• 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.

According to the U.S. Energy Information Administration (U.S. EIA) (2018a), in 2017 the energy delivered to the residential and commercial building sector represented 27% of the total delivered energy in the United States. In the case of greenhouse emissions (GHG), the building sector represented around 40% emissions in the country (U.S. EIA, 2017). Anthropogenic GHG emissions are considered the main cause of climate change. One of the most notable consequences of climate change is the temperature rise. For the Austin area is expected the temperature rise between 2.6°C to 4.5°C by 2100 in comparison to the average temperature observed between 1990 and 2010 (Hayhoe, 2014). Also, building design and construction in the United States has been regulated by different codes and standards. In the case of building energy performance, there exist both mandatory codes and voluntary green building certifications to increase building energy performance. Using Urban Building Energy Modeling tools (UBEM), in this case, the urban modeling interface (UMI), this thesis analyzes the building energy performance of different mandatory design codes and voluntary green building certifications under three different climate change scenarios. UBEM tools are capable to perform an urban scale energy simulation. Mueller neighborhood located in Austin, Texas was the location selected for the modeling and simulation process for this thesis. The three different emission scenarios projected by the Intergovernmental Panel on Climate Change were used for this thesis, are A2, A1B, and B1. On the other hand, building templates analyzed are the International Code Council mandatory codes used in Austin, the Leadership in Energy and Environmental Design (LEED) voluntary certification and the Austin Energy Green Building (AEGB) voluntary certification. Results from the simulation process show that it is mostly inevitable to avoid the effects of climate change in the energy performance of the building. However, buildings designed under the different green building certification requirements presented the most resistance against the increase of temperature. This methodology helps to identify the impact of climate change in buildings and can be used as feedback for policy making, climate change mitigation, and energy strategic analysis ; Sustainable Design

The large-scale and comprehensive artificial dataset introduced in this research reflects the energy demands of two neighbourhoods and with some reasonable limitations mimics monitoring campaigns otherwise collected on-site from buildings in use. The monitoring campaigns are created using white-box simulation models for single-family houses representing typical neighbourhoods in Flanders. The datasets are generated using Dymola and the IDEAS package embedded in TEASER. Each house varies in geometry, size, envelope properties, occupancy schedules, and installed gas heating systems. In this research, two datasets are created, one reflecting the properties of a low-performing building stock dating before the introduction of the EPBD (2006), and the other reflecting properties of a well-performing stock built after 2006. The envelope properties for older houses are allocated using EPC data grouped in four construction periods, while for newly built houses the properties are based on EPB reports, both were collected in Flanders. The datasets include heavy-weight houses in a detached, semi-detached, or terraced typology. Furthermore, the houses are simulated as one or two-zone buildings, depending on the number of floors which range from one to three floors. In the simulations, a natural infiltration model is implemented as well as a stochastic occupant behaviour model mimicking gains from occupants and appliances. Due to the complexity of the large-scale simulation, the heating system is post-processed in a data-driven approach and the heat source for both datasets are gas-fired heating systems. In total six system configurations are considered including condensing and non-condensing boilers with three types of domestic hot water (DHW) sub-systems (no integrated DHW, direct and with a storage tank). For all configurations, a variable production efficiency is considered dependent on the load ratio. The urban-scale simulation is carried out at a 10-minute frequency for the weather data assuming the location of Heverlee (Belgium) in the year 2016.The original purpose of this dataset was the development of statistical tools for the assessment of the heat loss coefficient of the building fabric. However, the generated artificial datasets provide a large spectre of usually difficult-to-measure inputs suitable to assess the importance of different components in the overall energy balance. Even though the original work looked into individual building behaviour, the datasets can be also used from an urban perspective for energy planning purposes.

This dataset contains the present-day, global, survey-based, and spatially explicit air-conditioning adoption rate dataset developed in Li et al. (2024), “Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) through Explicit Representation of Urban Air-conditioning Adoption”, published in Journal of Advances in Modeling Earth Systems. It also contains the simulation results analyzed in the article. Details about this dataset (data sources, data collection and processing methods, simulation setup, etc.) are described in the article. The air-conditioning adoption rate dataset is publicly available in tabular, vector, and gridded formats. It is compatible with CESM, and can also be leveraged in other climate and energy modeling applications and socioeconomic or integrated assessment analyses. This dataset may be useful for multiple scientific communities regarding urban climate and energy, impacts, vulnerability, risks, and adaptation applications. For more detailed description, please refer to the README file (global_AC_adoption_rate_README.txt) included in the dataset.

