Köppen

280

classification, Yazd is categorized as a hot desert climate (BWh) [55]. The latitude of this city

281

is 31.89, and the longitude is 54.35. The constant high temperature during summer and cold weather

282

in winter with the high difference between the maximum and minimum temperature during a day

283

are the most important characteristics of this climate. Low rate of precipitations and high rate of

284

evaporation in summer are of two important factors which make this province one of the driest

285

areas of Iran [56]. The results of hourly weather data analysis show that the maximum and

286

minimum temperature points happen on the 18th of August for 42.3 °C and 3rd of January for -7 °C

287

respectively (Fig. 5).

13

Figure 5a

Figure 5b

Figure 5c 288 289 290 291

Figure 5- The diagram depicts the most important features of the weather condition of base case archetype. Figure 3a shows maximum, minimum and average outdoor dry bulb temperature (°C), 3b depicts the amount of direct normal solar and diffuse horizontal solar gain, figure 3c shows the wind speed (m/s).

292

This dwelling archetype is a south-facing single-family house attached to two houses with similar

293

height in the west and east sides. The total net floor area of the base case model is 260 m2 (Table 1).

294

The dwelling archetype with an average window to wall ratio of 0.4, 0.3, 0.0, and 0.0 in south,

295

north, east, and west respectively was constructed more than 30 years ago. The base case model has

296

single-layer windows and non-insulated exterior and interior walls, only the roof has been insulated,

297

resulting in poor energy performance. Windows have one single layer of 4mm flat glass with metal

298

frames resulting in U value of 3.1 (W/m2 K) (Table 1).

299

The heating system is provided locally by hot water radiators working with natural gas boilers. The

300

overall seasonal COP/efficiency of the heating system is about 0.8 without automatic regulations.

301

The cooling system is provided by low efficient evaporative coolers with ducted ventilation used for

302

about 4 months of the year. The buildings heating and cooling systems were controlled by heating

303

set point: 22◦C and heating setback 12◦C and cooling setpoint: 24◦C and cooling setback of 28◦C.

304

The heating schedules are set on for November to March and the cooling schedule is on during June

305

to September from 8 am to 6 pm. These values were considered to be the same over the analysis

306

period (Table 1).

307

Occupancy pattern and schedules for HVAC and lighting system are based on real data from

308

building reference case. The fluorescent lighting system is defined for the living room and

309

bedrooms (Table 1). In order to consider the heat gain from home appliances and other equipment,

310

their operation schedule is assumed to the default definition of each function [57].

14

311

Table 1- Short description of input data for the reference archetype Building function

Orientation

Numbe

Building

Buildin

Building

r of

length

g width

height

floors

[m]

[m]

[m]

1

19

15

3

Plot

U value

ratio*

(W/m2 K)

Residential– Single family

South

Wall

Window

Roof

0.51

3.1

0.72

0.65

house

312

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

313

After data gathering and building survey, the modelling process is done by Designbuilder V5 as a

314

multi-zone residential building including all typical spaces [57]. Once the modelling process is

315

complete, the model is exported to EnergyPlus to execute the annual energy consumption for each

316

retrofit scenario. The annual energy usage of the residential archetype is divided to four main

317

subcategories: heating system, Domestic Hot Water (DHW), electric usage for the cooling system

318

and minor equipment and finally lighting system (Fig. 6).

319

The results adopted by energy simulation shows that consumption is heavily dominated by space

320

heating and cooling requirements. The results of energy simulation are compared to the real data

321

from bills to be assured of the accuracy of the research. In the next step, the most applicable retrofit

322

actions are selected to be implemented in building with a focus on the improvement of energy

323

consumption and GHG emissions. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Jan

Feb Mar Apr May Jun Heating/DHW

Cooling

July Aug Sep

Oct Nov Dec

Lighting/Room Electricity

324 325 326

Figure 6- Monthly energy consumption profile of the archetype shows the significant portion of space heating and cooling in energy consumption.

