Energy Building Stock Simulation and Planning for Small

Municipalities

A Web-based Urban Energy System Model for Potential Analysis and Citizen

Participation

Simon Schneider, Thomas Zelger and Pierre Laurent

Department Renewable Energy, Fachhochschule Technikum Wien, Höchstädtplatz 6, 1200 Vienna, Austria

Keywords: City Energy Simulation, Building Stock, Archetypes, Future Energy Scenarios, Citizen Participation.

Abstract: City planning for sustainable energy and development goals in small municipalities suffers from unresolved

complexities, insufficient data and prohibitive cost. We propose a low-cost urban energy system for building

stock assessment and urban energy planning by combining archetype-based dynamic energy demand and

coverage simulation with incentive-based citizen participation as a means to improve data quality. Combining

a white-box based physical approach with multi-dimensional archetypes for individual building energy

demand and supply estimation with statistical top-down validation and calibration, we obtain an energy

simulation method that requires less data on the building stock than other typical methods.

1 INTRODUCTION

Changing energy use is a physical necessity. The

Paris Climate 2015 established moderate goals for

this transition until 2050. In spite of efforts around the

globe, progress is dramatically slower than necessary

and time is running out.

In small cities, resources, expertise and data may

not be as abundantly available as in large “Smart

Cities”. Consequently, city planners struggle to

implement effective measures to pursue their

sustainability goals. This paper aims to highlight a

feasible approach of energy simulation through

archetype classification on the example of the city of

Korneuburg, Austria. The city of around 13 thousand

inhabitants aims to achieve their self-proclaimed goal

of energy self-sufficiency by the year 2036

(Stadtgemeinde Korneuburg, 2014).

The first step to this end is to establish certainty

of the current energy usage: Once known, cities can

develop roadmaps from the current to the desired

situation. This encompasses all emission-related

aspects of energy usage such as mobility, buildings

and industry.

City planners need a framework to predict energy

demands and coverages for entire cities and compare

possible effects of future measures (Sousa et al.

2012). Examples of such projects are abundant (see

Tardioli et al., 2015, Caputo et al., 2012), and yet it

remains difficult to generalize the approaches to small

cities. A number of factors limit the usage of such

tools: First, the required data for the framework may

not be readily available and its aggregation cost-

prohibitive or hindered by privacy concerns (Ballarini

et al., 2011 and Ballarini et al., 2014). Second, the

expertise required to operate such tools may not be

available within smaller administrative bodies.

Finally, the resources required to sustain complex

energy estimation and planning systems may not be

available.

Typical approaches and solutions fall in one of

three categories: (i) High-accuracy physical energy

estimation tools that incorporate all energy aspects of

a city. They tend to be expensive, as they require

detailed and extensive physical data and expertise

(engineering method), (ii) well established tools for

narrow areas of city planning such as heating demand

estimation, grid planning, etc. Their ability to

visualize energy system states and changes are

therefore limited. And (iii): Holistic city planning

solutions –typically trading detail for accessibility.

344

Schneider, S., Zelger, T. and Laurent, P.

Energy Building Stock Simulation and Planning for Small Municipalities.

DOI: 10.5220/0006804403440351

In Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS 2018), pages 344-351

ISBN: 978-989-758-292-9

Existing research on quantifying a city’s building

stock energy system focuses heavily on demand

estimation, specifically heating energy demand. A

holistic city simulation must also include methods of

quantifying how these energy demands are met, and

their corresponding greenhouse gas emissions.

Finally, existing building stock should be compared

to varying future scenarios. The energy framework

for small municipalities is required to answer: What

energy usage and associated emissions are attached to

the current building stock? Which saving potentials

are available and where? What could happen if we

assume certain transition rates (which can represent

political measures)? The energy framework is also

required to provide a number of technical and

practical features and constraints to be feasible in a

non-scientific context: Data management: Possibility

to integrate different data types, and versioning of this

data, user-friendly interfaces: Comfortable, intuitive

and robust design

The importance of the last two points cannot be

understated. It must be ensured that data aggregation

can continue on the running system and can be done

in an economically feasible way.

