abstract: City administrators and real-estate developers have been setting up rather aggressive energy efficiency targets. This, in turn, has led the building science research groups across the globe to focus on urban scale building performance studies and level of abstraction associated with the simulations of the same. The increasing maturity of the stakeholders towards energy efficiency and creating comfortable working environment has led researchers to develop methodologies and tools for addressing the policy driven interventions whether it’s urban level energy systems, buildings’ operational optimization or retrofit guidelines. Typically, these large-scale simulations are carried out by grouping buildings based on their design similarities i.e. standardization of the buildings. Such an approach does not necessarily lead to potential working inputs which can make decision-making effective. To address this, a novel approach is proposed in the present study. The principle objective of this study is to propose, to define and evaluate the methodology to utilize machine learning algorithms in defining representative building archetypes for the Stock-level Building Energy Modeling (SBEM) which are based on operational parameter database. The study uses “Phoenix- climate” based CBECS-2012 survey microdata for analysis and validation. Using the database, parameter correlations are studied to understand the relation between input parameters and the energy performance. Contrary to precedence, the study establishes that the energy performance is better explained by the non-linear models. The non-linear behavior is explained by advanced learning algorithms. Based on these algorithms, the buildings at study are grouped into meaningful clusters. The cluster “mediod” (statistically the centroid, meaning building that can be represented as the centroid of the cluster) are established statistically to identify the level of abstraction that is acceptable for the whole building energy simulations and post that the retrofit decision-making. Further, the methodology is validated by conducting Monte-Carlo simulations on 13 key input simulation parameters. The sensitivity analysis of these 13 parameters is utilized to identify the optimum retrofits. From the sample analysis, the envelope parameters are found to be more sensitive towards the EUI of the building and thus retrofit packages should also be directed to maximize the energy usage reduction. / Dissertation/Thesis / Masters Thesis Architecture 2017

Urban energy system planning can play a pivotal role in the transition of urban areas towards energy efficiency and carbon neutrality. With the building sector being one of the main components of the urban energy system, there is a great opportunity for improving energy efficiency in cities if the spatio-temporal patterns of energy use in the building sector are accurately identified. A bottom-up engineering energy model of buildings, known as urban building energy model (UBEM), is an analytical tool for modeling buildings on city-levels and evaluating scenarios for an energy-efficient built environment, not only on the building-level but also on the district and city-level. Methods for developing an UBEM vary, yet, the majority of existing models use the same approach to incorporating already established building energy simulation software into the main core of the model. Due to difficulties in accessing building-specific information on the one hand, and the computational cost of UBEMs on the other hand, simplified building modeling is the most common method to make the modeling procedure more efficient. This thesis contributes to the state-of-the-art and advancement of the field of urban building energy modeling by analyzing the capabilities of conventional building simulation tools to handle an UBEM and suggesting modeling guidelines on the zoning configuration and levels of detail of the building models. According to the results from this thesis, it is concluded that with 16% relative difference from the annual measurements, EnergyPlus is the most suitable software that can handle large-scale building energy models efficiently. The results also show that on the individual building-level, a simplified single-zone model results in 6% mean absolute percentage deviation (MAPD) from a detailed multi-zone model. This thesis proposes that on the aggregated levels, simplified building models could contribute to the development of a fast but still accurate UBEM.

Buildings play a central role for livability and carbon footprint of urban areas. Ambitious energy saving and emission reduction targets created a need for a new generation of decisionsupport methods and tools that allow for detailed analysis of urban energy on a large scale. Urban building energy modeling (UBEM) that has emerged recently is an efficient approach to assess energy performance of multiple buildings and system effects from urban energy interventions. However, the further upscale of UBEMs is significantly limited due to the lack of automation for building energy performance (BEP) simulations required for such models in large amounts. This thesis aimed to explore challenges for automation of BEP simulations, and to develop a prototype tool that would serve as a middleware between UBEM and BEP simulation engine, focusing on the IDA ICE simulation software. The result of this thesis is icepy — a tool for automation of BEP simulations in IDA ICE. It uses IDA ICE API and Lisp scripting to provide interaction between UBEM process and IDA ICE in order to generate initial simulation model (IDM), execute simulation and manage results in an automated way. Being implemented as a Python package, it allows to modify multiple IDMs or export simulation results with a few lines of code. The developed tool has been tested and validated for the case building in Minneberg, Stockholm. The automation capabilities provided by icepy has allowed to perform sensitivity analysis for building design parameters as was demonstrated for the window-to-wall ratio (WWR) and three various algorithms for window distribution. The resulting tool has limited functionality as it addressed building envelopes which is only one component of building simulation. However, it has proved to be an efficient approach to automate simulation process and has shown a good potential for further development of such tools. / Byggnader spelar en central roll för urbana områdens levbarhet och koldioxidavtryck. Ambitiösa mål för energibesparing och utsläppsminskning har skapat ett behov av en ny generation beslutsstödmetoder och verktyg som möjliggör detaljerad analys av städers energianvändning i stor skala. Urban byggnadsenergimodellering (UBEM) har nyligen utvecklats och är ett effektivt tillvägagångssätt för att bedöma energiprestanda för flera byggnader och systemeffekter för olika energiåtgärder inom den urban miljön. Den ytterligare uppskalningen av UBEM är dock begränsad på grund av bristen på automation av simulering som är inriktade på byggnadsenergiprestanda (BEP), vilket krävs för att hantera stora byggnadsbestånd. Det här examensarbetet syftar till att utforska utmaningar med automatisering av BEP-simuleringar och att utveckla en prototyp som ska fungera som en mellanprogramvara mellan UBEM och BEP-simuleringsmotorer, med fokus på IDA ICE(som är en simuleringsprogramvara). Resultatet av examensarbetet är icepy, som är ett verktyg för att automatisera BEP-simuleringar i IDA-ICE. Icepy använder IDA ICE API och Lispskript för att tillhandahålla interaktion mellan UBEM-processen och IDA ICE för att generera en initial simuleringsmodell (IDM), utför själva simuleringen och slutligen hanterar resultatet på ett automatiserat sätt. Genom att icepy implementeras som ett Pythonpaket kan den modifiera flera IDM:er och även exportera simuleringsresultat med några få kodrader. Området Minneberg i Stockholm har använts i en fallstudie för att validera och testa verktyget. Automatiseringsfunktionerna i icepy har möjliggjort känslighetsanalyser för olika byggnadsdesignparametrar, exempelvis studerades påverkan av olika värden på förhållandet mellan fönster och väggar genom användning av tre olika algoritmer för fönsterdistributioner. Det utvecklade verktyget har begränsningar i funktionalitet framförallt på grund av att enbart byggnadens ytterskal studerades i byggnadsenergisimuleringarna. Verktyget har dock visat sig vara ett effektivt tillvägagångssätt för att automatisera simuleringsprocesser, vilket visar på en god potential att också vidareutveckla dessa verktyg.