327

4-2-

328

The definition of a retrofit scenario relies upon available actions, special characteristics of the

329

context and the existing constraint for resources. In order to identify the refurbishment scenarios,

330

seven categories of retrofit actions are identified including exterior walls, floor, windows, roof,

331

HVAC system, airtightness, and lighting system (Table 2). The energy simulation is conducted in

Retrofit Scenario Application

15

332

the EnergyPlus as open-source free software, developed by the US Department of Energy [25] with

333

IWEC (International Weather for Energy Calculations) weather files [58].

334

4-3-

335

In this section, the sensitivity analysis is used to determine the most important retrofit scenarios

336

among all available retrofit actions. In this regard, only the most effective retrofit measures are

337

entered into the optimization process to reduce the required tests and save time. Consequently, less

338

significant factors are eliminated form optimization algorithm [14]. In this research, DOE is used to

339

conduct the sensitivity analysis for retrofit scenarios.

340

In this analysis, energy consumption is considered as main objective and retrofit alternatives are

341

input variables. Annual energy simulation conducted by EnergyPlus determines the energy

342

performance of dwelling for each scenario based on the hourly weather condition files. For

343

calculating the impact of design variables in DOE, a number of levels should be defined for each

344

run of the experiment. Each level explains the input value of the retrofit alternative for separate

345

experiments. In order to obtain a sufficient resolution that how to retrofit scenarios affect the energy

346

performance of residential buildings, each input parameter is simulated at specific ranges (Table 2).

347

By performing analysis, the differences between response variables would be assigned to the design

348

variables and their interaction [59].

349 350

Table 2- The range of possible values for defined levels of input variables for application in DOE analysis Cate gory

Sensitivity Analysis for Retrofit Scenarios

Location

Description

Abbreviate of Action

Altering Factor

Levels/ Ranges No EXIi 1-EWI1

Exterior walls

Thermal performance improvement with sufficient insulation

External Wall Insulation (EWI)

2-EWI2

Dimension:

3-EWI3

Thickness

4- EWI4

Construction

5- EWI5 6- EWI6 High EXIi+n No FIi 1-FI1 2-FI2

Floor

Implementation of mixed layers of insulation

Floor Insulation (FI)

Dimension:

3-FI3

Thickness

4- FI4 5- FI5 6- F6 High FIi+n

16

Windows

No WRi

Window Replacement

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

(WR)

Element:

1-WR1

Replacement

2-WR2 High WRi+n No WRi 1-RI1

Application of roof insulation

Roof

Roof Insulation

Dimension:

(RI)

Thickness

2-RI2 3- RI3 4- RI4 5- RI5 High WRi+n Very Poor ARi

Element: Building envelope

Infiltration reduction

1-AR1

Replacement/ Adding new item

Airtightness (AR)

2-AR2 3- AR3 Excellent ARi+n

Equipment/ Technology

HVAC system

Temperature Set Points

Changing the ideal temperature of occupants

HVAC system

(TS)

Operation Schedule

Improving occupants’ behaviour

(OS)

Ideal status TSi

Building Management system (BMS):

2-TS2

Change schedule

Economy Status TSi+n

Building management system (BMS):

1-OS1

Change schedule

Economy Status OSi+n

1-TS1

Ideal status OSi

2-OS2

Low Standard ALi

Element: Lighting system

Artificial Lighting

Application of Highperformance lighting system

1-AL1

Replacement/ Adding new item

(AL)

2- AL2 3- AL3 High Standard Ali+n

and

351

Where

are the numbers of values in the upper and lower parts of the domain of the input

352

variable;

353

parts of the domain of the input variable (Eq. 1 & 2). Where

354

output variables, respectively, and

355

output variable in the upper and lower parts of the domain of the input variable [60] (Eq. 3 & 4).

