A process for developing deep energy retrofit strategies for single-family housing typologies: Three Toronto case studies

Accepted Manuscript Title: A Process for Developing Deep Energy Retrofit Strategies for Single-Family Housing Typologies: Three Toronto Case Studies Author: Denver Jermyn Russell Richman PII: DOI: Reference:

S0378-7788(16)30022-6 http://dx.doi.org/doi:10.1016/j.enbuild.2016.01.022 ENB 6395

To appear in:

ENB

Received date: Revised date: Accepted date:

23-2-2015 15-1-2016 17-1-2016

Please cite this article as: D. Jermyn, A Process for Developing Deep Energy Retrofit Strategies for Single-Family Housing Typologies: Three Toronto Case Studies, Energy and Buildings (2016), http://dx.doi.org/10.1016/j.enbuild.2016.01.022 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. 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.

*Highlights

A Brute Force method for retrofit optimization for single family homes. Retrofit strategies to achieve two EUI levels for three archetypes. Local costs for retrofits were gathered to support the optimization process.

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*Manuscript Click here to view linked References

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Denver Jermyn – [email protected] Russell Richman1 – [email protected] Ryerson University, 350 Victoria Street, Toronto, ON M5B 2K3, Canada.

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A Process for Developing Deep Energy Retrofit Strategies for SingleFamily Housing Typologies: Three Toronto Case Studies

Abstract

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Energy consumption of existing single family homes in cold climate urban centers needs to be reduced. This research presents a process for developing and analyzing retrofit strategies for

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specific housing archetypes using Toronto (Canada) as a case study. The process was applied to three Toronto urban archetypes with two separate energy intensity goals for heating and cooling:

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(1) 75 kWh/m2 and (2) Passive House EnerPHit estimated equivalency. Building data was collected through field study and calibrated baseline energy models (EnergyPlus) were created. Retrofit strategies were identified and costs were estimated in consultancy with several

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experienced Toronto based retrofit contractors. The process utilized a Brute Force method for

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retrofit selection considering the cost/benefit of each strategy. Furnace and select building envelope parameters were shown to be priorities while windows were not. Energy use reductions

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of 64-67% and 88-89% from a baseline were achieved when meeting the 75 kWh/m2 and EnerPHit equivalency targets, respectively. The capital costs of $30,000-$80,000 to achieve the retrofit targets are likely to be prohibitive for homeowners, suggesting that a government funded program is necessary to achieve deep energy retrofits of this nature and to ensure early adoption leading to wide spread market growth.

Keywords:

Residential Buildings; Deep Energy Retrofits; Cost Estimation; Building Envelope; HVAC Energy Efficiency; Archetypes; Calibrated Building Energy Simulation; EnergyPlus.

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Corresponding author. [email protected], +1 (416) 979 5000 ext. 6489, 350 Victoria Street, Toronto, Ontario, Canada, M5B 2K3

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List of Abbreviations: ACH – Air Changes per Hour

CMHC – Canada Mortgage and Housing Corporation

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CVRMSE – Coefficient of Variation of the Root Mean Squared Error

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ASHRAE – American Society of Heating, Refrigeration, and Air Conditioning Engineers

DOE – United States Department of Energy

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HDD – Heating Degree Days

HVAC – Heating Ventilation and Air Conditioning System

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kWh – kilowatt-hour NMBE – Normalized Mean Bias Error

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OBC – Ontario Building Code

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TFA – Treated Floor Area

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RSI – Metric unit for resistance to heat transfer (R-value)

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1. Introduction Concerns surrounding climate change and the rising cost of energy have fuelled public interest in energy conservation and efficiency. Residential buildings are responsible for 17% of

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total energy consumed and 15% of total greenhouse gas emissions in Canada (Natural Resources

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Canada, 2012). In the Canadian residential sector, single-detached dwellings have been shown to use 1.8 times more energy than apartment dwellings on a per capita basis (Norman et. al., 2006).

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This is due to the greater number of exterior walls in detached dwellings compared to apartments, which typically have fewer exterior walls per unit. While it is generally accepted that

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new buildings are more energy efficient than their predecessors (VandeWeghe & Kennedy,

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2007), existing buildings have a long life span and a low rate of replacement (Foley, 2012). Therefore there is a need to address the high energy consumption of existing residential

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buildings, focusing on single family homes. Ma et. al. (2012) concluded in their study that the

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“retrofitting of existing buildings offers significant opportunities for reducing global energy consumption and greenhouse gas emissions”. Pitt et. al. (2012) stressed the need for building

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retrofits asserting that “evidence suggests that energy-efficiency retrofits to existing buildings represent the biggest, fastest, cheapest, cleanest, and most long-lasting opportunity to reduce energy use and greenhouse gas emissions in cities”. With 57% of energy consumed in Canadian households being used for space heating (Centre for Energy, 2013), it is prudent to focus retrofits on reducing heat loss through building envelope upgrades and on improving mechanical systems to increase efficiency and match the reduced heating requirements of a retrofitted building envelope (Harvey 2009, CMHC 2008, Badescu & Sicre 2003, Urquhart et. al. 2014, Touchie & Pressnail 2014).

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Evaluating the large array of building retrofit options and strategies available, as well as the relationship between the strategies, presents a problem of multi-objective optimization (Diakaki et. al. 2008, Verbeeck & Hens 2007). To undertake optimization, Rysanek and

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Choudhary (2013) recommended building energy modelling and concluded in their review of building retrofit literature that building energy modelling is the benchmark by which researchers

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investigate the full range of retrofit strategies. Several multi-objective optimization software

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programs have been developed as front end applications to building energy modelling software, for example BeOPT and OptEPlus for the EnergyPlus program (National Renewable Energy

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Laboratory 2010, National Renewable Energy Laboratory 2014). These programs perform an automated optimization utilizing a Brute Force method in which every retrofit possibility from a

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defined list is carried out and analyzed against the listed project goals. However, both

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Eisenhower et. al. (2012) and Asadi et. al. (2012), indicated that these types of software become

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impractical when a large number of retrofit design variables are utilized due to the computational time required to carry out all of the building energy simulations. Therefore alternative retrofit

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decision making processes utilizing a systematic and multi-criteria assessment are required (Aste & Del Pero 2013, Flourentzou & Roulet 2002). For the purpose of this paper, deep energy retrofits are defined as whole building retrofits, including building envelope and HVAC system retrofits, for achieving significant reductions in energy intensity (annual energy consumption per unit of floor area). The main objective of this research is to present a process through which to develop and analyze deep energy retrofits for specific housing archetypes or typologies utilizing building energy modelling. The process includes a Brute Force Sequential Search method for retrofit decision making intending to simplify the multi-criteria assessment decision making process and address the time constraint.

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The process is meant to be customizable and repeatable allowing it to be applied to a variety of housing types and allowing different retrofitting goals to be incorporated. The process is aimed at being applicable for researchers to study building retrofits with the aim of moving towards a

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process that can be utilized by homeowners to develop retrofits for their homes. This paper

explores the development of the process through three case study housing archetypes in the City

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of Toronto (Canada) and is organized into three main sections: (1) developing retrofit strategies

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and estimating the associated costs, (2) determining the required retrofits to achieve specified energy intensity targets for heating and cooling, and (3) estimating the cost implications of

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meeting the performance targets.

