The Representativity of Energy Models

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IN THE FIELD OF TECHNOLOGY DEGREE PROJECT

ENERGY AND ENVIRONMENT AND THE MAIN FIELD OF STUDY MECHANICAL ENGINEERING, SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2020,

The Representativity of Energy Models

A case study analysing causes for discrepancies affecting major system components and project phases when simulating energy demand

LINN CAROLINE BOSTRÖM

KTH ROYAL INSTITUTE OF TECHNOLOGY

SCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

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i

abstract

In order to clarify factors affecting energy simulations and their relation to real-world processes and results, this thesis sought to analyse how representative energy simulations and calculations are in relation to the in-use energy utilization of a building. This was done by firstly conducting a literature study which illustrate the major system components as well as project phases’ discrepancy factors. Thereafter an explorative case study was performed where the 2014 energy model was re-modelled, the building’s in-use energy utilization and discrepancies were audited, followed by a mapping of the mentioned discrepancies to identified factors.

Major findings within the study were that the simulated total energy use was representative of the audited energy use intensities, however the division of subsystem energy use was not. Discrepancies causing this gap between model and reality affected all major energy components as well as phases. The representativity was affected by in-built factors, lack of in- tegration of system design revisions and a lack of knowledge regarding e.g. tenant behaviour when simulating the building. Furthermore, faults and issues which arose during system installation and operation were identified. Lack of data from the in-use system further widened the gap between model and reality due to the assumptions and simplifications needed for the in-use system evaluation.

Regarding the representativity, as there is no definition for the term, it is impossible to conclude exactly how representative the model was. The final result is that it can be said that the original model was partly representative, while a comprehensive re-simulation implementing all intentional discrepancies, including both input changes as well as energy system revisions, rendered the most representative result. Further, there were five process improvements described in order to improve representativity throughout the project process;

1) Model the system as extensively as possible, do not oversimplify units, 2) Note “invisible”

input data, 3) Use iterative modelling to ensure representativity by implementing the final system design and simulating, 4) Ensure a greater utilization of the designed system, such as installing all specified meters and logging data properly, as well as 5) Define key numbers related to meters and units and present these under a specific heading in the energy report in order to decrease discrepancies, increase measurement data and ease analysis of the in-use system.

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ii

sammanfattning

Då byggnaders funktion som skydd samt komfort tas för givet, har fokus förflyttats till håll- barheten hos byggd miljö de senaste decennierna. Ett viktigt begrepp gällande detta är den energikonsumption som är kopplad till användningen av byggnader. Trots ansträngningar för att minska denna energianvändning, har den samt de relaterade CO2-utsläppen nått en statistisk toppnotering de senaste åren. I Sverige är en av de nämnda ansträngningarna att implementera energikrav som måste uppfyllas för att få bygglov. Vidare, har incitament som certifieringar framgångsrikt introducerats på marknaden, där energikraven är striktare än de lagstiftade. Denna framtida energianvändning förutsägs oftast med hjälp av energisimu- leringar av byggnaden.

Representativiteten för en sådan modell undersöktes geom en fallstudie av ett flerfamiljshus kallat Paradiset i Stockholm, som certifierades som Miljöbyggnad Silver år 2014. Det ur- sprungliga modellen som användes då jämfördes med de tekniska systemdokumenten, och sedan även med resultaten av en energigenomgång av fastigheten i bruk 2020. Detta inklu- drade att identifiera avvikelser och deras bidragande faktorer mellan var och en av itera- tionerna av byggprojektet, samt att relatera dessa till de tre relevanta faserna inom projekter- ingsprocessen. Den kartlagda energianvändningen verifierades även med hjälp av fem bran- schmetoder för att utvärdera ifall dessa kan hantera och minimera effekten av avvikelserna mellan modell och verklighet.

Eftersom det är omöjligt att uttrycka representativitet numeriskt, användes denna kartläg- gning samt uppradning av avvikelse och orsakande faktorer för att belysa hur väl simulerin- gen speglar förrukningen då systemet är i drift. Resultaten visar att trots den närliggande totala specifika energianvändningen mellan modell och verklighet, speglades inte undersys- temens energianvändning lika väl. Skillnaderna som påverkade detta inträffade inte enbart mellan modell och installerat system, utan är även inbyggda i modellen genom översimpli- fieringar av energisystemet. Vidare har reviderad systemdesign som ej modellerats ytterligare separerat modell och verklighet då en ny iteration av modellen ej simulerats,

Analyser visar att byggnadens energisystem inte användes till sin fulla potential. Vi- dare var vissa avvikelser möjliga att förhindra genom förbättrade processer för projektering, medan andra var kategoriserade som oundvikliga och kunde ej representeras i en simuler- ing. Användningen av verifieringsmetoder hanterade inte alla avvikelser; dock presterade den komplexa omsimuleringen, som implementerade alla systemavvikelser som kunde modelleras, det resultat som närmade sig den utvärderade energianvändingen mest.

De presenterade processförbättringarna beskrev att utformade system bör modelleras så omfattande som möjligt för att undvika översimplifiering av komplexa system och deras in- teraktioner. Vidare är det viktigt att notera "osynlig" indata så som scheman i energirapporten för att understryka vikten av sådana antaganden. För att representera energisystemet fullt ut är det även viktigt att iterativt modellera systemet då nya beslut tas, så att alla revisioner är implementerade i den slutgiltiga modellen. Den faktiska användningen och skötseln av systemet i byggnaden är även viktig för representativiteten då detta dels påverkar hur väl det kan utvärderas, men också för att maximalt utnyttja potentialen för energireduktion inom energisystemet. Den slutgiltiga föreslagna förbättringen för denna process är att definiera nyckeltal för installerade enheter samt mätare för att säkerställa installationen av dessa och förenkla framtida energianalyser.

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iii

foreword

This thesis is my final work for the master program of Sustainable Energy Utilization at KTH Royal Institute of Technology. By focusing on energy efficiency relating to the modelling and project process of a building, I feel that my understanding of how to ensure energy efficient buildings has deepened.

First and foremost I would like to thank my supervisor and examiner Jaime Arias Hurtado at KTH for the support and help provided. Furthermore, I would like to thank Imek Rådgivande Ingenjörer, BRF Etaget as well as Forstena Energi as they made this thesis possible, with a special thanks to both Johan Enmalm and Andre Hedberg at Imek for their continued support.