356

The results of student t-test reflect the magnitude, direction, and contribution of each action to the

357

objective.

and

are the means of the values for the response parameters in the upper and lower

Equation 1 =

|

−

1

+

and

|

=

(

− 1)

+

+(

are the upper and lower

are the variances of the population for the response

Equation 2 1

and

Equation 3

− 1) −2

17

=

∑(

− ) −1

Equation 4 =

∑(

− ) −1

358

As all the retrofit actions improve energy performance since the direction is positive for these

359

actions. DOE results obtained from student t-test indicate that AL, EWI, AR, and RI are the most

360

effective variables (Table 3).

361 362

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

16343

1.28

3

AR

14478

1.14

4

FI

9782

1.13

5

TS

1212

0.14

8

OS

7208

0.82

6

EWI

15860

1.89

1

RI

11886

1.38

2

WR

4566

0.52

7

363 364

The results of table 3 affirm that EWI has the most significant impact on reducing energy

365

consumption. Changing the value of EWI variable from range EW1 (the lowest level of retrofit

366

action) to EW6 range (the highest level of retrofit action) results in the most significant changes in

367

the response variable. While RI alters from RI1 (the lowest level of insulating) to RI5 (the highest

368

level of insulating), the building performs better significantly from the energy performance point of

369

view. The impact of AL on the archetype energy performance is about 68% of EWI effect and 1.13

370

time more significant in comparison to FI. These four parameters account for about more than 50%

371

of the energy performance of the base case model (Fig. 7).

372

373 374 375

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

18

376

This large contribution of EWI is related to the two points: (1) There a is a considerable number of

377

outdoor walls with the poor condition in south and north façade of this building; (2) The poor

378

thermal conductivity condition of outdoor walls in the base case model. Other studies have also

379

affirmed the impact of EWI in reducing energy loss and improving the rate of building in energy

380

assessment ranking [61,62]. The energy performance of the building is highly influenced by the

381

insulation of exterior walls especially in extreme cold/hot climates [63].

382

The results indicate that RI is the next important parameter in this hierarchy. Retrofitting the roofs

383

results in higher saving rates of energy [64], especially in a one-story building. Since the reference

384

model has one level, all the ceilings have outdoor surfaces leading in more energy loss. In addition

385

to the significant contribution of EWI and RI, they are considered as cost-effective methods for

386

residential buildings [63,65].

387

The third effective factor is AR which involves different parts of buildings. The level of airtightness

388

is defined by the crack template of EnergyPlus for building envelope including walls, openings,

389

roofs and also interior elements such as partitions [25]. Different studies have affirmed the

390

significant impacts of airtightness on ventilation rates and energy consumption. Results have

391

revealed that AR is highly affected by heating and cooling controlling systems and weather

392

condition [63]. In a retrofit study, the contribution of AR to the reduction of energy consumption is

393

reported by one third [65]. FI as another parameter with significant impact on the response variable

394

[66] has similar specifications to FI, EWI, and RI because of the insulation regulations and

395

construction details.

396

According to statistical analysis, the four parameters of AL, OS, WR, and TS have less contribution

397

to the response variable. However, their significant size is not neglectable (Table 3). For instance,

398

AL is an important parameter in the hierarchy of contribution to an energy reduction of this

399

archetype. In other literature, replacing the efficient lighting system is one of the most effective

400

methods to improve the energy performance of the buildings [67,68]. However, replacing old low

401

standard lighting systems with more high standard systems such as fluorescent or LED requires a

402

significant budget and consequently increases the cost of scenario [69]. Improving the energy

403

efficiency by means of lighting system incorporates taking advantage of day-lighting, sun shading

404

system and using high-performance lighting system with intelligent control systems.

405

4-4-

406

In the previous step, all the parameters were analysed through a sorting process resulting in two

407

categories of inputs. The first category includes four parameters of EWI, RI, FI, and AR as altering

408

factors controlling the optimization process toward the objective function. The second group

409

includes parameters of AL, WR, OS, and TS with less significance. These parameters are assigned

Optimization Procedure

19

410

typical values and considered constant for entering the optimization process for calculating the

411

thermal comfort quality.