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2. Case Study Archetypes

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Three case study archetypes in the City of Toronto are utilized to demonstrate the

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process: the Century Home and War Time Home, as developed in the study by Blaszak and Richman (2013), as well as a semi-detached Century Home (Century-Semi) developed by

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Jermyn (2014). These housing types are prevalent in the City of Toronto and on average tend to be the least energy efficient among Toronto archetypes (Jermyn, 2014).

Figure 1: Century Home Archetype

Figure 2: War Time Home Archetype

(Adapted from Blaszak & Richman, 2013)

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Typical building geometry, features, envelope constructions, air tightness, and HVAC systems, as determined by Blaszak and Richman (2013), were confirmed, updated, and expanded

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in this research through a field study of the three Toronto archetypes. To inform the heating and cooling energy intensity targets for the building retrofits, high

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performance buildings were reviewed in the literature (Bassett & Shandas 2010, Foley 2012, Passive House Institute 2013, Wagner 2012). From these studies, it was determined that a

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minimum 50% reduction in energy use is often targeted for retrofitting. Blaszak and Richman

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(2013), utilizing data obtained from the national Canadian EcoENERGY Database (Natural Resources Canada, 2010), provided an average energy intensity of 204 kWh/m2 for Toronto

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homes, therefore a 50% reduction in energy use would be 100 kWh/m2. This study aimed to improve on the typical 50% reduction target by setting an initial target of 75 kWh/m2 for heating

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and cooling representing approximately a 63% reduction.

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The second, more aggressive target, was inspired by the Passive House EnerPHit

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standard requiring a heating and cooling maximum energy intensity of 25 kWh/m2yr of treated floor area2 and an air leakage rate of 1 ACH50 (Passive House Institute, 2012). This target was chosen as the authors are familiar with this standard and are involved in the adoption of the standard for North America. The standard offers an aggressive, but achievable target for the Toronto climate. As certification is required to officially achieve the EnerPHit standard, this study aims to achieve an estimated equivalency to the EnerPHit standard, based on the authors’ Passive House training and experience, further denoted as “EnerPHitEQUIV” for the purpose of the research.

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Treated floor area is a calculation based on the floor area of a building to approximate the actual heated living space (Passive House Institute, 2013)

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3. Archetype Baseline Modelling Baseline energy models for the Century Home, Century-Semi Home, and War Time

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Home were constructed using EnergyPlus (DOE, 2015) The baseline energy models were prepared in a similar manner utilizing the same

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simulation parameters, run times, calculations methods, and weather file. The internal

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environmental conditions, including heating schedules and set points, as well as internal heat gains were constant in the three models and occupant behavioural factors were not considered.

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Internal heat gains from domestic hot water, appliances, lighting, and the presence of occupants were modelled based on typical usage schedules, as developed by Zirnhelt (2013). Each of the

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models utilized a two minute time step (30 calculations per hour) for calculating building energy use over a one year period. Once the baseline models were created, building specific factors (e.g.

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geometry, envelope constructions, air tightness, and HVAC systems) were applied to align the

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models with the field data collected for each archetype. This procedure ensured that only

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building specific factors influenced the modelled energy intensity of the archetypes and removed variations arising from the modelling methodology. The models were constructed utilizing the calibrated model procedure developed by Zirnhelt (2013) and the building specific parameters, determined through field study, outlined in Table 1.

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Table 1: Building Specific Parameters for Baseline Energy Models Century-Semi

War Time

Reference

250.9m2 5.8m x 13.4m

165.9m2 5.1m x 13.7m

131.7m2 6.5m x 12.8m

Field Study Field Study

1 Rectangular 1940-60 Driveway on one side

Field Study Field Study Blaszak and Richman (2013) Field Study

Half-width porch Gable Light-wood frame Brick

Field Study Field Study Blaszak and Richman (2013) Field Study

3 2.5 Rectangular L-shape <1940 <1940 Adjacent to Neighbours Adjacent to Neighbours Finished attic, full-width Finished attic, full-width porch porch Gable Gable front, flat rear Double-wythe brick Double-wythe brick Brick Brick 2.64 1.01 0.55

2.64 1.01 0.55

3.58 1.65 0.83

Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013)

Shingle OSB 100mm Fibreglass Lath and Plaster

Shingle OSB 100mm Fibreglass Lath and Plaster

Shingle 150mm Fibreglass Gypsum

Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013)

Double Wythe Brick 20mm Air Space 25mm Fibreglass Lath and Plaster

Double Wyth Brick 20mm Air Space 25mm Fibreglass Lath and Plaster

Brick 20mm Air Space OSB 50mm Fibreglass Gypsum

Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013)

Quadruple Wythe Brick

Quadruple Wythe Brick

300mm Concrete 25mm Fibreglass Gypsum 8.17

Blaszak and Richman (2013) Blaszak and Richman (2013) Blaszak and Richman (2013) EcoENERGY Database

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Wall Assembly

Foundation Assembly

31.4% 15.0% 11.7% 8.6%

Field Study Field Study Field Study Field Study Field Study Field Study Field Study Field Study

1.3m3/s

1.0m3/s

Field Study

1.9 and 3.07 W/m2 Kitchen/Hot Water 4 People

1.9 and 3.07 W/m2 Kitchen/Hot Water 4 People

Zirnhelt (2013) Field Study Field Study

31.4% 15.0% 11.7% 8.6%

31.4% 15.0% 11.7% 8.6%

Qasass et al. (2014) Zirnhelt (2013) Qasass et al. (2014) Qasass et al. (2014)

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1.9 and 3.07 W/m2 Kitchen/Hot Water 4 People

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Lighting Appliances Occupancy Framing Factor Exterior Wall Interior Wall Floor Roof

1.3m3/s

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Air Leakage (ACH 50Pa) 10.54 11.7 Glazing (%) Front 19 15.6 15.8 Side 5.7 3.6 5.4 Side 3.2 N/A 3.6 Rear 15.6 10.8 20.1 Window Type Double Glazed Air Filled Double Glazed Air Filled Double Glazed Air Filled Door Type Insulated Insulated Insulated HVAC System Type Forced Air Gas Forced Air Gas Forced Air Gas Controls Location Dining Room Main Floor Living Room Vent Flow Rate Internal Gains

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Features Roof Structure Cladding Insulation RSI Ceiling Walls Foundation Ceiling Assembly

Century

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Heated Floor Area Footprint Building # Storeys Plan Shape Vintage Lot Placement

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Archetypes

As the archetypes are meant to represent typical Toronto urban single family residential buildings, there were no specific site related factors utilized when creating the energy models. For this reason several parameters were fixed in the models including building orientation and shading. The buildings were assumed to be north facing. As many of the Century and CenturySemi Homes in urban Toronto are situated on narrow lots with minimal spacing between buildings, the east and west face of the buildings were assumed to be 100% shaded whereas the north and south faces were assumed to be 0% shaded. For the War Time Homes, which are typically separated by driveways, all building faces were assumed to have 0% shading.

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To calibrate the energy models monthly bills for natural gas, the primary heating fuel for the archetypes, were collected from a sample of field study homes. The homes were selected to ensure that they closely resembled the archetypes in geometry, size, and HVAC system and had

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not been altered by previous renovations or additions. The heating set point was also determined for each home to ensure that the heating set point was identical to that of the energy models. In

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total, natural gas bills were collected from two Century Homes, and two Century-Semi Homes,

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for the years 2012 and 2013.