Last but not least I would like to thank all others who have supported me throughout my five years of study; that is both friends and family, especially my mother.

sincerely, linn boström

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iv Acronyms

acronyms

AHUAir Handling unit. 23 BBRSwedish Building Codes. 1 COPCoefficient of Performance. 21 CWCold Water. 33

DHWDomestic Hot Water. 14 DUCDigital Undercentral. 11

DWHEXDrain Water Heat Exchanger. 21 EUIEnergy Use Intensity. 15

HEXHeat Exchanger. 21 HPHeat Pump. 29

HRRHeat Recovery Ratio. 31 HRUHeat Recovery Unit. 30 HSHydronic System. 28

HWCHot Water Circulation. 16

IDA ICEIDA Indoor Climate and Energy. 4 KPIKey Performance Indicator. 5

NZEBNearly Zero Energy Buildings. 1 SDGSustainable Development Goals. 1

SMHISwedish Meteorological and Hydrological Institute. 6

SVEBYStandardisera och Verifiera Energiprestanda för Byggnader. 14

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contents v

list of figures

Figure 1 Street view of the Etaget building complex on Kungsholmen . . . 3

Figure 2 The main system components in a descending order from external factors towards internal elements. . . 6

Figure 3 Illustrative collage of three vernacular designs; from Turkey, Burma and Iceland. . . 6

Figure 4 Visualization of the fundamental categorization of the considered energy system phases. . . 8

Figure 5 Categorization and sub-categorization of factors from the system design phase. . . 9

Figure 6 Categorization of sub-categorization of factors from the system installation phase. . . 11

Figure 7 Categorization of sub-categorization of factors from the operation and maintenance phase. . . 12

Figure 8 Visualization of the division of the energy use within a building; facility demand to the left and tenant consumption to the right. . . 13

Figure 9 Paradiset, located on Kungsholmen in Stockholm. . . 19

Figure 10 Visualization of each construction category and the used materials’ thickness in millimeters and total transmittance. . . 21

Figure 11 Energy system visualization, adapted from the DUC graphics and the technical system drawing delivered to the customer in 2014. . . 29

Figure 12 The central hydronic underfloor system schematic. . . 30

Figure 13 The central AHU system schematic. . . 31

Figure 14 The tap water system schematic. . . 32

Figure 15 DHW heating temperature set points for accumulator hot water charg- ing using the heat pump loop and electric boilers respectively. . . 33

Figure 16 The non-normalized energy signature of the average power demand for the facility and tenant electricity, excluding the commercial spaces. . 36

Figure 17 The non-normalized facility(-) energy signature using Paradiset’s gathered historical data for 2019. . . 40

Figure 18 Evaluated historical electricity demand for each subsystem - heating, cooling, DHW and facility electricity; and trend lines relating to ambient temperature. . . 41

Figure 19 Energy signature over the estimated secondary energy demand for the cooling and heating subsystems within Paradiset related to ambient temperatures. . . 43

Figure 20 Schematic over primary energy use division within the building’s subsystems, as well as illustrating the notations of facility(+), facility(-) and facility electricity. . . 85

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list of tables vii

list of tables

Table 1 The BBR20 specific energy use limitations, in kWh/m2,year. . . 13

Table 2 The BBR20 installed electric heating effect limitations, in kW. . . 13

Table 3 The BBR20 maximum mean heat transfer coefficient, in W/m2,K. . . 14

Table 4 A brief compilation of BBR & SVEBY input data for multi-family buildings. . . 14

Table 5 Non-negligible deviations for different subsystems. . . 17

Table 6 Presented area division in the building, from 2014. . . 19

Table 7 Key numbers for each KPI set for meeting the building code requirements. . . 20

Table 8 Key numbers for each KPI set for the initial energy system evaluation for Miljöbyggnad Silver using IDA ICE. . . 20

Table 9 Key numbers for the rated peak loads, from the initial energy subsystem evaluation report. . . 21

Table 10 System unit powers and performance coefficients from the project design phase. . . 22

Table 11 The 2014 IDA ICE-model specified thermal bridges. . . 22

Table 12 The 2014 IDA ICE-model specified distribution system losses. . . 23

Table 13 The 2014 IDA ICE-model specified indoor climate set points. . . 24

Table 14 The 2014 IDA ICE-model specified internal gains. . . 24

Table 15 Key numbers from the initial energy subsystem evaluation report, an- ticipated demand in kWh/m2, year. . . 25

Table 16 Further KPIs from the evaluation report, electricity consumption in kWh/m2, year. . . 25

Table 17 Operational hours for the different areas. . . 28

Table 18 The heating system set point curve of HSsupplytemperature related to the ambient temperature, gathered from the DUC. . . 30

Table 19 Audit specifics for HRR evaluation. . . 31

Table 20 Statistical analysis of total tenant electricity data. . . 37

Table 21 The calculated electricity consumption for an average apartment. . . 38

Table 22 DHW electricity consumption calculation per subsystem; electric boilers and heat pump respectively; firstly presented per month and then extrapolated for a whole year. . . 39

Table 23 The calculated non-normalized specific energy uses for the individual subsystems, in kWh/m2,year. . . 41

Table 24 The calculated normalized specific energy use for each subsystem in kWh/m2,year. . . 42

Table 25 The relation of the ambient temperature to the two maximum heating power outputs as well as the amount of days that are colder than the used ambient temperature. . . 43

Table 26 Key numbers from the energy audit energy report; calculated 2019 energy demand in kWh/m2. . . 44

Table 27 Further KPIs from the audit report, calculated primary energy demand in kWh/m2, year. . . 44

Table 28 Summarized BBR verification data for Paradiset. . . 47

Table 29 Key numbers from the BBR verification; calculated 2019 energy demand in kWh/m2. . . 47

Table 30 Further KPIs from the BBR verification, calculated primary energy demand in kWh/m2, year. . . 47