This work proposes the use of a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban university campus in order to assess how district operation strategies can be leveraged to support the transition to flexible work arrangements. The results show that when users are given the flexibility to pursue more comfortable workspaces, they are still comfortable only 58% of the time. BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation ISBN:979-8-4007-0230-3

Urban Building Energy Modeling (UBEM) is a method of simulating the energy usage of a grouping of buildings, at the scale of a neighborhood or city, rather than the typical simulation of a single building. This can be a powerful tool to reduce current energy usage, through testing retrofit scenarios on the existing building stock, and to guide future planning efforts. This switch in simulation scales is crucial to move towards more sustainable and resilient cities. This thesis addresses data availability issues to inform UBEM studies, in all urban contexts, by establishing a list of readily available data sources as well as a multi-step, theoretical framework that can be used to gather the data required to run an accurate UBEM that considers the surrounding socioeconomic factors. This framework is demonstrated through a case study in the Grove Park neighborhood of Atlanta, Georgia. 110 single-family households were modeled. The results of the study analyze current energy use patterns, compare neighborhood-specific archetype definitions to default residential archetype templates, and investigate the neighborhood’s performance under future weather scenarios. The study shows that within a single neighborhood the energy use intensity (EUI) can vary by up to 92 kWh/m2 based on building envelope condition and occupancy patterns. Default archetype inputs can dramatically underestimate or overestimate the energy use of households in a low resource community. Investigating energy performance under both current and future weather scenarios allows for energy efficiency strategies that are beneficial to the neighborhood now while increasing future resiliency. ; M.S.

Urban building energy models (UBEM) have the potential to become integral planning tools for district energy systems due to the dynamic, interactive and complex nature of temporal building energy demand patterns. Although the demand patterns are related to the occupancy profiles of buildings supplied by district energy systems, occupant behavior in current UBEM approaches does not usually consider diversity in occupancy profiles among buildings of the same use-type. In this work, a novel method to create context-specific, data-driven commercial building occupancy profiles was used to generate diverse and non-diverse urban building occupant presence models (UBOP). Diverse UBOP randomly assigned occupancy profiles to buildings. Non-diverse UBOP assigned the data-driven mean or median profile to all buildings. ASHRAE standard profiles and occupant densities served as a baseline for comparison. The impact of diverse vs. non-diverse UBOP was assessed by comparing UBEM simulations for district energy efficiency benchmarking, renewable energy integration potential, and district energy system design, using a case study in Singapore. The results demonstrate that, because of the relationship between occupant presence and building systems operation, occupancy profiles are highly sensitive parameters for district energy demand predictions. For the case study, the energy demand estimation is significantly influenced by the shape of occupancy profiles. In particular, the choice of UBOP influences the cooling demand to the degree that district cooling system design decisions might be impacted. Therefore, it is advisable to use diverse UBOP and to run probabilistic UBEM simulations for district energy system design. Applied Energy, 277 ISSN:0306-2619 ISSN:1872-9118

The nUBEM model offers a powerful AI-driven framework for evaluating the energy performance and greenhouse gas emissions of residential buildings on a national scale. By enabling urban and nationwide insights, it supports comprehensive analysis of building characteristics and energy performance across the residential building stock. This model is useful for the design of targeted energy efficiency policies and assessing their effectiveness in reducing greenhouse gas emissions. The code in this repository is part of the paper 'Predicting Energy and Emissions in Residential Building stocks: National UBEM with Energy Performance Certificates and Artificial Intelligence', published in Applied Sciences in 2025 and written by Carlos Beltrán-Velamazán, Marta Monzón-Chavarrías and Belinda López-Mesa from the Built4Life Lab, University of Zaragoza - I3A (Spain).

Residential facilities and buildings are the largest consumers of energy in the United States. More than 76% of electricity and more than 40% of all energy consumption comes from providing a comfortable and healthy environment in buildings. Prioritizing building energy technology goals for performance and cost can significantly reduce energy use within the next ten years despite a substantial forecasted population increase. Retrofitting existing buildings and improving future construction has the potential to realize cost savings and environmental benefits over time. A key component to realizing these benefits is regional building energy modeling. One of the challenges to widespread building energy modeling is that required data is often dispersed, unintegrated and inaccessible, if it exists at all. This can make energy planning highly expensive and time-intensive for many state and local governments. This project aims to serve as an initial exploration into building energy modeling and urban energy modeling. Specifically, an analysis of a building is conducted and compared in terms of energy consumption to the same building as part of an urban building energy model. The urban building energy model is based on a simple building archetype structure. The process of developing the two models is compared qualitatively. Finally, recommendations for future research in building energy modeling, urban building energy modeling, and building archetype construction are provided.

District-scale building energy models can be a powerful tool for the integration of renewable energy sources and efficiency measures in urban areas. One key limitation of these models, however, has been their rather simplified treatment of building occupants. Since it is their activities which create the needs for energy in an area, an improved analysis of the effects of occupants on demand at the district scale is needed. This paper presents a novel population-based approach (PopAp) inspired by agent-based transportation models, in which a population of occupants was defined based on class and employee registers and each was given an individual daily schedule. This approach was then used to assess the effect of occupant presence modeling on district-scale energy demand simulations by comparing the data-centric PopAp method to standard-based deterministic and stochastic approaches. The maximum number of occupants in the area was found to be 33% higher for the deterministic model compared to the data-centric PopAp results, a deviation that was especially pronounced in education buildings. The results for space heating, space cooling and electricity demand for lighting and appliances show that while the mean deviation between models on a yearly basis is within 10% for all demands, on an hourly scale the deviation for space cooling and electricity exceeded 15%. Given the importance of the hourly scale for peak demand prediction for technology sizing, more detailed occupant modeling approaches should be considered when planning energy systems. Building and Environment, 181 ISSN:0360-1323 ISSN:0360-1323