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Natural gas billing periods in the study area do not directly correspond to calendar months and are read bi-monthly. To account for these discrepancies a normalization of the

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natural gas bill data to heating degree days was performed. Heating degree days (HDD) for central Toronto for both 2012 and 2013 (Government of Canada, 2014) were compared to both

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the energy usage of the energy models as well as the natural gas bills. Samples of this

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comparison are provided in Tables 2 and 3.

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Table 2: Energy Bill Normalization Example – Century Home 2, 2012

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Table 3: Energy Bill Normalization Example – Century-Semi Home 2, 2012

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The monthly energy usage from the energy models was divided by the monthly heating degree days which yielded a relatively consistent value of gigajoules of energy usage per degree day

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(GJ/HDD). The monthly natural gas bill data was also divided by the monthly heating degree days which yielded widely varying values by comparison. It was assumed that the gigajoules of

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energy use per heating degree day value should remain relatively constant in the natural gas bills

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(as was the case with the energy models) and therefore an average value was calculated. This

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average natural gas bill GJ/HDD (0.0339 and 0.0296 from Tables 2 and 3 respectively) was then multiplied by the number of heating degree days for each month to determine the normalized natural gas bill data. As the heating degree days represent the actual calendar month, the intent of the normalization was to bring the energy bill data in line with the calendar month and to reduce the discrepancies inherent in the estimated natural gas bills. Additionally, the normalization as presented provided estimated values where previous gaps in the natural gas bill data existed (refer to the month of October in Table 3). It should be acknowledged that the month of September presents a discrepancy as September typically had a lower GJ/HDD value than the other months. This may be the result of HVAC systems starting up late in the month after the hot

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summer or it could be the result of the homes staying warm relative to outdoor temperatures due to heat stored in the thermal mass of the buildings.

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After normalization, the energy models over-predicted energy use compared to the natural gas bills for many months by a consistent margin and calibration was achieved by fine

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tuning variables such as air tightness. The models were calibrated utilizing ASHRAE Guideline 14 (ASHRAE, 2007) which states that a model is considered calibrated when the statistical

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Normalized Mean Bias Error (NMBE) is within +/- 5% and the Coefficient of Variation of the

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Root Mean Squared Error (CVRSME) is less than or equal to 15% on a monthly basis. MBE and

(Y i − Ŷ i ) ∑ NMBE = 100∗

√

[1]

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̄ (n− 1)∗ Y

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CVRSME are calculated using Equation [1] and Equation [2] (ASHRAE, 2007).

n− 1 Ȳ

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CVRSME= 100∗

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∑ (Y i − Ŷ i )2

[2]

Where : Y = model predicted value Ŷ = actual value Ȳ = mean value of the measured data n= number of samples

The final MBE and CVRSME values for each of the calibrations are summarized in Table 4.

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-4% 0% -4% 1% -4% -5% 5% -5%

CVRSME 12% 7% 13% 7% 11% 11% 10% 11%

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Year MBE 2012 Century Home 1 2013 2012 Century Home 2 2013 2012 Century-Semi Home 1 2013 2012 Century-Semi Home 2 2013

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Table 4: MBE and CVRSME Results for Energy Model Calibrations

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The baseline energy models were meant to be an average representation of the archetypal homes and therefore the energy models utilized average or typical insulation and air tightness

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values. It is clear that these parameters vary from home to home and therefore it was reasonable

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to alter these parameters slightly to calibrate the energy model to actual home energy bill data. Although it was proven that the baseline energy models could be calibrated to individual

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homes within an archetype, the objective of this study was to model an average or typical

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archetypal home. Therefore the models carried forward included average archetype characteristics as outlined in Table 1. As the baseline energy models were created identically

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except for building specific variables that were different between the archetypes, it was reasonable to assume that the War Time Home energy model with average parameters was accurate.

The final baseline energy intensity of the models for combined heating and cooling was calculated to be 211 kWh/m2 for the Century Home, 228 kWh/m2 for the Century-Semi Home, and 209 kWh/m2 for the War Time Home.

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4. Selecting Deep Energy Retrofit Strategies This research focused on building envelope and HVAC retrofits targeted exclusively to reduce energy use. Eight categories of retrofit strategies were identified to apply to the baseline

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energy models of the archetypes: wall insulation, roof insulation, foundation wall insulation, slab

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insulation, window upgrades, air sealing, heating and cooling equipment, and ventilation energy recovery. For this research, wall, roof, foundation wall, and slab retrofits refer to increasing

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insulation levels. Window retrofits refer to decreasing overall window u-factors through adding glass layers, surface coatings, or altering the gas mixture in the glazing units. Air sealing retrofits

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refer to increasing the air tightness of the building by sealing penetrations and air leakage paths.

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Heating/cooling retrofits refer to installing alternative furnace/air conditioning systems with greater efficiencies than the baseline systems and finally, ventilation retrofits refer to installing

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heat recovery or energy recovery ventilators of various efficiencies where none were originally

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present in the baseline homes.

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For the eight retrofit categories outlined above, three levels of implementation were developed. For wall, roof, foundation wall, and slab insulation, the first level was modelled after RSI values specified by the current Ontario Building Code (OBC, 2012) and the second and third levels were modelled after high performance assemblies developed by Mucciarone (2011), as well as high RSI enclosure recommendations for Toronto’s climate zone published by Straube (2011) and Straube and Grin (2010).

The first level wall assemblies utilized closed-cell spray applied polyurethane foam (25 mm) and mineral wool insulation (140 mm). The second level wall assemblies utilized extruded polystyrene insulation (75 mm) and mineral wool insulation (140 mm) for the Century Home and Century-Semi Home and closed-cell spray applied polyurethane foam (63 mm) and blown

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cellulose insulation (165 mm) for the War Time Home. The third level wall assemblies utilized mineral wool insulation (50 mm) and blown cellulose insulation (356 mm) for the Century Home and Century-Semi Home and closed-cell spray applied polyurethane foam (63 mm) and blown

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cellulose insulation (261 mm) for the War Time Home.

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For the Century Home and Century-Semi Home roof assemblies (cathedral roofs), the first and second level assemblies utilized closed-cell spray applied polyurethane foam (25 mm

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and 100 mm respectively) and dense pack cellulose insulation (205 mm and 270 mm

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respectively). The third level assembly utilized polyisocyanurate insulation (150 mm) and dense pack cellulose insulation (230 mm). For the War Time Home (attic roof) the first, second, and

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third level roof assemblies utilized blown cellulose insulation (375 mm, 450 mm, and 563 mm respectively).

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The first, second, and third level foundation wall and slab assemblies utilized extruded

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polystyrene insulation (50 mm, 63 mm, and 76 mm respectively for the walls and 19 mm, 25

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mm, and 40 mm respectively for the slab).

Window retrofit levels were developed utilizing industry standard frame profiles along with consulting a large North American manufacturer of high efficiency windows (Ray, 2014). The overall U-factor of the retrofit windows was taken into consideration utilizing center of glass, edge of glass, frame, and divider (if applicable) U-factors. The overall U-factors were calculated utilizing an average window area of 1.4 m2 obtained from the archetype field study. Air sealing retrofits were modelled after Blaszak and Richman (2013) who developed expected reductions in ACH50 and final retrofit air sealing expectations through interviews with industry experts. Air sealing expectations included a 10% reduction in ACH50 when new

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windows were installed and a requirement for both wall and roof retrofits to be undertaken in order to reach the lowest values.