412

As mentioned above, the optimization algorithm is conducted by a combination of three tools

413

including EnergyPlus, JEPlus, and JEPlus-EA [70]. JEPlus is used for running parametric analysis

414

with EnergyPlus IDF files and the optimization algorithm is performed in JEPlus-EA which

415

receives the variables from JEPlus parameter tree (Fig. 3). For running the multi-objective genetic

416

optimization of NSGA-II, the population size of 20 and maximum generation of 30 are selected

417

based on the previous studies to achieve the best trade-off between Pareto front and computational

418

time [18].

419

4-4-1-

420

The objective function is written in the command script files of JEPlus project. Two objectives of

421

the environmental impacts of the base case model and thermal comfort of occupants are considered

422

as the response variable for the optimization process. The first objective is defined to be minimized

423

and thermal comfort as the second objective has to be maximized. In the objective function, the

424

balancing factor between these two objectives controls the optimization algorithm [14].

425

In this model, the simultaneous optimization of CO2 emission and thermal comfort hours is sought.

426

The CO2 emission and total discomfort ours of the building are directly assessed by EnergyPlus.

427

The CE and DS are defined to represent the amount of Carbon Emission in kilogram and the total

Objective Function

428

amount of discomfort hours. In this expression, the c(x) values are based on the energy

429

consumption of different parts of building such as domestic hot water, heating and cooling, etc. The

430

metric used to assess thermal discomfort is the standard Predicted Mean Vote (PMV), based on

431 432 433

Fanger’s mode. This value is calculated for each individual zone (d(z)) and summed up to represent the total discomfort hours (DC(x))

CE(x) = ∑ !" c (x)

434

DC(x) = ∑#!" d# (x)

435

In these formulas, ‘i’ is related to the sources of energy consumption and ‘z’ shows different zones

436

in the building. The final goal of the optimization problem in this phase is to find the optimize

437

condition where both carbon emission and the total amount of discomfort hours are minimum. An

438

optimisation algorithm is used to assess this multi-objective-optimization problem and identify the

439

set of non-dominated solutions. For this purpose, we have defined a new objective where is a

440

combination of CE and DS for each retrofit scenario (x), weighted equally (p and p') in our

441

calculation. Obj(x) = p CE(x) + p' DS(x)

442

Where p + p' = 1 and p = p' = 0.5 20

443

4-4-2-

444

To calibrate the design scenarios, a framework is defined in which the less important retrofit actions

445

are fixed in the average level and the more effective factors alter through the optimization process

446

(Fig. 8). The design variables are a set of retrofitting alternatives of EWI, RI, FI, and AR which are

447

considered as the altering parameters for different scenarios (Fig. 8). Four parameters of OS, TS,

448

WR, and AL are fixed as preliminary actions with typical values. OS and TS are fixed on their post-

449

retrofit conditions to improve energy performance. AL is also fixed on the average level. WR for

450

double glazed windows is applied as the basic action based on the easy applicability and availability

451

of this method in the context (Fig. 8).

452 453 454 455 456

Figure 8- This diagram depicts the optimization process applied to propose the most efficient solutions to minimize 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

457

The range of input variables for the definition of retrofit alternatives is presented in table 4. To

458

ensure the reliability of the results, the steps are defined in various ranges to cover all possible

459

solutions (Table 4).