2 METHODOLOGY

Three key components form the proposed energy

framework are: (i) An energy database, (ii) an energy

building simulation engine and (iii) a web-based

front-end user interface, later referred to as the “web-

platform”. The focus of the following section is to

describe the energy simulation method. Thereafter, a

brief overview on the data model and web platform is

given.

2.1 Energy Simulation

According to (Swan and Ugursal., 2009) and

(Tardioli et al., 2015), large scale building stock

energy simulation can be classified into three

categories: White-box based approaches employ

physical energy models on a city scale, typically

extrapolating from single buildings through

distribution, archetype or sample methods.

Black-Box based approaches use statistical

methods such as regression, decision trees and

artificial neural networks to estimate energy

consumption based on existing data (Tardioli et al.,

2015). Although physical models have their merit in

obtaining detailed results for specific buildings, the

amount of parameters and computation time required

usually prohibits their use for city scale application

(Kavgic et al., 2010, Keirstead et al., 2012, Pervez et

al.,2014). A purely black-box based approach (as

employed Humeau et al., 2013 and Powell et al.,

2014) extrapolating from existing data is not

sufficient, as there is no data on future, more efficient

buildings to extrapolate from. Finally, grey-box based

approaches can be seen as a combination of both

white- box and block-box based techniques.

Figure 1: The components of the implemented energy web-

platform and their interaction.

For single building simulation, and more so for

estimation of future building and plot potentials, a

physical approach is necessary. It is however virtually

impossible to collect sufficient data on each building

to apply physical methods individually (Ballarini et

al., 2011 and Ballarini et al., 2014). Instead, the first

important modelling decision is to appropriate each

building from the building stock with a virtual

building type, typically referred to as an archetype

(Swan and Ugursal, 2009), designed to reflect

determining features such as

 Surface-to-Volume ratio (compactness)

 The share of solar transmitting surfaces on the

building envelope and their orientation

 The thermal transmission properties (of building

materials)

 Usage of the building (for residential, commercial

and other purposes)

 Energy system (including all energy uses such as

heating, cooling, (de-)humidification, end-use,

etc.)

The relative influence of these parameters have

been extensively investigated and discussed: (Schüler

et al., 2015) found through statistical regression

analysis on 57.000 buildings in Geneva that

microclimate, which they describe through the

parameters of average building height, horizontal

density and average irradiation, play an important

role. (Aksoezen et al., 2014) argued that the most

influential building parameters on energy demand are

Energy Building Stock Simulation and Planning for Small Municipalities

345

Figure 2: Energy simulation data model divided in demand archetype classification (light red) and building energy demand

calculations (dark red), as well as coverage archetype combination from parameters on the renewable energy potential of the

building and common coverage typology through conventional energy systems (grey box).

compactness and building age. In any case,

archetypes allow direct control over the trade-of

between modelling effort and model accuracy

(Korolija et al., 2013).

The energy simulation method proposed in this

work is combining data from the geoinformation

system of the municipality of Korneuburg, statistical

data on city quarters and archetypes to quantify the

energy use intensity of the building stock (compare

Caputo et al., 2012 for a detailed description of this

approach).

The archetypes developed in this study divide

between demand archetypes and coverage or supply

archetypes. The demand archetypes are

characterized by the combinations of (i) typical

building geometries, (ii) typical physical properties of

the thermal hull, (iii) typical usage profiles, (iv)

construction method and (v) orientation of the

building (as can be seen in Figure 2). Typical physical

properties are assumed according to vintage of the

building as surveyed in (Zelger and Waltjen, 2009)

and reflect typical building materials from a certain

time. The parameters of building construction method

and usage type follow the same categorization in this

source.