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Finally, heating, cooling, and ventilation retrofits consisted of removing the baseline furnace/air conditioning systems, with efficiencies of approximately 80% as determined through

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field study, and replacing them with the equivalent of Energy Star certified products currently on the market (Environmental Protection Agency, 2014) representing conventional, medium, and

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high efficiency levels. As no energy recovery systems were present in the baseline homes,

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ventilation retrofits involved providing a recovery system. Heat recovery ventilators (which recover only sensible heat) with varying efficiencies were utilized for the level 1 and 2 retrofits,

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whereas an energy recovery ventilator (which recovers both sensible and latent heat) with a high efficiency was utilized for the level 3 retrofit. The retrofit strategies and levels of implementation

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archetype field study.

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are summarized in Table 5. All ‘Baseline’ levels stemmed from the observations made during the

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Table 5: Retrofit Strategies and Implementation Levels Strategy Walls (RSI / U-value) Roof (RSI / U-value) Basement Walls (RSI / U-value) Slab (RSI / U-value) Windows (U-factor) Air Sealing (ACH at 50 Pa) Heating and Cooling (Efficiency) Ventilation (Efficiency)

Baseline Level 1 Level 2 Century Century-Semi War Time Century Century-Semi War Time Century Century-Semi 1.01 / 0.99 1.01 / 0.99 1.65 / 0.61 4 / 0.25 4 / 0.25 4 / 0.25 6 / 0.17 6 / 0.17 2.64 / 0.38 2.64 / 0.38 3.58 / 0.28 9 / 0.11 9 / 0.11 9 / 0.11 10.5 / 0.09 10.5 / 0.09 0.55 / 1.82 0.55 / 1.82 0.83 / 1.20 2 / 0.50 2 / 0.50 2 / 0.50 3 / 0.33 3 / 0.33 0.058 / 17.24 0.058 / 17.24 0.058 / 17.24 0.75 / 1.33 0.75 / 1.33 0.75 / 1.33 1/ 1 1/ 1 2.7 2.7 2.7 1.9 1.9 1.9 1.2 1.2 10.54 11.7 8.17 20% Reduction 20% Reduction 15% Reduction 3 3 80% 80% 80% 90% 90% 90% 94% 94% N/A N/A N/A 60% HRV 60% HRV 60% HRV 85% HRV 85% HRV

War Time 6 / 0.17 10.5 / 0.09 3 / 0.33 1/ 1 1.2 2 94% 85% HRV

Level 3 Century Century-Semi 10 / 0.10 10 / 0.10 13 / 0.08 13 / 0.08 3.5 / 0.29 3.5 / 0.29 1.75 / 0.57 1.75 / 0.57 1 1 1 1 97% 97% 80% ERV 80% ERV

War Time 10 / 0.10 13 / 0.08 3.5 / 0.29 1.75 / 0.57 1 1 97% 80% ERV

While the strategies and levels of implementation were mostly consistent for the archetypes, some differences existed between Century and Century-Semi and War Time Homes. This is due to the different existing envelopes and structures of the archetypes leading to different assemblies being required to reach the same RSI value. Additionally, air sealing expectations varied for Century and War Time Homes (Blaszak & Richman, 2013).

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5. Methodology for Estimating Capital Cost of Individual Retrofit Strategies The capital costs of the retrofit strategies were estimated using a hybrid method

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consulting both R.S. Means construction cost data (Waier et. al., 2012) and through interviews with large renovation contractors in the City of Toronto. Contractors were recruited to ascertain

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local market costs that reflect the reality of the Toronto retrofit industry. In order to focus on

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contractors most familiar with the types of retrofits identified in this research, three contractors specializing in ‘energy efficient’ renovations were recruited. Contractors were requested to

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provide installed costs on a per square metre (square foot) basis wherever possible, inclusive of profit and overhead but exclusive of taxes. The retrofit costs were provided for a single home

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retrofit and did not include economies of scale that could result from retrofitting many houses in

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a similar manner. When pricing, the contractors were instructed to assume the following: A full retrofit of the building (i.e. a single mobilization for all retrofits)

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No repairs were required to the existing structure

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No re-location of mechanical, electrical, and substantial lighting components

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No interior finishing (i.e. painting)

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All retrofits were to include demolition of the existing components to be replaced

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Labour and material costs were to be provided

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The square metre cost of each retrofit was then applied to take-off quantities from the Century, Century-Semi, and War Time Homes to ascertain the total cost of applying each retrofit to the homes. Finally the total capital costs were averaged amongst the four sources. Figure 3 presents the results of this process. Further detail can be found in (Jermyn, 2014).

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Figure 3 - : Average Capital Cost for each Retrofit and Level of Implementation Organized by Archetype

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It was assumed that air sealing levels 2 and 3 were achieved through a combination of the

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level 2 and level 3 wall and roof retrofits. This assumed that air sealing techniques were employed during the retrofitting process. Therefore, a capital cost of $1500 was assumed for air sealing levels 2 and 3, based conservatively on the average cost of air sealing level 1, to account for labour and materials (tapes, sealant, etc.) required for contractors to seal penetrations and leakage paths during wall and roof retrofits.

6. Modelling Retrofit Strategies The methodology for modelling the retrofit strategies and identifying the optimal path of strategies for the discrete set of boundary conditions followed a Brute Force Sequential Search method (Rysanek & Choudhary 2013, Hiyamaa 2014, Alaidroos & Krarti 2015) conducted in a

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series of iterative rounds. As an example, Level 1 of each retrofit strategy in the first round was applied to the baseline energy models and the resulting energy use reduction was quantified. The lowest capital cost per unit of energy ($/kWh/m2) saved over a one-year modelling period was

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utilized to select the most appropriate retrofit in the round. Next, this most appropriate retrofit was applied to the model and formed the new datum. Continuing on in Round 2, the remaining

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Level 1 retrofits were then applied to the new baseline, as well as Level 2 of the initially selected

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most appropriate retrofit strategy. This process was repeated in an iterative manner until the

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Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV Baseline Baseline Baseline Baseline Baseline Baseline Level 1 Baseline Level 1 Level 1 Level 2 Level 1 Level 2 Level 3

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Retrofit Round 1 Round 2 Round 3 Round 4 Round 5 Round 6 Round 7

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retrofit targets were met. A schematic example of the iterative process is shown in Figure 4.

Figure 4: Brute Force Sequential Search Method for Retrofit Determination

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Once the retrofit target was met for the archetype, the retrofit package that met the energy target stated above and the capital cost was calculated. During the energy modelling process it was assumed that air tightness Levels 2 and 3 were mutually inclusive with both Level 2 wall and roof and Level 3 wall and roof retrofits, respectively. As such, air sealing occurs in the same round as the second of wall and roof retrofits are selected (Refer to Round 8 of Table 8). It was also assumed that air sealing at envelope penetrations and leakage paths was conducted during wall and roof retrofits (CMHC 2012, Straube 2008).

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As the wall retrofit assemblies varied in thickness, a new floor area was calculated when each new wall assembly was applied. To compare EnerPHitEQUIV annual heat demand, treated

assembly in accordance with Passive House Institute (2012) guidelines.

ip t

floor area (TFA) was utilized in the energy intensity calculation utilizing the level 3 wall

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The floor areas utilized for each of the wall retrofit assemblies and the TFA are outlined

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in Table 6.

