To help solve the climate crisis, municipalities are increasingly modifying their building codes and offering incentives to create greener buildings in their cities. But, city planners find it difficult to set and assess these policies, as most municipalities do not have the types of data used in urban building energy modelling (UBEM) that would allow their planners to forecast the impacts of various building policies. This thesis offers techniques for operating in this data-poor environment, presenting best practices for developing data-driven archetypes with machine learning, demonstrating inference of parameter values to improve archetypes by using surrogate modelling and genetic algorithms, and a demonstration of techniques for assessing residential retrofit impact in a data-limited environment, where data is neither detailed enough to create an in-depth single archetype study, nor broad enough to create an UBEM model. It will be shown that inference techniques have potential, but need a certain amount of detailed data to work, though far less than traditional UBEM techniques. For performing residential retrofit, it will be shown the lack of ideal detailed data does not present an overwhelming obstacle to drawing useful conclusions and that meaningful insight can be extracted despite the lack of precision. Overall, this thesis shows a data-poor environment, while challenging, is a viable environment for both research and policy modelling. / Graduate

Buildings and the built environment account for a significant portion of the global energy use and greenhouse gas emissions, and reducing the energy demand in this sector is crucial for a sustainable energy transition. This highlights the need for accurate and large-scale estimations and predictions of the future energy demand in buildings. Urban building energy modeling (UBEM) is an analytical tool for precise and high-quality energy modelling of city-scale building stocks, which is growing in interest as a useful tool for researchers and decision-makers worldwide. This thesis contributes to the understanding and future development in the field of UBEM and multi-variate cluster analysis. Based on a review of contemporary literature, possible improvements and knowledge gaps regarding UBEM are identified. The majority of UBEM studies are developed for similar applications, and some challenges are close to universal. Difficulties in data acquisition and the identification and characterisation of building archetypes are frequently addressed. Drawing on conclusions from the review, a clustering methodology for identifying building archetypes for hybrid UBEM was developed. The methodology utilised the k-means cluster analysis algorithm for multiple diverse parameters, including socio-economic indicators, and is based on open data sets which eliminates data acquisition issues and allows for easy adaptation. Building archetypes were successfully identified for two large data sets, and proved to be representative of the sample building stock. The results of the analysis also show that the error metric values diverge after a certain number of clusters, for multiple runs of the algorithm. This property of the algorithm in combination with the use of both existing and novel error metrics provide a reliable method for determining the optimal number of clusters. The methodology developed in this thesis enables for an improved modelling process, as a part of a complete UBEM.

Complying with the Paris agreements requires substantial efforts in the building sector, and especially within the existing building stock which is responsible for a considerable amount of emissions and energy consumption. This master thesis focuses on the residential district of Minneberg, located in the west of Stockholm in Bromma. The urban building energy modelling (UBEM) approach is used to model the situation of the current district. This method uses real-life data provided by the district, as well as information found in energy performance certificates and in public databases. Based on that, a virtual archetype building representing the whole district is modelled and calibrated. Suitable energy-efficient solutions that can contribute to reducing the energy consumption are identified and applied in two different scenarios. The first scenario consists in retrofitting the current building stock, while the second represents the case where the building has to be designed from scratch today to comply with Boverket’s requirements on nearly zero-energy buildings ("New Minneberg" scenario). The aggregation of the results shows that the current district is already quite energy-efficient, with the installation of solar panels seeming to be the only economically viable retrofitting option. As for the "New Minneberg" scenario, it is possible to comply with the requirements and achieve a C-class building by reducing the primary energy consumption, but that comes at the expense of a higher actual energy consumption. / Att följa Parisavtalen kräver stora ansträngningar inom byggsektorn, och särskilt inom det befintliga byggnadsbeståndet som står för en betydande mängd växthusgasutsläpp och energianvändning. Examensarbetet fokuserar på det svenska bostadsområdet av Minneberg, som ligger i västra Stockholm i Bromma. UBEM-metoden (urban building energy modelling) används för att modellera situationen i det nuvarande distriktet. Metoden använder verkliga data från fastighetsområdet, liksom information som finns i energideklarationer och offentliga databaser. Därefter modelleras och kalibreras en virtuell arketypsbyggnad som representerar hela distriktet. Lämpliga energieffektiva lösningar som kan bidra till att minska energiförbrukningen identifieras och tillämpas i två olika scenarier. Det första scenariot består i renovering av det nuvarande byggnadsbeståndet, medan det andra representerar fallet om byggnaden hade designats från grunden idag, för att uppfylla Boverkets krav på nollenergihus ("New Minneberg" scenario). Resultaten visar att det nuvarande distriktet redan är ganska energieffektivt, där installation av solpaneler verkar vara den enda ekonomiskt lönsamma åtgärden. Gällande "New Minneberg" scenariot är det möjligt att uppfylla kraven och uppnå en C-klass byggnad genom att minska primärenergitalet, men det resulterar i en högre verklig energiförbrukning.