Design Variables

21

460 461

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

Design Variable

Abbreviate of Action

1

External wall insulation

EWI

Roof Insulation

RI

Floor insulation

FI

Airtightness

AR

2 3

4

Minimum and Maximum of each variable

Altering step

- Insulation thickness (m)

0.05:0.60

0.005

- Insulation thickness (m)

0.034: 0.639

0.005

0.100: 0.700 0.130: 0.490 0.02: 0.00014 0.0022: 0.0001 0.00037: 0.0001

0.005 0.003 0.001 0.0001 0.00001

Altering parameters for the definition of variable

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

462

The results of the optimization algorithm are analysed in different ways based on the specific

463

approaches and goals of the research methodology. In fact, extracting the general trends is one of

464

the important outputs to understand the pattern and behaviour of the model. Figure 9 illustrates the

465

general trend of the optimization progress. During this process, based on the selected population

466

size and generation number, the algorithm finds the building conditions with the best performance

467

regarding the selected objectives.

468 469

Figure 9- The diagram illustrates the simulated points during the optimization process at different

470

building conditions. Each column height represents the number of selected points, where the

471

calculated carbon emission is in the range of appropriate plotted X values (e.g. 7000 < CO2 < 8000

472

kg). The average total discomfort hours of the selected models are also presented with black

473

diamonds.

474

Thus, as it can be seen, the number of selected points with lower carbon dioxide emission (less than

475

8000 Kg CO2) is significantly higher in comparison with other simulated points (Fig. 9). In

476

addition, the result proves that the minimum value of total discomfort hours is always coupled with 22

477

the lowest possible values for CO2 emission. It means that a building with low carbon emission

478

simultaneously can improve thermal comfort condition. In other words, the increasing trend of

479

thermal discomfort with the higher value of the CO2 production depicts the coupling between these

480

two objectives.

481

Figure 10 displays some of the optimized solutions in the non-dominated frontier resulting in

482

different GHG emission level and thermal comfort conditions. The optimization analysis shows that

483

the whole range of discomfort hours varies from 13867 to 19139 and the produced CO2 changes

484

from 7109 to 17749 kg in one year of simulation (Fig. 10). Further increasing in annual carbon

485

emission leads to a higher rate of total discomfort hours confirming that these objectives can be

486

minimized concurrently. Additionally, the slope of the curve in figure 10 depicts that the total

487

discomfort hour is much more effective in the objective function.

488 489 490

Figure 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.

491

Choosing each solution results in various trade-offs between GHG emissions and thermal comfort

492

of residents. The most important function of this algorithm is to control the balance between the

493

allowance of fresh air entrance and thermal transmittance of construction variables. Fresh air

494

entrance results in improvement of occupants’ thermal comfort; while thermal transmittance of

495

construction variables affects the energy performance and GHG emissions. Therefore, if more

496

retrofit actions are applied, the result will not be in the acceptable range for occupants’ thermal

497

comfort. However, the level of energy consumption is significantly increased because of the poor

498

level of airtightness. Therefore, it seems that other parameters such as mechanical ventilation

499

systems can play a paramount role in changing this balance [71].

23

500

It is worthwhile to mention that the limited number of optimized solutions in the Pareto front is

501

based on the two points. First, the range of input variables is defined based on the expert wisdom

502

showing the possibility for application in the real base case model. Consequently, impossible

503

solutions are automatically eliminated from the procedure. Second, the similar correspondence of

504

objectives results in a lower number of generated solutions in comparison to those with many

505

conflicts and different characteristics [14].

506

Analysis of optimization results demonstrates that in the optimized solution, the total discomfort

507

hours will be around 13867 hours; while the amount GHG emission is 7117 Kg for one year. This

508

result happens in a specific combination of design variables. A deeper view to input variables shows

509

that value of 0.275 for EWI results in the minimum environmental effects and maximum thermal

510

comfort of interior places. Meanwhile, the combined thickness of FI is reported for a total value of

511

.120 and 0. 142. The RI as another significant parameter is assigned to the value of 0.18. The

512

important role of AR is also based on the fact that this parameter incorporates various elements of

513

building envelope and interior components. In this case, the reported values for crack template of

514

building envelope include three subcategories of air mass flow coefficient for windows, walls and

515

roofs as following: 0.00014 Kg/s.m crack @ 1Pa, 0.0001 Kg/s.m2 crack @ 1Pa and 0.0001 Kg/s.m2

516

crack @ 1Pa respectively. This balance ensures the provision of a healthy and productive

517

environment for interior residential places.