Table 6: Floor Areas for Wall Retrofits and Treated Floor Area for EnerPHitEQUIV

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Archetype Baseline (m2) Level 1 (m2) Level 2 (m2) Level 3 (m2) Level 3 TFA (m2) Surface Area / Volume Century 250.9 235.5 229.8 210.1 179.9 0.71 Century-Semi 165.9 152.4 147.5 130.6 109.6 0.62 War Time 131.7 126.4 123.9 119.9 117.1 0.79

7. Results

d

For each of the three archetypes both energy targets were achieved. The Century Home

te

achieved the 75 kWh/m2 target at Round 7 of the upgrades and achieved the EnerPHitEQUIV target

Ac ce p

at Round 14, which equated to 22 kWh/m2 of actual floor area (TFA not utilized). The CenturySemi Home achieved the 75 kWh/m2 target at Round 8 and the EnerPHitEQUIV target at Round 17, equating to 22 kWh/m2 of actual floor area. The War Time Home achieved the 75 kWh/m2 target at Round 7 of the energy modelling and achieved the EnerPHitEQUIV target at Round 22, equating to 23 kWh/m2 of actual floor area. The results of the modelling are summarized in Tables 7 through 9.

Page 20 of 42

Table 7: Century Home Energy Modelling Results by Round

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Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV kWh/m2 Baseline Baseline Baseline Baseline Baseline Baseline Level 1 Baseline 188 Level 1 181 Level 1 169 Level 1 144 Level 1 108 Level 2 83 Level 1 74 Level 2 Level 2 57 Level 2 55 Level 1 54 Level 3 46 Level 3 Level 3 38 Level 1 31 Level 2 22

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Retrofit Round 1 Round 2 Round 3 Round 4 Round 5 Round 6 Round 7 Round 8 Round 9 Round 10 Round 11 Round 12 Round 13 Round 14

Table 8: Century-Semi Home Energy Modelling Results by Round

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Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV kWh/m2 Baseline Baseline Baseline Baseline Baseline Baseline Level 1 Baseline 203 Level 1 194 Level 1 172 Level 1 150 Level 1 119 Level 2 97 Level 1 86 Level 2 Level 2 63 Level 2 60 Level 2 58 Level 3 55 Level 2 53 Level 1 52 Level 1 47 Level 2 39 Level 3 30 Level 3 Level 3 22

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Retrofit Round 1 Round 2 Round 3 Round 4 Round 5 Round 6 Round 7 Round 8 Round 9 Round 10 Round 11 Round 12 Round 13 Round 14 Round 15 Round 16 Round 17

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Table 9: War Time Home Energy Modelling Results by Round

Level 3

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Level 1 Level 2

Roof Basement Slab Windows Air Sealing Furnace HRV/ERV kWh/m2 Level 1 Baseline Baseline Baseline Baseline Baseline Baseline 193 Level 1 171 Level 1 148 Level 1 112 Level 1 106 91 69 Level 1 67 Level 2 64 Level 1 56 Level 2 47 Level 2 Level 2 42 Level 3 40 Level 2 37 Level 3 34 Level 2 31 Level 2 30 Level 3 29 Level 3 28 Level 3 27 Level 3 25 Level 3 23

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Walls Baseline

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Retrofit Round 1 Round 2 Round 3 Round 4 Round 5 Round 6 Round 7 Round 8 Round 9 Round 10 Round 11 Round 12 Round 13 Round 14 Round 15 Round 16 Round 17 Round 18 Round 19 Round 20 Round 21 Round 22

For the Century Home and Century-Semi Home, initially upgrading the furnace and

Ac ce p

increasing air-tightness provided significant energy savings with the least amount of cost – the ‘lowest hanging fruit’. Basement envelope parameters were then upgraded followed by above grade walls and the roof. It is interesting to note that window upgrades were not required to meet the first energy targets and were only upgraded during the final rounds to meet the EnerPHitEQUIV targets. This is directly attributed to the high capital costs currently associated with high performance windows in North America. The Century Home and Century-Semi Home followed very similar trends due to their similar characteristics. The driving differences between the two archetypes are floor area and surface area to volume ratio as shown in Table 6. The results

Page 22 of 42

highlight the important influence geometry has on annual heat demand for single family residential dwellings.

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The War Time Home followed a distinctly different path than the other archetypes with roof and ventilation upgrades being made early in the process and air sealing upgrades being

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made later in the process. This was due to the low cost of increasing the attic insulation and the relatively large roof to wall ratio in addition to the higher initial air tightness; this further

us

highlights the importance of floor area and surface area to volume ratio. Also, as the walls of the

an

War Time Home had greater insulation than the other archetypes initially, the energy savings from wall retrofits were less pronounced, therefore lower cost upgrades such as ventilation were

M

initially more attractive from a cost/benefit standpoint. The War Time Home was similar to the other archetypes in that envelope and furnace upgrades contributed the most to energy intensity

d

reductions and that window upgrades were not a priority. This is because of the high cost of the

te

windows relative to the energy savings.

Ac ce p

Tables 7 to 9 also show that not all upgrades were required for the Century Home and the Century-Semi home to meet the targets, whereas all upgrades were required for the War Time Home to meet the targets. The Century Home only required Level 3 upgrades for the walls, roof, and air sealing. The Century-Semi was similar although it required greater basement and slab upgrades to meet the EnerPHitEQUIV target. This observation further highlights the importance of a building’s relative shape and size. While smaller homes, like the War Time Home, tend to consume less energy, their energy intensity (kWh/m2) is generally higher due to their larger surface area to volume ratio; the ‘small house penalty’ (Holladay 2011, Ueno 2010).

Page 23 of 42

8. Estimating Total Capital Costs to Reach Retrofit Targets (alternatively this heading could be removed and the following could simply be part of the results)

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Once the retrofit levels required to meet the targets were identified, the capital costs of achieving the targets were calculated utilizing the average capital costs of the retrofit strategies

cr

presented above. The sensitivity of each retrofit strategy was also determined on a per round

us

basis. The sensitivity indicates the cost differential within which the retrofit would still be the optimal option based on cost/benefit. The incremental cost and sensitivity for each round is

an

shown in Tables 10 through 12. The relative strength of a chosen retrofit option is directly related to the size of the sensitivity reported in these Tables. The larger the sensitivity, the

M

stronger the choice, and vice versa. The results show that for a number of rounds, the chosen strategy was not particularly strong, indicating other retrofits could be chosen at little or no effect

d

to the overall process (e.g. Round 3 of the Century Home).

