1 Background 

Worldwide, cities are responsible for more than 70% of global energy demand and 75% of greenhouse gas emissions. To meet targets for energy efficiency and greenhouse gas emission reduction, particularly in urban areas, it is necessary to adapt conventional sustainable urban development paradigms with new urban energy planning strategies. Urban building energy modeling (UBEM) is an emerging field spanning from building-level energy models to district- , city- and national-level energy solutions can be used as a means to support city-planner and decision-makers in our transition towards energy efficiency and sustainability. 

As in individual building energy modeling, the UBEM modeling procedure is composed of several steps, including development of energy models of buildings from their geometric and non-geometric properties. However, data collection and model characterization for each indi- vidual building in a city seems impossible. Instead, it is suggested to abstract the building stock into a number of building archetypes representing the main characteristics of buildings in a city. One of the most common approach in archetype development relies on deterministic methods for classification of building stock based on parameters such as use-type, age, shape, floor area and energy use. The classification of buildings, using readily available data from public or municipal data sets facilitates the modeling procedure and considerably reduce the computation time. 

In Sweden, since 2006, the national databases for energy performance certificates (EPC) collected to underpin the energy efficiency in Swedish building stock. An EPC includes the building’s heated floor area, energy consumption for heating and cooling, domestic hot water and electricity use, heating ventilation and air conditioning systems (HVAC), number of stories, and construction year, which can be used as a norm in classification of buildings and development of archetypes in UBEMs. 

Once the building archetypes are prepared, to obtain the aggregated energy demand of the city, the energy models of the buildings can be generated either for each chosen archetypes or for each individual building using the archetype characteristics. If only the representative archetypes are modeled, the simulation results have to be scaled up. The upscaling of the results from archetypes to clusters and the whole city is done by means of multiplication factors such as floor weighted area or number of buildings in each class. 

Figure 1: Archetype development for urban-scale energy simulation 

2 Thesis 1: Archetype Development for Urban-Scale Energy Use in Existing Districts 

2.1 Aim 

In this master thesis, the aim is to develop an archetype database for existing buildings in the city of Uppsala, based on deterministic information available in energy performance certicates(EPCs), and validate them against energy use data. By modeling the archetypes and aggregating their simulation results, we expect to be able to accurately estimate the energy demand for the whole city. 

2.2 Methods 

• Collecting the EPCs and measured energy use data 
• Classification the existing building stock in Uppsala under a few building archetypes based on age, type, heating systems, and energy use, included in EPCs. 
• Identification of the most representative characteristics of each class. 
• Development the thermal energy models of each archetype in one of the simulation engines, such as EnergyPlus, IDA ICE, TRNSYS or VIP-Energy and calibrate them based on measured energy use data. 
• Estimation the urban-scale energy demand of buildings by aggregating the results over the whole city. 

2.3 Requirements 

It is expected that candidates have previous knowledge in building physics and energy effi- ciency in buildings.It is also required to have experience in using simulation tools, such as EnergyPlus, IDA ICE, TRNSYS or VIP-Energy. Any competencies in database design and scripting in Matlab or Python is a merit. 

3 Thesis 2: Archetype Development for Urban-Scale Energy Use in Planned Districts 

3.1 Aim 

In this master thesis, the aim is to develop a method for constructing building archetypes for new urban areas, in contrast to existing ones. The idea is to use information from ongoing and very recent construction projects to identify representative models for new buildings and their typical spatial arrangement, in order to be able to make urban-scale predictions of energy use from city expansion. 

3.2 Methods 

• Collecting the information on very recent and planned construction projects. 
• Identification of the most common characteristics of the planned buildings, geometrical and non-geometrical. 
• Development of representative archetypes. 
• Development of thermal models of future archetypes in one of the simulation engines, such as EnergyPlus, IDA ICE, TRNSYS or VIP-Energy. 
• Conducting an urban energy planning study for a planned districts. 

3.3 Requirements 

It is expected that applicants have previous knowledge in building physics and energy effi- ciency in buildings. It is also required to have experience in using simulation tools, such as EnergyPlus, IDA ICE, TRNSYS or VIP-Energy. Any competencies in working with 3D tools (SketchUp, AutoCAD, Revit, ...) is a merit. 

4 Contact persons 

Fatima Johari, fatemeh.johari@angstrom.uu.se

Joakim Widén, joakim.widen@angstrom.uu.se 

5 References 

Here is a list of some the suggested literature: 

̈Osterbring, ́E. Mata, L. Thuvander, M. Mangold, F. Johnsson, and H. Wallbaum, “A dif- ferentiated description of building-stocks for a georeferenced urban bottom-up building-stock model,” Energy Build., vol. 120, pp. 78–84, May 2016.

Tuominen, R. Holopainen, L. Eskola, J. Jokisalo, and M. Airaksinen, “Calculation method and tool for assessing energy consumption in the building stock,” Build. Environ., vol. 75, pp. 153–160, May 2014.

Sokol, C. Cerezo Davila, and C. F. Reinhart, “Validation of a Bayesian-based method for defining residential archetypes in urban building energy models,” Energy Build., vol. 134, pp. 11–24, Jan. 2017.

F. Reinhart and C. Cerezo Davila, “Urban building energy modeling – A review of a nascent field,” Build. Environ., vol. 97, pp. 196–202, Feb. 2016.

Pasichnyi, J. Wallin, F. Levihn, H. Shahrokni, and O. Kordas, “Energy performance certificates — New opportunities for data-enabled urban energy policy instruments?,” En- ergy Policy, vol. 127, pp. 486–499, Apr. 2019.

Nageler, G. Schweiger, H. Schranzhofer, T. Mach, R. Heimrath, and C. Hochenauer, “Novel method to simulate large-scale thermal city models,” Energy, vol. 157, pp. 633–646, Aug. 2018.

S. Monteiro, A. Pina, C. Cerezo, C. Reinhart, and P. Ferr ̃ao, “The Use of Multi-detail Building Archetypes in Urban Energy Modelling,” Energy Procedia, vol. 111, pp. 817–825, Mar. 2017.

S. Monteiro, C. Costa, A. Pina, M. Y. Santos, and P. Ferr ̃ao, “An urban building database (UBD) supporting a smart city information system,” Energy Build., vol. 158, pp. 244–260, Jan. 2018.

Yun, R. Luck, P. J. Mago, and H. Cho, “Building hourly thermal load prediction us- ing an indexed ARX model,” Energy Build., vol. 54, pp. 225–233, Nov. 2012.

Johari, A. M. Nilsson, M.  ̊Aberg, and J. Widén, “Towards urban building energy mod- elling: a comparison of available tools,” . BUILDINGS, p. 10.

Dall’O’, A. Galante, and M. Torri, “A methodology for the energy performance classi- fication of residential building stock on an urban scale,” Energy Build., vol. 48, pp. 211–219, May 2012.

Cerezo Davila, C. F. Reinhart, and J. L. Bemis, “Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets,” Energy, vol. 117, pp. 237–250, Dec. 2016.

Cerezo, J. Sokol, C. Reinhart, and A. Al-Mumin, “Three methods for characterizing build- ing archetypes in urban energy simulation. A case study in Kuwait City.”

Cerezo, J. Sokol, S. AlKhaled, C. Reinhart, A. Al-Mumin, and A. Hajiah, “Compari- son of four building archetype characterization methods in urban building energy modeling (UBEM): A residential case study in Kuwait City,” Energy Build., vol. 154, pp. 321–334, Nov. 2017.

Built Environment Energy Systems Group Engineering Sciences Department Uppsala University