• Energy Research

Urban Building Energy Modeling aims at assessing the building energy performance at city scale with as little computational effort as possible. Thus, different methods have been developed in the last years to reduce the required calculation time by simplifying the modeling approach, selecting only representative buildings, or minimizing the building description. Starting from the latter ones, this work proposes a novel algorithm capable of abstracting a randomly shaped building into a representative shoebox. The presented shoebox generation algorithm is based on a preliminary sensitivity screening analysis on a set of reference parallelepiped-shaped thermal zones. This allowed the identification of the most significant geometry indicators influencing the building’s performance. Based on this, more complex geometries have been simplified to the shoebox with the same indicators and the accuracy of the algorithm has been evaluated comparing the simulated performance of simplified and original buildings. The approach includes the definition of equivalent shading surfaces, to account for self-shading elements in the original building geometry. The algorithm has shown good accuracy not only on the hourly thermal loads, but also the zones’ hourly temperature profiles, reducing to one third the energy simulation time with respect to the detailed building model. Although not as fast as other urban modelling approaches in the literature, it can retain accurate results at a finer time scale, i.e., on hourly basis, which is necessary in applications such as district heating and energy networks.

An increased use of renewable energy and of energy efficiency measures in buildings is needed to face the urgency of climate change. Buildings are in fact among the highest worldwide consumers of primary energy, mostly of fossil fuel origin, while still making insufficient use of in-situ renewable energy sources. To find a solution to this situation, many municipalities have promoted the use of solar cadastres mapping the solar energy potential of the existing building stock. However, their implementation has limits from different points of view including assessment accuracy, representation methods, and decision-support. To overcome these limits, this thesis proposes a planning-support system based on the photovoltaic (PV) potential of buildings. The goal is to provide decision-makers and stakeholders with a robust method to assess the potential of photovoltaic electricity generation of existing buildings under uncertain environmental conditions. The developed methodology is based on an urban-scale modeling workflow that includes the simulation of the photovoltaic electricity production and a simplified estimation of the building energy retrofit potential. Existing state-of-the-art models for solar radiation, building energy and PV performance are coupled in the workflow, which relies on a vector 3D city model featuring an accurate representation of buildings, terrain, and vegetation. The proposed modeling workflow also includes an innovative approach for simulating the arrangement of PV modules on the building envelope, which influences both the energy yield and the acceptability of the system. The modeling workflow is in turn integrated into a planning-support system that provides a robust assessment of the photovoltaic potential through risk-averse scenarios. We consider here two crucial yet underestimated uncertainty factors: weather and vegetation. The results are aggregated at different scales and, for each scale, the spatial locations are ranked through pairwise comparisons according to relevant energy indicators. The results are finally displayed in a 3D-mapping tool featuring false-color overlays at the considered aggregation scales to address different objectives and inform decision-makers. We conducted sensitivity analyses towards different input data resolutions and modeling scenarios so as to achieve a good trade-off between accuracy and computational cost and define confidence intervals for the calculated values. The simulated PV yield was also compared against measured data from an existing PV installation. The proposed modeling workflow and planning-support system were tested in an urban district within the city of Neuchâtel (Switzerland). The analysis highlighted areas with the highest potential and provided a priority list of interventions. It also showed the impact of vegetation on absolute results and especially on the ranking of the spatial locations evaluated by their energy potential.

Social houses built after the Second World War to accommodate workers and low-income families represent one of the major energy consumers and greenhouse gas emitters in the residential sector. Plans for their renovation are underway in all European countries, and the process is more complicated for Italian cities due to the lack of space and the large number of historical buildings. This study addresses this challenge by proposing a methodology to renovate a low-income district in the city of Venice using CityBES to model and evaluate energy conservation measures. CityBES is a web-based tool that allows users to employ urban building energy modeling for large-scale energy and retrofit analyses of building stocks. In the case study conducted for Venice's Santa Marta district, due to the particular context, four common energy conservation measures covering both the building envelope and heat generation boilers have been applied. The evaluation of energy-saving performances at the district level showed that the four measures together achieved 67% energy savings, an abatement in energy cost equal to 67%, and annual carbon dioxide emissions reduction of 1.1 MtCO2. The case study demonstrates a method and workflow replicable for energy retrofit analysis of building stocks in other historical districts.