Ac ce p

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Table 10: Century Home Retrofit Cost Increments by Round Retrofit Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV Sensitivity $ Total Package Round 1 0 0 0 0 0 0 $ 3,150 0 $691 $ 3,150 Round 2 $ 1,335 $1,199 $ 4,485 Round 3 $ 4,340 $340 $ 8,825 Round 4 $ 5,065 $8,035 $ 13,890 Round 5 $ 21,079 $917 $ 34,969 Round 6 $ 21,585 $1,315 $ 35,475 Round 7 $ 9,878 $6,619 $ 45,353 Round 8 $ 11,398 $ 1,500 $7,876 $ 47,039 Round 9 $ 3,665 $585 $ 47,554 Round 10 $ 2,125 $42 $ 49,678 Round 11 $ 23,226 $1,454 $ 51,319 Round 12 $ 16,563 $ 1,500 $447 $ 56,483 Round 13 $ 18,509 $4,283 $ 74,992 Round 14 $ 20,597 $8,707 $ 77,080

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$ Total Package $ 3,150 $ 4,355 $ 8,531 $ 11,279 $ 26,693 $ 27,117 $ 34,092 $ 35,422 $ 35,937 $ 36,179 $ 36,344 $ 36,474 $ 38,599 $ 50,185 $ 51,432 $ 52,587 $ 56,257

Sensitivity $ 302 $ 1,250 $ 2,855 $ 4,477 $ 1,055 $ 377 $ 2,532 $ 1,262 $ 679 $ 2,227 $ 4,924 $ 12,625 $ 646 $ 979 $ 3,831 $ 3,069 $ 1,077 $ 607 $ 2,545 $ 4,321 $ 5,331 $ -

$ Total Package $ 1,890 $ 5,040 $ 10,442 $ 16,909 $ 19,034 $ 27,521 $ 29,504 $ 30,686 $ 31,201 $ 44,231 $ 45,672 $ 46,255 $ 46,710 $ 47,028 $ 47,245 $ 47,477 $ 48,608 $ 49,078 $ 49,746 $ 49,925 $ 52,522 $ 53,250

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Sensitivity $ 200 $ 433 $ 1,874 $ 8,185 $ 830 $ 288 $ 3,101 $ 18,596 $ 2,128 $ 216 $ 1,211 $ 983 $ 192 $ 554 $ 2,271 $ 9,818 $ 10,652

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an

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Table 11: Century-Semi Home Retrofit Cost Increments by Round Retrofit Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV Round 1 0 0 0 0 0 0 $ 3,150 0 Round 2 $ 1,205 Round 3 $ 4,176 Round 4 $ 2,749 Round 5 $ 15,414 Round 6 $ 15,838 Round 7 $ 6,975 Round 8 $ 8,010 $ 1,500 Round 9 $ 3,665 Round 10 $ 4,418 Round 11 $ 4,582 Round 12 $ 2,879 Round 13 $ 2,125 Round 14 $ 11,586 Round 15 $ 12,833 Round 16 $ 16,993 Round 17 $ 11,680 $ 1,500

Ac ce p

te

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Table 12: War Time Home Retrofit Cost Increments by Round Retrofit Walls Roof Basement Slab Windows Air Sealing Furnace HRV/ERV Round 1 0 $ 1,890 0 0 0 0 0 0 Round 2 $ 3,150 Round 3 $ 5,402 Round 4 $ 6,467 Round 5 $ 2,125 Round 6 $ 8,488 Round 7 $ 10,470 Round 8 $ 1,182 Round 9 $ 3,665 Round 10 $ 13,029 Round 11 $ 14,471 Round 12 $ 2,155 $ 1,500 Round 13 $ 2,610 Round 14 $ 5,720 Round 15 $ 5,937 Round 16 $ 6,699 Round 17 $ 3,256 Round 18 $ 3,726 Round 19 $ 4,333 Round 20 $ 6,878 Round 21 $ 17,067 Round 22 $ 11,199 $ 1,500

This analysis also highlights the importance of attaining accurate cost data. In some cases the difference between the selected retrofit strategy and the next most attractive strategy, based

Page 25 of 42

on the cost/benefit, was small and could be less than the anticipated variation in cost between specific projects. This is important for the Century Home and the Century-Semi Home as not all strategies were required to meet the targets, therefore a different set of strategies may have

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emerged if costs varied outside of the sensitivity range for chosen retrofits. This is less important for the War Time Home as all strategies were utilized to meet the targets; therefore the order of

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selection was less important.

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The cost for each archetype to meet the performance targets is summarized in Table 13. Table 13: Capital Cost to Reach Performance Targets for Each Archetype

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75 kWh/m2 EnerPHitEQUIV $ 45,353 $ 77,080 $ 35,422 $ 56,257 $ 29,504 $ 53,250

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Archetype Century Century-Semi War Time

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In all cases the cost of retrofitting increases with the size of the building and the cost to

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meet the EnerPHitEQUIV target is approximately 1.7 times the cost to meet the 75 kWh/m2 target.

Ac ce p

To compare the cost/benefit of the retrofits to the baselines, a long term cost estimation was conducted taking into consideration future energy costs (Aste & Del Pero 2013, Entrop et. al. 2010). The comparison was considered for a 25 year period as this is a typical mortgage period in a Canadian context. It was assumed that homeowners would finance the retrofits through a mortgage and would therefore be interested in whether the energy savings from the retrofits would justify the cost of the retrofits over the financing period. Different financing timeframes were not considered. The following parameters were utilized in the calculation: -

Retrofits were financed over a 25 year mortgage period (Canadian Bankers Association, 2013)

-

The natural gas price for heating was $0.185/m3 (Ontario Energy Board, 2014)

Page 26 of 42

-

The average electricity price (on-peak, mid-peak, off-peak) for cooling was $0.107/kWh (Toronto Hydro, 2014) The mortgage interest rate was assumed to be 4%

-

The currency inflation rate was 2.4% (Trading Economics, 2014)

-

Energy cost escalation was a low of 8% and a high of 12% per year. These rates are

ip t

-

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based on typical values used for long range cost forecasting (Sustainable Buildings

Repair and maintenance costs for the homes were not considered

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-

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Canada 2012, Sharp 2012).

The yearly cost of the retrofits financed over the 25 year period was calculated using

d

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Where: P = yearly payment N = number of payments L = loan C = period interest rate

[3]

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[C (1+ C ) N ] P= L [(1+ C) N − 1]

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Equation 3.

The total energy costs for heating and cooling over the 25 year financing period were then calculated utilizing the yearly energy usage of the retrofitted homes and the cost of utilities indexed yearly to the aggregated inflation rate (energy price escalation less currency inflation). The yearly energy costs were added to the finance cost (calculated utilizing Equation 3) over the 25 year period in order to determine the total long term cost of the retrofits versus the baseline case which only considered energy costs.

Page 27 of 42

The results of the long term cost estimations for each archetype at a fuel price increase of

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d

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an

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8% and 12% are outlined in Figures 4 through 6 below:

Ac ce p

Figure 4 - Cumulative 25 Year Cost Estimation with a Fuel Price Escalation of 8% and 12% for Century Home

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Ac ce p

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Figure 5 - Cumulative 25 Year Cost Estimation with a Fuel Price Escalation of 8% and 12% for Century Semi Home

Figure 6 - Cumulative 25 Year Cost Estimation with a Fuel Price Escalation of 8% and 12% for War Time Home

For each archetype the baseline is the most attractive from a cost/benefit perspective utilizing the energy price increase rate of 8%. In the 12% energy price increase scenario some of

Page 29 of 42

the retrofit options became more attractive from a cost/benefit perspective compared to the baseline case. The authors acknowledge that many assumptions were made in this analysis and that significant uncertainty exists in attempting to quantify the future economic conditions of

ip t

buildings (Menassa 2011, Shonder & Im 2012). What this analysis underlines however is that despite the high capital costs of implementing the retrofits, under certain economic conditions

cr

the deep energy retrofits can become more cost effective than the baseline retrofits. While past

us

research has indicated that homeowners tend to make retrofit decisions based on initial capital costs (Aydinalp et. al. 2001, Gamtessa 2013), the results of this research indicate that

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homeowners should take into consideration the long-term cost.