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viii list of tables

Table 31 Evaluation of non-negligible deviations for the subsystems, standard values compared to audit and SVEBYaconsumption. . . 48 Table 32 Key numbers from the SVEBYa verification; calculated 2019 energy

demand in kWh/m2. . . 49 Table 33 Further KPIs from the SVEBYaverification, calculated primary energy

demand in kWh/m2, year. . . 49 Table 34 Evaluation of non-negligible deviations for the subsystems, standard

values compared to audit and SVEBYbconsumption. . . 50 Table 35 Key numbers from the SVEBYbverification; calculated energy demand

in kWh/m2. . . 50 Table 36 Further KPIs from the SVEBYbverification, calculated primary energy

demand in kWh/m2, year. . . 50 Table 37 Comparative table over 2014 4.8 simulation input data, audit calculated

value and resulting 2020 4.8 re-simulation input data. . . 51 Table 38 Key numbers from the re-simulated energy models; calculated energy

demands in kWh/m2. . . 52 Table 39 Further KPIs from the re-simulated models, calculated primary energy

demands in kWh/m2, year. . . 52 Table 40 Evaluated specific energy demand compared to the calculated spe-

cific energy intensity using each verification method for Paradiset, in kWh/m2, year. . . 53 Table 41 Comparable evaluated specific energy demand from the two 2014 en-

ergy models and the 2020 audit for Paradiset, in kWh/m2, year. . . 54 Table 42 A mapping of the identified discrepancies that are primarily relatable

to major system components. . . 60 Table 43 A mapping of identified discrepancies and evaluated phases for the

project process. . . 62 Table 44 Mapping of specific factors causing discrepancies within the system

design phase . . . 64 Table 45 Mapping of specific factors causing discrepancies within the system

installation phase . . . 65 Table 46 Mapping of specific factors causing discrepancies within the operation

& maintenance phase . . . 67 Table 47 Evaluated primary specific energy demands using the same subsystem

performance efficiencies for all results as well as the evaluated tenant consumption, in kWh/m2, year. . . 68 Table 48 Period specific data over months, ambient temperatures and degree

hours for the current month and corresponding normal year month. . . 86

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introduction 1

1 introduction

In 2010, the European Union passed a directive which concerned the energy performance of buildings [1]; using the definition stated in this it has been decided that all newly produced buildings must fulfil criterion to be Nearly Zero Energy Buildings (NZEB) by 2020. NZEB is a classification which takes the energy use in built environment into account, however the exact method and numbers vary between member states. In Sweden, the national definition is included as a limiting factor in the Swedish Building Codes (BBR) [2].

Furthermore, in 2016 the United Nations announced 17 Sustainable Development Goals (SDG) to serve as guidelines in facing the global challenges of today. One of these, more specifically SDG7 regarding Affordable & Clean Energy, highlights the importance of further limiting the energy use in the built environment [3]. This focus has also been emphasized on a national level in Sweden where, for example, the national environmental goal God bebyggd miljö states that the energy utilization in all buildings must not only be lessened but also made more efficient [4].

Despite these efforts, final energy use in buildings grew from 2010 to 2018, while the share of fossil fuels decreased only slightly by 2%. Stated in absolute terms, building-related CO2 emissions are continuing a trend of annually rising to an all-time high. [5] In 2019 a total of 36% of global final energy consumption and nearly 40% of total direct and indirect CO2 emissions were caused by the facility and building construction sectors combined [6]. In Sweden that same year, the largest consumer of energy was the sector known as the household and service sector, with a total of 146 TWh; almost 40% of the final energy consumption [7].

These numbers mark the continued importance as well as the overall need for increased sustainability in terms of energy use in the built environment. As both legislators, consumers and stakeholders have become aware of the issue, there has been an increased demand of

"energy efficient" buildings; especially concerning new productions. This demand has not only been embodied in legislation, as stated above, but also as efforts such as certifications for buildings. These certifications are assessment methods for the sustainability of a building, where most incorporate an energy use aspect [8].

Both building permits and certificates are most often partly based on energy model simulations of the building, that indicate the final energy consumption of it while operating.

Therefore, these decisions are based on an approximation of reality, as that is what a model is. The key numbers provided are not the "truth". However, as an approximation is necessary, the question of how well the energy model reflects reality remains. What discrepancies are there? When do they occur and what causes them? Can they be handled during the simulation stage or using another process? In order to evaluate these questions to an extent, a case study may be performed to highlight instances in a real case.

1.1 Background

The perceived issues with buildings have changed over the course of history, as prioritized characteristics shift. The original, primary need that is satisfied by the built environment is the physiological need for shelter and a conditioned climate [9]. As such, shelter has always been a priority throughout the history of mankind. However, this would not always be the case as the industry evolves. Currently, in large parts of the world, the functions of shelter and comfort are taken for granted. Instead, there is a large focus on the sustainability of the built environment in an attempt to mitigate the continued exploitation of resources and energy which has resulted in an unsupportable global trend [10]. Therefore, the resources used throughout buildings’ life cycles are scrutinized more heavily and strategies to evaluate different aspects of sustainability are more and more widely used.

When aiming to create a more sustainable built environment, the main issue is a lack of consensus regarding what exactly constitutes sustainability and excellence for buildings

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2 1.2 problem definition

and their performance. The concept of sustainability covers the overlapping dimensions of social, environmental, economical and technological factors [11], and are as such hard to con- ceptually condense into one definition which is acceptable to all. Therefore there are many initiatives which focus on this development within the building sector, some in the form of legislation and other as certifications or incentives, which focus on different sustainability aspects. The NZEB legislation as well as the building code requirements are examples of this focus on energy consumption. Furthermore plenty of construction projects undertake more responsibility voluntarily by certifying the buildings, which may encompass different sustainability dimensions. In Sweden, there is an exponential trend regarding using certifications for marking the building as "sustainable" or "green", where the most used systems are Svensk Miljöbyggnad, Green building and LEED [12]. These offer, amongst other criterion, even stricter energy requirements than the BBR.

This limited energy use may be evaluated in different ways, but most often there are criterion for the evaluations; such as the use of time steps and energy balance. For new productions, both the BBR assessment and an eventual certification process may rely on energy simulations to detail the specific or primary energy usage of the building’s operational phase [2,13]. Neither the BBR nor some certification standards, or levels, are subjected to any form of obligatory verification - meaning that the pre-construction computerized energy model is deemed sufficient enough for a "stamp of approval". Thus, energy simulations are not only a large part of the building process, but also critical for the final performance of the building.

These models are built up from data regarding the building envelope, construction, local climate, operation and system schematics, internal gains, ventilation systems and a number of other assumptions regarding building usage, which are aggregated into one single file.

The result is then a deterministic absolute value regarding the energy use per square meter and/or energy source. However, as the aphorism goes, all models are wrong but some are useful; which implies that as the model is simply an approximation of reality, the accuracy of it may be questioned. It is, as such, important to not only verify the calculations, but also to continuously ensure follow-up evaluations of modelling procedures and the input data used.

The representativeness of an energy simulation would therefore be interesting to evaluate;

to compare the simulated potential energy use and the measured consumption in order to determine how close reality and the idealized model are. In order to be able to evaluate the representativeness of a simulation, measurement data from the building in use is required.