Similarly to (Kazas et al., 2015), the orientation

parameter was quantified into eight equally sized

segments, as can be seen in Figure 3. Since

orientation could only be derived from GIS building

footprints, it was defined by the direction of the

largest building façade. As there is no possibility to

distinguish between a building and its rotation by

180° from a rectangular building footprint alone, the

two cardinal directions opposite each other cannot be

distinguished with this method and subsequently,

orientation of archetypes can only be equally

distributed between the two. For the most part, this is

of no concern, as archetypes are mostly, but not

generally, mirror symmetrical and thus invariant to

180° rotation. However, for highly unsymmetrical

building geometries this approach is not directly

applicable.

One of the key predictors of a building stock’s

energy demand is building age: As pointed out by

(Aksoezen et al., 2014), the dependency of heating

energy demand on building age is non-linear.

However, a statistical approach with archetypes

allows for estimations with low margins of error.

In the case of Korneuburg, data on building age

was available as a statistical distribution for around

hundred districts (“sub-zones”) with five to fifty

individual buildings each from (Galosi et al., 2012).

Since the data model needs to depict not only

present but also future conditions, the coverage

simulation is required to factor in energy demand,

coverage systems, as well as renewable energy

potentials on site. To this end, we employed coverage

archetypes, that are similar yet more numerous than

the energy demand archetypes described above, and

combined from two parts: The first part is a

parametric typology of “common” coverage systems

such as gas or oil powered heating systems. They are

“common” in the sense that they typically cover the

energy demand of the building stock, but this is

purely semantical. The methodological distinction to

the other parametric types is that opposed to the latter,

the “common“ coverage systems do not have any

constraints in their application

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346

Table 1: Geometric types constituting the building stock of the city of Korneuburg, as well as possible densification variants.

Geometry

Detached

single family

house

Modern double

family house

Attached

Single family

house

Multifamily

house

Retail Office

Building

stock

Gross floor

areas [m²]

209 418 108 760 750 510

Densifi-

cation

variants

Retail and Office Densification

geometry types currently in

development

Gross floor

areas [m²]

500 662 162 1064

Figure 3: Distribution of building orientations in

Korneuburg. Most parts of the city align with the river Danu

be (flowing from North West to South East).

Not so for the group forming the “unique renewable

potential” (Figure 2). These parameters determine to

extent to which renewable energy potential can be

used for any given building. The “solar” parameter

describes different rulesets that determine which

areas of a building geometry type can be used for

solar energy. These rules are not specific to certain

building geometries. However, since surface

suitability for solar usage depends heavily on building

geometry, specific solar type rulesets with certain

geometries in mind can yield better results than

generic rules. Figure 4 shows how three different

solar ruleset types are applied to two different

geometries.

Since the orientation of the building archetype’s

geometries is unknown and statistically distributed, it

is necessary to distinguish between areas of technical

solar potential (the last step of solar ruleset types) and

the actual feasibility of these potential areas due to

irradiation depending on orientation. All roof and

façade potential areas are divided in primary areas

(with the highest irradiation of the roof or façade

areas respectively) and secondary areas (the rest). In

a last step, the actual feasibility is determined by

combining the possible orientations with a certain

Figure 4: Exemplary application of three solar typologies

onto different building geometries: The "cost effective"

typology classifies 90% of the south-most facing, sloped

roof surface, and/or 40% of horizontal roof areas at optimal

orientation (tilt and azimuth) as solar potential. “Shogun”

classifies all roof areas (automatically adding 1m wide

canopies), and the hypothetical “kukla” typology assumes

all building surfaces as solar potential (resulting solar

potential areas in yellow).

190

1066

214

1110

190

1066

214

1110

North

NortEast

East

SouthEast

SouthWest

West

NorthWest

Energy Building Stock Simulation and Planning for Small Municipalities

347

“exploitation strategy”, which determines the share of

primary and secondary roof and façade surfaces

actually used, as well as which technology, thermal,

photovoltaic or hybrid is being applied to each. Figure

5 shows primary and secondary areas for different

orientations.

Figure 5: Primary (green) and secondary (purple) solar roof

potential areas for different orientations according to an

exploitation strategy.