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9. Discussion

d

The methodology outlined in the study is applicable beyond the scope of the case studies

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presented. In order to reduce the energy consumption of residential homes, widespread adoption of retrofits is desirable. Generally every building is different either in architectural, construction,

Ac ce p

or operational characteristics which increases the difficulty in developing recommended retrofit strategies that can be widely adopted. Therefore building retrofits are often investigated and studied for individual buildings on a case by case basis, expending significant amounts of design and financial resources for a single building retrofit design that is not necessarily applicable to other buildings. Additionally, the wide array of retrofit strategies and technologies that are available, as well as the trade-off between level of implementation and cost, lead to difficulty in the retrofit decision making process for researchers, designers, and homeowners. Fortunately, often within smaller geographical areas and within specific periods of construction, residential building typologies and archetypes emerge which are similar in terms of

Page 30 of 42

architecture and construction. This allows retrofits to be designed on an archetypal basis, as was demonstrated in this research, instead of a case by case basis to allow individual designs to be

ip t

more widely adopted within the housing stock. The retrofits chosen for the case studies represent a sampling of the possible retrofits that

cr

could be applied to single family residential buildings. The retrofits focused on specific

us

assemblies, insulation types, and insulation thicknesses selected from among the wide array of possibilities that are most common in the study area (i.e. Toronto, Canada). Additionally, site

an

specific assumptions were made in the modelling process based on the case study typologies and field study conducted. These factors include building orientation and shading. The authors wish

M

to acknowledge that these conditions do not necessarily reflect all of the homes within the study area and that these assumptions may not be applicable outside of the study area. The selected

d

retrofit strategies, levels of implementation, and assemblies represent a single example of how to

te

apply deep energy retrofits to the archetypal homes with many other potentially successful

Ac ce p

options being possible. Additionally, the retrofit decision making process which focused on a brute force sequential search method and was concerned with minimizing energy use and cost, presents a single example of the types of retrofitting goals that can be incorporated into a multicriteria assessment. The process developed in this research is meant to be customizable and repeatable allowing it to be applied to a variety of housing types and allowing different retrofitting goals and strategies to be incorporated. The calibration of the energy models was limited by the data available from the archetypal homes sampled in the field study. The homes where data collection occurred are standard homes without research grade modelling equipment. Therefore monthly energy bills were utilized to extract baseline energy use data. This approach is limited and could be improved

Page 31 of 42

through the use of monitoring equipment to collect data on sub-monthly time steps. While ASHRAE Guideline 14, allows and provides guidelines for calibration on a monthly basis, utilizing a sub-monthly method would likely contribute to greater accuracy in the calibrated

ip t

models.

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The Brute Force Sequential Search method, as presented, provides several benefits for retrofit decision making. First, this method limits the total number of upgrade combinations from

us

a defined list of retrofits by selecting the most appropriate upgrades first, based on the “rule”,

an

and eliminating combinations that don’t include these upgrades. Therefore this method is more efficient and less time intensive than a typical Brute Force method. Second, this method is

M

customizable in terms of retrofitting strategies and goals. Any type of retrofit strategy can be employed and can be measured against any desired goal. This expands the applicability and

d

repeatability of the method beyond the housing typologies and goals presented in this research;

te

the overall method remains the same regardless of the goals, the number and type of retrofit

Ac ce p

strategies considered, and the building typology. The Brute Force Sequential Search method is limited in that it is sensitive to cost. This was outlined in the sensitivity analysis of the selected retrofit strategies. In some cases the sensitivity was small enough that a different set of most reasonable strategies may have emerged if costs varied outside of the small sensitivity range for chosen retrofits. This highlights the need to attain accurate cost data when carrying out the analysis. Otherwise, it is possible that the low cost of a particular retrofit strategy may make it attractive when utilizing this method, whereas practically it may not be reasonable to select the strategy. For example, in an attic roof situation, the cost of insulating is low, however when adding additional insulation there is a diminishing return. Due to the low cost of insulating, the methodology may continue to select attic insulation

Page 32 of 42

strategies even when they result in minimal energy savings, thereby neglecting other options that have a greater impact on overall energy use. While the methodology will still be successful in selecting the lowest cost option, from a material and space efficiency standpoint it may be more

ip t

practical to consider a strategy with a greater capital cost A further limitation of this

methodology is that in the first rounds the retrofits are selected in isolation; their combined

cr

effects are not taken into consideration. It is only after a number of rounds when several retrofits

us

have been selected, that the algorithm begins to consider subsequent retrofits in relation to the strategies selected in the initial rounds. It is possible that the combined effects of a selection of

an

retrofits may actually achieve greater energy savings at a lower capital cost than the initially

M

selected, singularly strong retrofit strategy.

The research was successful in achieving the energy intensity targets for deep energy

d

retrofitting. While each building followed a slightly different approach to meet the targets, the

te

retrofits that contributed to the greatest reduction in energy intensity were similar. In all cases

Ac ce p

furnace efficiency and select building envelope components were responsible for the majority of the energy intensity reduction. It was particularly interesting to find that windows did not provide an attractive cost/benefit in the research for these archetypes. While the RSI value of windows is significantly less than a wall assembly, windows were not found to be a large source of heat loss in the archetypal homes and were one of the more cost intensive retrofits. This leads to a conclusion that current pricing of high performance windows in North America is high relative to other retrofit strategies; this cost will decrease as market demand increases for these products. As similar retrofits were applied to all three of the archetypes, the differences in the retrofit strategies and levels of implementation required to meet the targets can be attributed to

Page 33 of 42

the geometry of the archetypes. The War Time Home has a larger surface area to volume ratio than the other two archetypes and is therefore likely to be less energy efficient which was shown by the level of retrofits required to reach the targets. There were also differences in geometry

ip t

between the Century Home and the Century-Semi Home that can explain the differences in the retrofits required to meet the targets. The Century-Semi Home had an L-shaped geometry

cr

whereas the Century Home was rectangular. This geometrical difference proved to be a larger

us

factor in increasing energy use in the Century-Semi Home than the party wall was in decreasing

an

energy use compared to the Century Home.

Similarly, differences in the overall cost of implementing each retrofit strategy and level

M

of implementation can be attributed to these differences. Cost is highly dependent on geometry and surface area as can be seen in the differences in the costs of applying each retrofit strategy

d

and level of implementation to the three case study housing types. For example, the single storey

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War Time Home has a smaller overall wall area than the three and 2.5 storey Century and

Ac ce p

Century-Semi homes which resulted in the cost of wall retrofits being significantly smaller. While the differences between the three case study housing types in terms of geometry and surface area are considerable, houses of the same housing type tend to be very similar. However, in some cases there are still differences from house to house within a housing type. Therefore the relationship of geometry and surface area to cost is important to take into consideration when applying the cost study to a specific housing typology as any variations in wall, ceiling, basement wall, and window area between different homes will result in different application costs for each retrofit strategy. The energy impacts of the retrofits explored in this study are considerable. For the Century Home, Century-Semi Home, and War Time Home a reduction in energy intensity from

Page 34 of 42

the baselines of 211, 228, and 209 kWh/m2 per year for heating and cooling to 75 kWh/m2 per year represents 64%, 67% and 64% reductions in annual energy use respectively. For the EnerPHitEQUIV target the reduction in energy use is 88%, 89%, and 88% respectively. As these

ip t

homes can be expected to have a long remaining useful lifespan, perhaps 50 years or more, the energy use reduction over the remaining life of the home would be considerable. It is clear that

cr

utilizing the same retrofit design for the archetypal homes in a small geographical area can

us

considerably reduce the local energy usage while minimizing the design time and expense that

an

would be required to prepare retrofit designs on a case by case basis.