Such data is often available within projects aiming to increase the sustainability of a building by reducing the energy consumption. Therefore, a case study is used for exploring which factors, for which system components, in what phases of the production influence the energy consumption and how these differ from the modelled building. The chosen project is a multi-family building known as Paradiset and is located on Kungsholmen in Stockholm. The building is depicted in figure 1 [14].

1.2 Problem definition

This thesis seeks to analyze how representative energy simulations are in relation to the measured energy consumption of the building in use. Furthermore, it seeks to relate any discrepancies to different factors and phases of the construction project, as well as highlight important input assumptions. By doing so, factors affecting energy simulations and their relation to real-world processes and results are clarified.

The following research question is to be answered:

• How representative are energy simulations that are used to predict energy utilization in comparison with measured energy uses in multi-family residential buildings?

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1.3 objectives 3

Figure 1: Street view of the Etaget building complex on Kungsholmen

The research question is further supported by the following questions:

• How was the energy utilization simulated?

• How is the energy system use metered and measured in the building?

• What energy breakdown similarities and differences exist between the in-use demand and the simulated demand? Where do they occur?

• What are the identifiable reasons for the differences and can these be tackled in any way?

1.3 Objectives

The objectives of the proposed study are to:

1. Evaluate how representative the simulated energy demand of the chosen multi-family residential building is, by comparing the simulation results and the measured energy consumption.

2. Evaluate the discrepancies between the energy model and gathered data.

3. Propose possible improvements for the process.

1.4 Methodology

In order to fulfil the objectives, this study was designed as an explorative case study divided into three main phases. Firstly, a literature study was conducted where information was com- piled regarding multi-family household energy use, energy efficient buildings, certifications, and verification methods, as well as energy-related implemented installations. The information was primarily collected from relevant literature such as scientific books and reports, as well as Swedish laws and regulations.

The second phase of the project included part of the case study, where the reproduction of the building energy model was performed. To enable this phase, the project report from

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4 1.4 methodology

2014and a partial simulation file was used to extrapolate the initial model to encompass the whole building.

The last phase consisted of analysing the energy model and gathering historical data, and further relating these to the initial project key numbers. The primary methods used were evaluating and analysing the theoretical whole-building energy simulations - both from the previous stage in the project and the new models created with current and historical data. As such, a key action is to collect pre-measured data of the building. Furthermore, there may be a need for performing measurements on the building and its systems in use.

The primary software used for the study is the whole-building simulation program IDA In- door Climate and Energy (IDA ICE), however MS Excel will also be used as well as AutoCAD.

1.4.1 Company collaborations

This thesis is performed in collaboration with and for IMEK Rådgivande Ingenjörer AB in Stockholm, Sweden. The thesis was further supported by Paradiset’s housing cooperative BRF Etaget and the facility manager Forstena Energi & Kontroll.

1.4.2 Limitations

In this thesis the case study focuses on one building, which is known as Paradiset in Stock- holm. The focus is not to re-simulate the building using parametric inputs, but to analyse discrepancies between the initial building model and the in use building energy system by auditing and using verification methods. The building energy system is evaluated using the available data, no additional data gathering was performed and as such there was no tenant survey conducted. Therefore, system performance from the demand-side perspective was not investigated.

The found discrepancies will be related to what system components they affect as well as during what phases they occur. The phases are limited to the system design, system installation and operation & maintenance, i.e. sourcing, demolition and disposal are not discussed.

Furthermore, alternative system solutions are not discussed either.

The energy simulations were performed in IDA Indoor Climate and Energy (IDA ICE), meaning that energy models and simulations performed in other similar programs will most likely provide other results. The original energy simulation files were unable to be fully recovered and as such the 2014 energy simulation is in part based on data from the energy report as well as with the help of the original model creator.

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theory 5

2 theory

The theory section focuses firstly on defining what key performance indicators are, and secondly describes energy use within buildings as well as theoretical background and energy requirements stated by laws and regulations. This is followed by subsections containing information regarding typical values used to define systems in multi-family buildings, energy efficiency and certification systems for buildings as well as factors affecting energy use from a system perspective and lastly verification methods for proving or disproving simulated energy consumption predictions.

2.1 Key Performance Indicators

A general definition of a Key Performance Indicator (KPI) is that it is "a very important indicator" used as "a way of measuring a company’s progress towards the goals it is trying to achieve", or "that shows how well an economy, company, stock, project, etc., is doing" [15].

The definition also holds true for building projects and determining the energy performance.

By defining a KPI to be used during the system design, clear requirements for certain aspect related to energy use is stated. This may apply to the cooling power used, efficiency value or similar. Furthermore, if more than one KPI is set these can be prioritized. That is, when defining the so-called goals, a hierarchy may be established in order to better sort between groups, alternative solutions or performances. [16]

KPIs may be optional, additional requirements to take into consideration in addition to the requirements set by the BBR. These may not only be used as measuring sticks during design and production, but also as KPIs during any following evaluation or verification of the building performance. These quantifiable factors of interest may be used for e.g. tracking the performance between months or over years, or comparing buildings to each other [17].

2.2 Buildings as energy systems

Focusing on the operational phase of a building’s lifetime, how much energy does it require?

Technically, nothing at all. Energy is only needed for the thermal comfort and conditioning of the indoor climate [9], as well as for the provision of different services such as cooking or the use of appliances; the need is not set by the building but the people and activities performed in it. Therefore, it is this which constitutes the demand - which in the ideal world this would perfectly correspond to the consumption. The reality however, is that the building does not exist in a vacuum and as such a multitude of independent factors and efficiencies exist that further increase the specific energy consumption. The physical connection between the demand and the consumption is not only made up of the system specifics or appliances, but also of factors such as materials properties, thermal mass, solar irradiation and heat generation.

This system based perspective where the energy consumption is determined by the inter- action of the environment, building and its activities, can be understood as a complex system of inter-related factors. The main system components which relate to this may be summarized as: the climate, the building envelope, the indoor climate, operation and control of the system as well as tenant behaviour [9]. The chosen order of these reflect the layering from external factors towards internal elements which affect the building’s energy consumption, as can be seen in figure 2.

These system cornerstones will be further contextualized and described under each respective section below.

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6 2.2 buildings as energy systems

Figure 2: The main system components in a descending order from external factors towards internal elements.

climate

The local climate, whether it is cold, hot, temperature, humid or dry, determines the fundamental preconditions in which the building will exist. The influence of this factor is especially prominent in vernacular bio-climatic building designs where many archi- tectural features have been developed over time to face the specific challenges set by the regional climate, to increase passive regulation and self-function [8]. Illustrative examples of a Turkish mud house [18], a Burmese stilt house [19] and an Icelandic turf house [20] are presented in image 3.

