Archetype identification in Urban Building Energy Modeling : Research gaps and method development

Sammanfattning: 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.