518

5- Conclusion

519

A novel multi-objective optimization method using GA coupling with sensitivity analysis is applied

520

in a residential archetype. This archetype is located in a hot climate requiring heating and cooling

521

systems for most of the year. The low energy performance of this archetype emphasizes the

522

necessity for the refurbishment project. However, the side effects of retrofit projects have to be

523

monitored especially for improving the health and wellbeing of occupants. This research explores

524

the significance of various retrofit scenarios which are common in the context of the project.

525

Application of the proposed approach demonstrates an energy-saving method without any

526

dissatisfying effects on the thermal comfort of occupants. Then, it seems necessary for national

527

energy performance standards to include comfort analysis as a consequence of a retrofit project.

528

Since the required time for the calculation of the GA optimization process is significant, a unique

529

methodology is provided to calibrate retrofit scenarios. At the first step, eight retrofit measures are

530

selected by reviewing the related literature and availability of actions in the context. To reduce the

531

required iterations for the optimization process, a sensitivity analysis is conducted to reduce the

532

retrofit measures by defining the most effective ones based on the climate and morphological

533

conditions. Consequently, less important measures are identified by DOE and then, they are fixed

534

on their typical values. In this regard, four less significant parameters are eliminated and others 24

535

were entered into the optimization process. The input parameters for finding optimized solutions

536

include EXI, RI, FI, and AR. In this way, the total computational time associated with the GA is

537

reduced considerably for improving two objectives of GHG emissions and thermal comfort

538

simultaneously.

539

The analysis of results demonstrates how physical characteristics of energy efficiency measures

540

simultaneously affect the decision-making objectives. Therefore, by including more objectives in

541

the process, more diverse energy-efficient solutions will be provided that facilitate the decision

542

making the process. Investigation of optimized solutions reveals that airtightness is a significant

543

parameter for controlling the results compared to the three other variables. This balance ensures the

544

provision of a healthy and productive environment for interior residential places.

545

As a result of this research, combining the PSA at the early stages of this algorithm will assist to

546

facilitate the optimisation process and investigate the comfort-related interaction of retrofit actions.

547

The most influential input parameters will be used in order to form a minimum set of accurately

548

defined input data for conduction optimisation algorithms. Furthermore, the minimum data set can

549

be used in order to add some objectivity to the decisions made regarding input data assumptions and

550

simplifications, ultimately leading to increased optimisation accuracy and/or decreased optimisation

551

time.

552

Further research has to focus on investigation for other interventions of retrofit measures on quality

553

of spaces and occupants’ behaviours such as indoor air quality. These metrics should be analysed

554

based on the climate and context specifications and morphological parameters. Implementation of

555

such studies in offices is critical due to the high rate of energy consumption and considerable time

556

of people attending in interior places which requires great attention for comfort-oriented retrofit

557

projects. Furthermore, the results from this research can be used as a necessary input for future

558

studies to investigate the cost of retrofitting actions and accordingly all the dimensions of

559

environment, cost and comfort will be included in the energy-oriented retrofit planning.

560

25

561

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• • • •

Considering occupants' thermal comfort as an integrated part of retrofitting Role of retrofitting in thermal comfort without increasing environmental impacts Facilitating optimisation algorithm by incorporating parametric sensitivity analysis Developing a multi-objective optimization method using Genetic Algorithm

School of Mechanical and Materials Engineering University College Dublin, Belfield, Dublin 4, Ireland E-mail: [email protected] 21/08/2019

Dear Prof Brito,

There is no conflict of interest or funding to declare for this specific research.

Yours sincerely, Reihaneh Aghamolaei PhD candidate School of Urban planning; College of Fine arts; University of Tehran; Tehran, I.R. IRAN P.O.Box: 14155-6619 +98(21) 61113411 [email protected] [email protected]