Figure 3: Illustrative collage of three vernacular designs; from Turkey, Burma and Iceland.

These preconditions are amalgamations of meteorological elements, where the most defining aspects may be stated to be the ones represented in the standard year models that Swedish Meteorological and Hydrological Institute (SMHI) releases; this includes wind speed and direction, air temperature, relative humidity, cloudiness as well as both global, direct and indirect radiation. Other climate factors to consider are the more static ones, such as the location and orientation of the building.

All of these affect the final energy consumption in buildings, primarily the heating and comfort cooling needs. The BBR has as such set different energy restrictions depending on where in Sweden the building is located with the use of different zones.

The resolution of the zones, i.e. the amount, has changed over the years; there used to be three climate zones [2], however as of the latest BBR this has been expanded to four zones [21]. Furthermore, the BBR specifies other factors, such as the geographical factor

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2.2 buildings as energy systems 7

F, differs in value depending on location.

building envelope

The buildings envelope is sometimes referred to as the bridge between the climate and the indoor climate, and sometimes it is referred to as the barrier between them.

No matter the connotation, the shell of a building is an important construct which facilitates the creation and control of an indoor climate. The foundation, floor, walls, roof, windows and doors separate the exterior and interior of the building. Therefore, the choice of materials and construction of these affect the energy performance as it is their material properties that determine the influence the climate has on the desired indoor climate and thermal comfort.

The major losses are connected to the transmission of heat through the building envelope, air leakage rates as well as ventilation losses [9]. The first two are closely interconnected with the envelope. Building construction choices such as insulation thickness, thermal transmittance, thermal bridges and air tightness are vital in order to provide an effective building and reduce such energy losses [22]. Furthermore, the heat capacity of materials and the total thermal mass also affect the time constant of the building; that is for how long a building may offset temperature changes within the building due to the external climate [9]. This defines the resilience of a building towards the climate and its changes.

As the construction of the envelope is such an important system component, it stands to say that the BBR has set values for certain envelope characteristics, e.g. the minimum thermal resistance allowed [21].

operation & control

The technical systems in the building operate to support the indoor climate using the controlled set points and is the first internal system component. These controls in- clude subsystem settings such as flow rates, temperatures and operational strategies for equipment. A division may be made between facility operated set points which are cen- trally controlled and individual set points which tenants may influence, such as room temperature.

The majority of the inputs for the operation & control part of the system are not static, but subject to change. Both between the design and implementation of the technical systems, as well as during the operational phase. Facility operators may change certain settings, the activity in the building may change and as such require other conditions or a whole subsystem may be renovated. Most of energy saving measures target this type of change specifically due to the fast results and the generally low investment cost [23].

Modern day technical solutions and IT-models for buildings’ energy systems are both cause for opportunities as well as difficulties. The ability to have a complex control may facilitate fine tuning of the systems and optimization of control strategies, however, it may also be cause for too complex controls that are unnecessarily complicated and difficult to manage. On a lesser scale, the installation of different meters to monitor both set points and e.g. related flows are valuable from a monitoring stand point, and the data may be used to verify energy performance and follow up upon system design efficiency.

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indoor climate

The indoor climate is a main system component which is created by the use of the envelope and controlled and maintained by the use of buildings’ technical systems. The indoor climate may be summarized as the control of the air quality and thermal comfort [24], defined by set points which generate a desired condition that is to be upheld. This is an energy intensive process, and as such it is important to balance eventual energy saving measures to the impact on the indoor climate.

Temperature, draft control, air freshness, daylight, humidity, pollution control are some of the aspects which are included in the term "indoor climate" [24]. Just as with the previous components, the indoor climate is largely regulated by the use of minimum values, where the BBR specifies such for ventilation flows and daylight levels [21], to mention two.

tenant behaviour

Tenant behaviour is a major component in the total energy system within a building.

The internal gains from both occupants, activities performed and equipment are en- tirely dependent on the tenant behaviour, which is impossible to predict. There are industry standards which define normal occupants and internal loads related to them [25], however the deviations from these will affect the heating and cooling loads as well as the total electric consumption. Not only the quantities, but the temporal variations of these, e.g. seasonal or hourly, are important as the schedules of the tenants affect the hourly energy balance of the whole system.

The conditions and interactions of these major system components all determine a building’s energy consumption.

2.2.1 Categorization of factors affecting energy use

From a longitudinal perspective, the main factors affecting energy use may be divided into three distinct phases; System design, System installation, and Operation & maintenance [26], as depicted in figure 4.

Figure 4: Visualization of the fundamental categorization of the considered energy system phases.

system design

The system design is the first phase which defines the operational energy performance of the building. Here, the key numbers and factors are set, as are the measures that are to be integrated in order to reach the chosen performance. Furthermore, this section also includes

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the project evaluation of the completed energy system and initial verification. There may be many different sources of deviations and uncertainties which will affect the results in this stage, such as uncertain inputs or flaws inherent in the energy calculation software [26]. These are visualized in figure 5 [26,27] and described thereafter.

Figure 5: Categorization and sub-categorization of factors from the system design phase.

The displayed categories will be expanded upon in the following sections.

uncertain inputs

The first main category is uncertain inputs, which at a base level may be defined as "lack of knowledge concerning the true value of a quantity" [28]. In this thesis, uncertainty is not further defined by using the separate typologies of factors which are included in the term. Instead it is divided into two main categories, Misuse or lack of knowledge and Faulty values, as can be seen in figure 5.

Lack of knowledge is a broad categorization which includes not knowing the "true"

value or the uncertainty inherent in the input data used during the system design, as well as a lack of knowledge leading to less qualified assumptions or simplifications of the system. The system design phase is characterized by qualified guesses based on experience, industry standards or other recommendations - therefore, the lack of knowledge does not encompass data gaps but instead the use of unrepresentative data in order to fill those gaps in calculations.

Faulty values are instead focused on the human error, that the wrong value is en- tered or a revision is overlooked. This does not overlap with faulty values being used due to a lack of knowledge.

climate data

climate data is the next factor, as there is currently no industry standard for climate files. This is not simply categorized as a case of uncertain inputs as this data defines one of the major cornerstones of the system, see section 2.2. The major issue is not that the climate files are lacking statistical basis or values, but instead that the results calcu-

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lated using these are compared to measured results which are then normalized using a standard year. In most cases the climate file used and the standard year are not based on the same data, which leads to an unbalanced comparison of system design results and operational real time data.

shortcomings within the software

The third major factor defined under the system design phase is shortcomings within the software, divided into shortcomings that are Inherent in chosen software and Even- tual lack of features.