Urban building energy modeling (UBEM) is attracting increasing attention in the energy modeling filed. Unlike modeling a single building using detailed building systems information, UBEM generally uses limited high-level building stock data to infer default assumptions about building characteristics and operations. This practice inherently brings uncertainty to UBEM. This study introduced a novel method of automatic and rapid calibration of UBEM based on the annual electricity and natural gas energy use data by learning the correlations between crucial model input parameters and the building energy use from the reference building models. A case study was presented to calibrate 72 large office buildings built before 1978 in San Francisco. Seventeen model parameters were selected and Monte Carlo sampling was used to create 1000 samples that reasonably represent the parameter space. Then 1000 simulations were performed for the reference building model to create an energy performance database. The results showed that by learning from the energy performance database, it took less than four simulation runs on average to calibrate a building model. After the calibration, the distributions of each parameter were obtained to replace their single predefined default values. For example, the default lighting power density of 21.39 W/m2 was calibrated to be 7.50 W/m2 on average. The case study successfully demonstrated the effectiveness of the novel calibration method for UBEM in the mild climate. The method will be further tested in future for other climate zones and other building types.

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.

Buildings represent 40% of the European Union’s final energy consumption and are largely of resi- dential use. From 2006 to 2016, existing European housing stocks have been analysed at national level to make the energy refurbishment processes transparent and effective. However, at the meta- scale of regions, cities or neighbourhoods, case-by-case analysis using Building Energy Models (BEM) becomes an unfeasible decision-support tool. To try to overcome this limitation, the nascent field of Urban Building Energy Modelling (UBEM) is making substantial progress in the assessment of build- ing energy performance at urban scale. Still, most of the UBEM projects rely upon archetypes – i.e. virtual or sample buildings illustrative of the most frequent characteristics of a particular category, and the definition and description of such archetypes may compromise their reliability. This paper presents an alternative UBEM approach, especially designed for the homogeneous historic districts of cities where a significant proportion of the buildings are under preservation rules. These rules can restrict the scope of the measures to improve their energy efficiency or limit the possibility of implementing renewable energy systems. We introduce a new parameter (HAD) to classify blocks according to their heritage asset density. HAD is then mapped onto the study-area and the sample block is selected as representative of the most frequent HAD category. Using the historic ensemble of Seville as case-study, this paper shows results in energy consumption on a district scale and proposes a set of solutions to improve the energy efficiency of the buildings while respecting the heritage preservation rules. To sup- port consistent policy decisions, validation of these results has been carried out, by in-situ monitoring of a representative number of dwellings.

Abstract Urban building energy modeling (UBEM) seeks to evaluate strategies to optimize building energy use at urban scale to support a city's building energy goals. Prototype building models are usually developed to represent typical urban building characteristics of a specific use type, construction year, and climate zone, as detailed characteristics of individual buildings at urban scale are difficult to obtain. This study investigated the Italian building stock, developing 46 building prototypes, based on construction year, for residential and office buildings. The study included 16 single-family buildings, 16 multi-family buildings, and 14 office buildings. Building envelope properties and heating, ventilation, and air conditioning system characteristics were defined according to existing building energy codes and standards for climatic zone E, which covers about half the Italian municipalities. Novel contributions of this study include (1) detailed specifications of prototype building energy models for Italian residential and office buildings that can be adopted by UBEM tools, and (2) a dataset in GeoJSON format of Italian urban buildings compiled from diverse data sources and national standards. The developed prototype building specifications, the building dataset, and the workflow can be applied to create other building prototypes and to support Italian national building energy efficiency and environmental goals.