The resulting simulation method for individual

buildings provides dynamic calculation methods for

thermic building simulation in a single thermal zone

and yields energy usage, energy supply, humidity and

temperatures in a quarter-hour resolution, as well as

primary energy balances and greenhouse gas

emissions. It was developed upon (Rondoni, et.al.

2015), which was validated using a TRNSYS

reference model.

For the purposes of city-scale scenarios, the

calculation is separated in two steps: First, the

detailed energy demand of all predefined archetypes

is calculated. Then, results are aggregated according

to the spatiotemporal distribution of these archetypes

throughout the city and the years of the scenario to

obtain a city estimate. The building stock is derived

from the archetypes to match its actual energy

reference area. Area-specific values are assumed to

equal those of their corresponding archetype. The

results can be compared to top-down energy use data

from the local energy supplier to calibrate the

archetypes and their distribution throughout the city.

The last important feature of the energy

simulation method is future energy scenarios. This is

accomplished through virtual “measures”, which is a

transition rule with the following parameters: (i) a

building parameter such as geometry or energy

system to transition, (ii) a filter that determines which

archetypes should be affected, (iii) a target value for

the parameter, and (iv) a transitioning rate per year

and (v) a timespan in between years.

2.2 Database and Web-platform

As (Swan and Ugursal, 2009) and (Kavgic et al.,

2010) have pointed out, it is generally difficult to

obtain reliable information from end-users and

residents due to privacy and collection issues and

cost. As (Aksoezen et al., 2014) put it: “There is a

missing link in practically all models between the

estimated energy consumption and the real energy

consumption”.

The approach of this study is to make the results

of the simulation available to the end-users and

residents themselves on a dedicated web-platform.

Here, the citizens are incentivised to voluntarily

partake in the data collection in two ways:

First, the web platform offers detailed simulation

of individual buildings, also in virtual refurbishment

variations as a service to residents. They can use this

service to explore the opportunities for their own

home. Since it is in their self-interest to obtain as

reliable results as possible, they are incentivised to

produce as detailed information about their building

as possible.

The second incentive is through collaborating on

a common goal. As projects such as Wikipedia have

impressively shown, members of a community have

an innate desire to contribute in a meaningful way

towards a common goal. In the case of the web

platform of the particular city of Korneuburg, the goal

is to achieve municipal energy self-sufficiency by

2036 (Stadtgemeinde Korneuburg, 2014). It is

important to point out, that this goal was not imposed

by the city administration or other government, but

rather was the result of an inclusive development

project by citizens, politicians and other stakeholders

to shape the roadmap into the city’s future.

3 RESULTS

With this city energy model it is possible to quantify

and visualize the building stock’s energy demand

(Figure 8), energy use intensity and greenhouse gas

emissions (Figure 7). Furthermore, future scenarios

can be evaluated by adjusting the existing archetypes

and transitioning them with archetypes that reflect

future, more energy-efficient buildings. Different

densification strategies can also be examined (Figure

6), as well as the quantitative effect of retrofitting

measures. For the goal of energy-autonomy, as in the

case of Korneuburg, it is possible to investigate

different mixes of archetypes, whether they are

sufficient to meet the goals and how possible

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transitioning roads require different rates of

retrofitting and other energy policy measures.

Citizens are incentivised to visit the energy web

platform, as they can investigate their city for

themselves and discover the (im)possibilities of

certain futures. By providing examples of best-

practice buildings, the web platform aims to connect

the overarching goals of a city to the individual action

space of their residents. If they decide to, they can

even provide detailed data on their own building, such

as energy audits, archetypical information and energy

bills – thus improving the database, which is

primarily based on statistical assumptions (Figure 9).

Figure 6: Densification potential of plots within existing

density regulations from high (red) to low (blue).

Figure 7: Greenhouse gas emissions per building and year

from blue (< 40 kg CO

equiv./m²a) to red (> 180 kg

equiv./m²a).

Since resource efficiency is a major concern, the

data model is able to combine different datasets.

Apart from user input, it is also possible to

incorporate measurement and sensory data, which can

be used to validate the underlying modelling

assumptions. A role-management system enables

control over rights and privileges of all participants.