The first sub-category, inherent shortcomings, relates to the intentional simplification of physics such as heat transfer, i.e. model uncertainty. The degree and amount of such simplifications will lead to a rendered result which will differ more or less from the actual energy use.

The second sub-category which addresses the eventual lack of features, focuses on the degree to which extent the chosen system design can be modelled within the software.

Important technical details may not be adequately represented, leading to misleading results and thus affecting the downstream energy use.

verification

The last category of factors during the design phase is verification, where the identified factors are No verification requirements and Misused, uncertain or blunt verification methods. As the system design phase culminates in a proposed solution there is no actual system performance to be verified in comparison to operational values during the phase. However, by verifying the performance to the calculations when the system has been implemented, sources of deviations and uncertainty may be highlighted and possibly handled in the future. The use of verification methods may lead to optimization opportunities for the set system, and furthermore eventual insights may be helpful for future system design phases. If there were to be no verification requirements set up from the beginning, verification may be hindered down streams due to a lack of measurement equipment, KPIs or similar.

Misused, uncertain or blunt verification methods may be seen as a lesser evil, as the energy use would be verified. However, a false positive may be more damaging than a negative, in regards to system performance assessments.

system installation

During the next phase, the system installation, the system may be subjected to changes or sub-par execution. Furthermore, factors such as construction faults are included under this phase. It is here the model is implemented in reality, and therefore the decisions and procedures during this phase affect the whole system and its performance. These are visualized in figure 6 [26,27].

system design

system design, the first factor during the system installation phase is related to the whole previous phase known by the same name, System design. If there is a Revised system design compared to earlier phase, the energy used by the system will deviate from what has been calculated.

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Figure 6: Categorization of sub-categorization of factors from the system installation phase.

faulty installation

The second factor mentioned is faulty installations, for which the further divisions of Faults that are hard to quantify and Faults that are hard to attribute to energy use are made.

The first one regards the faulty implementation or construction of part of the building system where it is hard to directly measure or quantify the effects of such. The latter defines faults that may be quantifiable, but where the effects are hard to allocate to energy consumption and as such are of undefined influence.

inspection

inspection, the third category, only includes one factor; Measurement and control uncertainties. This relates to the quality of the inspections of the finished system, and how correct the data collected during such instances are.

verification

For this system phase, the verification factor is the Lack of revisions which may occur during a building process. This is not the same as the Revised system design mentioned earlier under this phase. The difference is that a revised design relates directly to whether or not such a decision has been made, while the verification factor relates to if an energy performance revision has been calculated, especially regarding unintentional system design changes.

operation & maintenance

The last phase is the operational and maintenance one, where the day-to-day consumption and upkeep influence the actual performance of the system while in use. Deviations which are also included under this phase are things such as if the intended activity of certain areas has changed, or if there are issues with the measuring equipment used. These are visualized in figure 7 [26,27].

deviation identification

The third phase’s first category is deviation identification, which is split into Mea- surements uncertainty, real time value and DUC metering and Calculation and assessment of impact. Here DUC is the Digital Undercentral (DUC), the digital system for operating

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Figure 7: Categorization of sub-categorization of factors from the operation and maintenance phase.

the machine room. These handle the two ends of identifying a deviation in energy use;

the quantification of a deviation and the quantification of its impact; meters, measurements and the identification of a deviation constitute the first factor, including quality and calibration of meters. This is in contrast of the calculation of impacts, where actual deviations may not be attributed the correct influence, or non-deviations may be assessed as having an impact which they do not have.

deviation cause

deviation cause is the category of assigning deviations to the correct cause; where they may be due to system failures or design issue such as Faulty equipment, wear and tear or similar, or the may be attributed to Activity related changes in external conditions.

External conditions relate to causes that are not intrinsic to the technical system, but instead deals with occupancy, operational hours, activities performed etc.

correctional measures

The last category of factors deal with the correctional measures employed to normalize or otherwise correct the energy performance. Faulty attribution of cause is the action of relating a deviation to the wrong cause and as such correcting the energy usage based on false assumptions, leading to an incorrect correction. The second factor, Improper correction of deviation, instead deals with the use of incorrect measures to correct "true" deviations. In opposition of these two factors, there is instead the Lack of correction of deviation where the non-action of not correcting such identified causes are a factor which affects the energy use.

When evaluating and determining affecting factors it is important to further divide these into those concerning faults and imperfections as well as those which are correlated to the occupancy and activities performed [27]. The first can either be handled when deciding upon inputs or through repeated simulations at the end of each phase. The second leads to consumption that is difficult to estimate during the project phase, and as such cannot be handled effectively.

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2.2.2 Energy requirements and limitations

There are laws and regulations which restrict energy use within a building, as mentioned in the introduction. These limitations must be adhered to, however, not all of the energy use is regulated. Primary energy use is divided between facility and tenant uses. That is, the energy used for the building’s technical systems such as heating or pumps and fans is included, but specific individual plug loads and lighting are relegated to tenant usage, see figure 8.

Figure 8: Visualization of the division of the energy use within a building; facility demand to the left and tenant consumption to the right.

Currently the regulated primary energy factor is defined as a key factor which is the sum of the building’s energy uses multiplied with a primary energy constant P depending on the energy source that is then divided by the conditioned floor area [Acond]. In this calculation heating use is corrected by the geographical factor F, which differs in value between predeter- mined zones in Sweden, between 0.9 and 1.6 [29]. However, as the energy use was evaluated in 2014, the BBR20 requirements were used for the evaluation of the proposed multi-family building’s key numbers. As such, it was not BBR28 that was used, but the laws and regulations valid in 2014 [2].

The BBR20 contains a different definition of the energy requirements of a completed building’s performance. The specific energy use has a maximum allowed set point, which depends on a set of three factors; defined climate zone, primary heating energy source and intended activity. Further energy requirements concern the installed effect of heating appliances which make use of electricity as the primary energy source, and the building envelope’s mean heat transfer coefficient, Um. [2] In the following tables the key data will be defined numerically.

Table 1: The BBR20 specific energy use limitations, in kWh/m2,year.

Climate zone I II III Residential, electric heating 95 75 55 Residential, other heating 130 110 90 Commercial use, electric heating 95 75 55 Commercial use, other heating 120 110 80

Table 2: The BBR20 installed electric heating effect limitations, in kW.

Climate zone I II III

Maximum installed electric effect 5.5 5.0 4.5

Additional effect if Acond> 130m2 0.035 · (Acond− 130) 0.030 · (Acond− 130) 0.025 · (Acond− 130)

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14 2.3 energy use in multi-family buildings

Table 3: The BBR20 maximum mean heat transfer coefficient, in W/m2,K.