While the retrofits were successful in achieving the target energy intensities there were

M

drawbacks. First, the retrofits were applied to the interior of the homes resulting in floor space loss (15 to 40m2 for the Century Home, 14 to 35m2 for the Century-Semi Home, and 5 to 12m2

d

for the War Time Home). This loss of floor space could be a drawback for a homeowner,

te

however in the authors’ experience, homeowners rapidly adapt to the new space once the interior

Ac ce p

gypsum wall board is installed.

Second, the capital cost of the retrofits would likely be prohibitive for the average homeowner with a range of $30,000 to $80,000 depending on the housing type and target achieved. This highlights the need for governments to introduce grants for deep energy retrofits. As shown in this research, depending on the fuel escalation cost, it makes economic sense to apply deep energy retrofits to existing single family dwellings. Further, strictly looking at the capital cost and energy cost of residential homes does not take into consideration the true costs of home energy use as energy infrastructure and capacity upgrades must be completed as more homes are connected to the energy grid. When taking into account these direct benefits (lower consumption, reduced impact on the environment, reduced stress on electrical and natural gas

Page 35 of 42

infrastructure), it is obvious that governments will benefit in the long term by making short term capital investments.

Conclusions

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10.

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The deep energy retrofit targets were achievable for each of the archetypes. The retrofit

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pathway followed by each of the archetypes showed some similarities and some differences. The Century Home and Century-Semi Home did not require all of the developed retrofits and levels

an

of implementation to reach the targets whereas the War Time Home required Level 3 retrofits for all of the retrofit strategies. Additionally, it was more difficult for the Century-Semi Home

M

archetype to reach the targets compared to the Century Home which had largely similar initial features. These differences highlight the effect that compactness and geometry has on the process

te

d

of choosing retrofit strategies. In all three instances the select building envelope parameters and the furnace parameter

Ac ce p

tended to have the greatest impact on energy intensity. Specifically upgrading the exterior walls and slab of the homes yielded the greatest energy use reduction for heating and cooling, however upgrading the roof yielded less of an impact on energy efficiency. Upgrading the efficiency of the furnace had the most attractive cost/benefit due to the relatively low cost of upgrading the furnace compared to that of the building envelope components. Window upgrades presented the least attractive cost/benefit as they did not tend to be an area of high heat loss for these archetypes and had some of the highest capital costs. It is important to note however that none of the homes surveyed in the field study had the original single glazed windows, but had at some point upgraded to conventional double glazed units, thereby reducing the energy reduction benefit from upgrading to high performance windows.

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The retrofit packages to meet the targets were associated with large capital costs and reductions in interior floor space. This may present barriers for the implementation of the retrofits by homeowners. The results highlight the need for government incentives to reduce the

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capital cost of deep energy retrofits since long term benefits outweigh the initial capital cost

cr

investment.

Overall the results of this research showed a valid process to implement significant

us

energy efficiency gains for single family residential buildings by using three urban Toronto

an

(Canada) archetypes as case studies. While the capital costs of the retrofit packages to meet the targets were high, the costs are significantly less than the cost of demolishing and rebuilding the

Ac ce p

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d

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home to a similar level of energy efficiency.

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References Alaidroos, A., Krarti, M. (2015). Optimal design of residential building envelope systems in the Kingdom of Saudi Arabia. Energy and Buildings 86, 104–117

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Asadi, E., da Silva, M. G., Antunes, C. H., & Dias, L. (2012). Multi-objective optimization for building retrofit strategies: A model and an application. Energy and Buildings, 44, 8187.

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ASHRAE. (2007). ASHRAE Guideline 14-2002R. Atlanta, GA: American Society of Heating, Refrigeration and Air Conditioning Engineers.

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Aste, N., & Del Pero, C. (2013). Energy retrofit of commercial buildings: case study and applied methodology. Energy Efficiency, 6, 407-423.

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Aydinalp, M., Ferguson, A., Fung, A., Ugursal, I.V. (2001). Energuide for houses database analysis. Nova Scotia: Canadian Residential Energy End‐use Data and Analysis Centre.

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Badescu, V., & Sicre, B. (2003). Renewable energy for passive house heating: building description. Energy and Buildings, 35, 1077–1084.

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Bassett, E., & Shandas, V. (2010). Innovation and climate action planning: perspectives from municipal plans. Journal of the American Planning Association, 76(4), 435-450.

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Blaszak, K. M., & Richman, R. (2013). Prioritizing method for retrofitting Toronto’s single family housing stock to reduce heating and cooling loads. Journal of Architectural Engineering, 19(4), 229-224. Canada Mortgage and Housing Corporation. (2008). Approaching net-zero energy in existing housing. Retrieved February 15, 2013, from the Canada Mortgage and Housing Corporation Web site: http://www.cmhcschl.gc.ca/odpub/pdf/66060.pdf?fr=1303770799406 Canada Mortgage and Housing Corporation. (2012). Energy efficient building envelope retrofits for your house. Retrieved February 8, 2015, from the Canada Mortgage and Housing Corporation Web site: http://www.cmhc-schl.gc.ca/en/co/grho/grho_011.cfm Canadian Bankers Association. (2013). Changes to canada’s mortgage market. Retrieved July 20, 2014, from the Canadian Bankers Association Web site: http://www.cba.ca/en/mediaroom/50-backgrounders-on-banking-issues/657-changes-to-canadas-mortgage-market Centre for Energy. (2013). Heating and cooling. Retrieved February 15, 2013, from the Centre for Energy Web site: http://www.centreforenergy.com/Consumer/Residential/Heating AndCooling/About.asp?page=2

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Department of Energy (DOE) (2015). Energyplus simulation software. Retrieved January 29, 2015, from the United States Department of Energy Web site: http://apps1.eere.energy.gov/buildings/energyplus/

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Diakaki, C., Grigoroudis, E., & Kolokotsa, D. (2008). Towards a multi-objective optimization approach for improving energy efficiency in buildings. Energy and Buildings, 40(9), 1747-1754.

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Eisenhower, B., O’Neill, Z., Narayanan, S., Fonoberov, V. A., Mezić, I. (2012). A methodology for meta-model based optimization in building energy models. Energy and Buildings, 47, 292-30

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Entrop, A. G., Brouwers, H. J. H., & Reinders, A. H. M. E. (2010). Evaluation of energy performance indicators and financial aspects of energy saving techniques in residential real estate. Energy and Buildings, 42(5), 618-629.

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Environmental Protection Agency. (2014). Product finder. Retrieved April 2, 2014, from the Energy Star Web site: http://www.energystar.gov/certified-products/certifiedproducts?c=products.pr_find_es_products

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Flourentzou, F., & Roulet, C. (2002). Elaboration of retrofit scenarios. Energy and Buildings, 34(2), 185-192.

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