Municipal energy planners can then use the platform

to maintain their building energy data.

Figure 8: Heating energy demand from green (< 15

kWh/m²a) to red (> 150 kWh/m²a) for the existing building

stock (left) and a future city scenario with 80% of the

building stock retrofitted to passivhouse standard (right).

Figure 9: Web-platform user interface for detailed building

simulation and result presentation.

4 DISCUSSION

With this approach we aim to combine the strengths

of top-down statistical methods (i.e. bridging the gap

between simulation and reality and giving insight in

user behaviour), and engineering methods, which are

the only ones capable to evaluate the impact of new

Energy Building Stock Simulation and Planning for Small Municipalities

349

building solutions (Swan and Ugursal, 2009). The

number of archetypes derived in this study from

building parameters is around 58 excluding and 180

including the orientation parameter. For comparison,

(Mata et al., 2014) compiled 593 archetypes including

residential and non-residential buildings in France,

Germany, Spain and the UK.

The current implementation of the simulation

method focuses on the individual building. In reality,

a plot can and often does house multiple buildings.

Their energy demand can still be calculated

separately, however their potential for renewable

demand coverage and densification through

refurbishments are interconnected. The potential of

ground heat pumps for example depends on the area

of unsealed land, which remains when subtracting the

building’s footprints from the plot area.

Another example is densification: Construction

laws impose thresholds on building density for plots

of land. However, the plot specific densification

potential cannot be algorithmically allocated to each

building. This suggests plots instead of buildings to

constitute the central aggregation point for data. This

will require the development of plot archetypes,

which in turn can be comprised of various building

archetypes.

One notable example of an energy web-platform

is the “energy app” developed by the city of Glasgow

(Glasgow City Council, 2015). Although the method

of building stock estimation is different, it

incorporates similar features for citizens to explore

and interact with – especially the interactive 3D

online map.

3D based methods, most notably CityGML, have

demonstrated great potential for integrating GIS and

other georeferenced data (e.g. LIDAR) with energy

metadata for energy simulation purposes, as

(Agugiaro, 2014) demonstrated for the city of Trento.

(Monsalvente et al., 2015) developed a „modular

physical [building simulation] model in INSEL“,

which can be generated automatically and connects

directly to the CityGML open source standard.

However, its physical model can only be employed

for buildings with a level of detail (LOD) of at least

two, which includes roof shapes. Since roof geometry

is not commonly obtainable data in small

municipalities, the proposed approach in this paper

focuses on building footprints, which correspond to a

building LOD of zero, in combination with a small

number of geometric archetypes.

However, obtaining a sufficient number of

archetypes can be challenging for larger cities, or

cities with a very diverse and heterogeneous building

stock. However different, the approaches are not

mutually exclusive, as the building input data for

energy demand are very similar (compare Wate and

Coors, 2015). The authors aim to develop our

simulation method to be compatible with the

CityGML standard, and it’s energy application

domain extension (ADE).

5 CONCLUSIONS AND

OUTLOOK

Combining a white-box based physical approach with

multi-dimensional archetypes for individual building

energy demand and supply estimation with statistical

top-down calibration capabilities, we obtain an

energy simulation method that requires less data on

the building stock than other typical methods.

Nevertheless, further refinement on archetypes and

distributions will be necessary.

Once the platform opens up to a wide base of

users from the municipality, it will be interesting to

study the actual engagement of the users, as well as

the quality and quantity of their supplied data.

As with most projects on building stock, data

collection and processing is time-consuming and

error-prone. We fully agree with the suggestion of

(Keirstead et al., 2012) for “a centralized repository

for cities’ energy related data”. We believe that open

source standards such as CityGML are necessary and

need to be developed and put to use faster in order to

meet our goals in the future. To this end, we aim to

develop our simulation method to be compatible with

the CityGML standard, and it’s energy application

domain extension (ADE).

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the Austrian

Climate and Energy Fund for providing funding for

the research project “Way2Smart Korneuburg”.

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