Climate zone I II III Residential 0.40 0.40 0.40 Commercial 0.60 0.60 0.60

2.3 Energy use in multi-family buildings

It is difficult to define standards for the energy use in buildings in a general and comprehensive manner, however, some categorization is necessary for industry standard purposes.

For example, it is well known that occupant behaviour is a major factor of influence on the actual performance of a building, yet is incredibly difficult to account for. One cannot tailor a building to the specific inhabitants and consequently "standard" occupants and activity levels are needed.

There are official documents from Boverket, the National Board of Housing, Building and Planning, as well as from an independent industry organisation known as Standardisera och Verifiera Energiprestanda för Byggnader (SVEBY) which promotes standardization and verification, that present input data for different types of systems. These detail such things as indoor temperatures, demand time estimates and energy demand of subsystems such as the Domestic Hot Water (DHW). These may be used as key numbers for energy simulations and calculations. In table 4 aggregated suggested inputs are presented, [21,30,31].

Table 4: A brief compilation of BBR & SVEBY input data for multi-family buildings.

Parameter Sub-parameter Value

Indoor temperature Heating season 21 °C

Ventilation air flows Demand-driven flows: Kitchen fan 30 min/24 hours

Airing addition: Energy 4 kWh/m2,year

Solar shading Screen factor: Total 0.5¹

Screen factor: Demand driven 0.71³

Domestic hot water Energy: Yearly standard 30¹or 25²or 25/ηDHW3

kWh/m2

Internal heating: Accountable 20 %

Tenant energy consumption Energy: Yearly standard 30 kWh/m2

Internal heating: Accountable 70 %

Heat generation Presence 14 hours/24 hours,person

Power output 80 W/person

These standardised inputs are created for the Swedish building industry using statistical data from national building projects. The extensive work has as such successfully defined "normal"

values for several aspects within the built environment in a comprehensive manner. However, there are some criticisms directed towards this as well. There are questions regarding the validity of the values as energy efficiency increases, i.e. that the standards are out-of-date, and the use of conditioned area vs the use of living space area. A report issued by Skanska, a multinational construction and development company, contested the presented standard value of tenant energy consumption in table 4 and instead suggested an annual 20 kWh per square meter of conditioned area. Furthermore, the notion that 70% of this electricity is accountable as heating was deemed antiquated and inadequately researched in the same report. [32]

1BBR;²SVEBY;³The yearly efficiency of the heat source for DHW production

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2.4 energy efficiency & certifications 15

2.4 Energy efficiency & Certifications

In the context of the life cycle of a building, the operational phase typically lasts at least 70 years [9]. Meaning that a large amount of not only current, but also future energy consumption is embodied in the present; firstly by the performed energy calculations and then realized by the construction. It is therefore necessary to aim towards buildings having the quality of being more efficient - the housing industry must spend less resources during a longer period of time when performing the same action as it currently does. This is partly being regulated by the above mentioned building codes, where minimum requirements are stated and frequently revised. There is, however, a tendency for optimizing the performance of buildings right below these key numbers, meaning that the requirements have not become upper limiting factors but instead numbers to "strive towards" in a way that was not intended [9].

To mitigate this trend, while also increasing property value, there are a number of certifications available for sustainable buildings, in an effort to construct buildings that perform beyond the minimum; such as LEED, BREEAM or Svensk Miljöbyggnad. These do not only take energy consumption and efficiency into consideration, but are also used as means to evaluate building performances across a broad span of sustainability considerations which depend on the certification system used.

A granted certification is, however, not a guarantee that the building performs as ex- pected. Depending on the procedure and the amount of time within a verification must occur, if any is required at all, there may be buildings that are essentially green washing their energy profile. The general trend in Sweden is that the specific energy usage of new buildings is over 20% higher than evaluated, and in some cases as much as 50% higher, certified or not [33]. This is not a new fact, such performance gaps between calculated energy intensity and actual usage are well-documented [34, 35, 36, 37]. Therefore, verification processes and follow-up studies are crucial for not only upholding the certification standard, but also place importance in certification levels and their worth.

2.5 Verification methods

Verification of actual building performance in contrast to the certified Energy Use Intensity (EUI) is a necessity. Ideally the energy performance of a building would be a quantifiable characteristic which could be measured with good precision. However, in practice this is difficult to define as there are a multitude of factors affecting energy use. Therefore, there is a lot of importance placed in the methods used to collect data, calculate building performance and verify the specific energy use. Furthermore, the verification methods should also preferably be standardized so as to allow for comparisons between different projects. There are plenty of approaches described in literature and legislation. This thesis firstly focuses on performing an energy audit. However, as the thesis also has a national perspective the BBR recommended procedure and two SVEBY methods will be explored, as well as exploring the use of re-simulating the building using refined input data. The methods are further defined in each corresponding subsection.

2.5.1 Energy audit

An energy audit is a procedure carried out to understand the energy performance of a facility or building, mainly in order to find areas with energy saving potential; as such it is a study of the energy use and cost as well as identification and recommendation of measures to improve the building’s performance [23].

There are different scopes for these audits, depending on how in depth the investigation will be. The three main categories are: (1) Walk-through assessment, (2) Energy survey &

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16 2.5 verification methods

analysis, and (3) Detailed energy audit [17]. The first one assesses the performance by briefly surveying the building and analysing energy costs; resulting in a short report listing no- to low cost measures and a list of possible further investigations. The second level is a bit more detailed, a second building survey as well as energy analysis are performed, and historical data is also gathered. The report provides all that the first level does, while also offering insight to the more capital heavy measures. The third level focuses on potential optimization and capital projects based on a second level audit procedure, as well as containing all of the information included in a level I and II audit. This survey includes more data gathering and engineering analysis, and as such, the report includes a more thorough mapping of the building’s performance. [38] When collecting and analysing information regarding the building and its systems the energy performance may be established, and this is often done by utilizing one KPI or more [17].

2.5.2 BBR verification method

The BBR includes two procedures for verifying the building’s energy performance using known consumption values for each subsystem; one using normal year correction, based on the primary energy carriers supplying the building and the other using a previous energy model [31]. However, as the second method is not well detailed in the BBR it is instead evaluated under section 2.5.3 and only the first is attributed to this verification method. Therefore, the steps for residential buildings are as follows:

1. Normalize energy for DHW:

Delivered energy for tap water heating excluding Hot Water Circulation (HWC) losses are to be replaced by a set value. This may be reduced by energy which has been generated on the building’s grounds using solar, wind, air or water energy, and used for tap water production. The set value is to be calculated according to one of the methods described below [31, p.11]; where 25 is the standard energy use for DHW in residential buildings [kWh/m2,year], ηDHW is the efficiency of the hot water production, VDHW

is the consumed domestic hot water volume [m3], and 55 is the BBR assumed total temperature lift [K].

a. EDHW = (25·Acond/η_DHW) b. EDHW = V_DHW· 55/η_DHW

2. Normalize energy use due to indoor temperature deviations:

If indoor air temperature deviates from 21°C due to reasons other than faulty installations, the energy use shall be corrected with 5% per deviated degree [31, p.12].

3. Normalization of energy use due to internal gains deviations:

Heating and cooling energy use may be corrected if the tenant energy consumption is more than 3 kWh/m2,year larger than the standard tenant consumption [31, p.13]. The correction factor is to be calculated according to the equation below; where E∆ is the tenant energy deviation [kWh/m2,year], f is the percentage of tenant energy which is assumed to contribute to heating the building, η_heating is the yearly efficiency of the heat source and theatingis the length of the heating season [h].

a. Ecorrected = (E_∆· f · A_cond/η_heating)· (t_heating/8760) 4. Normal year correction:

Ambient temperature dependent demand shall be normalized using the SMHI Energy index [31, p.13].

The current SMHI Energy Index method, however, is more suited for older multifamily building complexes that were built during the 60’s. Therefore, the DegreeDay method may be more applicable. [39], despite the fact that DegreeDays are a blunter tool for correcting the energy use as it does not consider all of the factors that the EnergyIndex-method does.

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2.5 verification methods 17

Commercial buildings follow the same steps, however certain equations for correction have been modified. Firstly, the DHW correction uses 2 kWh/m2,year as a standard value for the energy used in the DHW production. Secondly, the standard internal gains are not given, but instead referred to the energy use calculated as an earlier KPI, or the energy use of a peer building.

2.5.3 SVEBY verification method

SVEBY includes two recommended methods for verifying energy performance using consumption data from subsystems; a) one using measured data and correcting these for a normal year, and b) another which uses the previous energy simulation model and new input data. These are separately described hereafter.

a. using measurement data

This method is based on performing an audit and collecting data regarding the subsystems’

energy consumption and then comparing these values to the earlier calculated or typical values; where SVEBY details non-negligible deviations between the two data sets, listed in table 5. If the deviation exceeds the stated values, corrections of the data sets are performed.

The results may then be used for calculating the energy performance, which can be used to verify or refute the previous performance result. [40, p.9]

Table 5: Non-negligible deviations for different subsystems.

Parameter Non-negligible deviation

Domestic hot water >±3 kWh/m2,year

Indoor temperature, Winter >±1.0 °C Indoor temperature, Summer >±1.0 °C

Facility electricity >±3 kWh/m2,year

Tenant electricity >±4 kWh/m2,year

Operational hours, weekdays >2 h/day Operational hours, week-end >2 h/day

Air flow, present >20% l/s,m2

Air flow, not present >20% l/s,m2

Airing >3 kWh/m2,year

b. using a previous energy model

If the energy simulation model is available and is still representative of the current activities, this may be used for verifying the energy performance. By using repeated direct measurement data as new inputs, e.g. ventilation flows and temperature settings, and re-simulating the model using these, energy consumption values that are closer to reality may be obtained. [40, p.11] Using this for calculating the total energy performance can then verify or refute the previously calculated result.

The same program and energy model is to be used for this sort of correction, where if the program version has been updated the original model should be simulated as is in order to set a new baseline. Thereafter the input data is to be replaced by measured data over measured consumption deviations where able. [40] The correction should, according to recommendations, at least cover DHW energy, indoor temperature deviations and tenant electricity deviations [31]. Non-negligible deviations are the same as for the method using measurement data, that is the deviations contained in table 5 are to be used as references.

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18 2.5 verification methods

2.5.4 Re-simulating energy model

A verification method which is sometimes used within the construction and energy industry is to re-simulate a previous phase’s energy model using corrected inputs and assumptions relating to actual usage and performance [27,41]. This method differs from the one described previously under section 2.5.3.b. in that this re-simulation includes the correction of other parameters such as the building envelope materials, if these have changed compared to the earlier model. That is, not only certain tenant behaviour related inputs are able to be changed.

However, this is not a standard verification method which is further defined other than that the building should be re-simulated using refined input data. As such there are no stated non-negligible deviations to refer to when evaluating what values should be changed or similar. Therefore, one can take two approaches to this; 1) all input data values that have been found to be misrepresentative of the evaluated state of the building and its systems would be changed, or 2) input data as well as intentional energy system design and other similar in-built changes are both to be corrected. The difference lies in the extent of change that is to be performed within the model. Due to the lack of a standard, as mentioned, the choice between the alternatives lies with the modeller in charge of this re-simulation and their perception of how representative different parts of the simulation’s system are.

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case study, model & verification methods 19

3 case study, model & verification methods

Figure 9: Paradiset, located on Kungshol- men in Stockholm.

The multi-family building complex known as Paradiset, as seen in figure 9 [42], located on Kungsholmen in Stockholm, is the case study which was evaluated in this project. This building was first evaluated by Imek in 2014;

the energy system design was set and energy use was estimated. The objective was to achieve a certification level of silver for Svensk Miljöbyggnad, which is further defined under section 3.1.1. Construction then started in 2015 and in 2017 the complex was final- ized and tenants moved in. The commercial spaces were occupied at different times during 2018. The 14 story high residential building houses 103 apartments and associated storage and garage spaces, as well as four spaces functioning as commercial facilities on floor 1 and 2. This case study is based on the 2014 delivered energy report produced by Imek, as well as available current and historical data.

3.1 Paradiset, 2014

Under this section the information that was documented in 2014 regarding the building, activities and system configurations is presented. The project description of the building stated that the building was to mainly operate as a residential building housing 104 apartments, but that the energy and water systems were to anticipate four commercial spaces of unknown activity on the bottom two floors as well. The specified area in the energy report was 13 485 m2 of which 11 300 m2 made up the conditioned area. The specific area division is presented in table

Table 6: Presented area division in the building, from 2014.

Description Conditioned area [m2]

Apartments 8 900

Facility areas 1 870

Commercial spaces 530

Total conditioned 11 300

Description Unconditioned area [m2]

Garage 2 185

Description Area [m2]

Total 12 485

Envelope area 10 500