Urban building energy modeling (UBEM) is a term used in the literature to refer to different types of simulations that are not necessarily related to the buildings in the “urban” area. Urban-scale, large-scale, and district-scale building energy modeling have been used in the literature interchangeably, but they all refer to the modeling and simulation of a group of buildings to study their energy use and behavior. The large-scale building energy modeling (herein UBEM) has been trending for the past decade (Figure 1) due to its effectiveness in providing a significant amount of data on a large group of buildings that could be used by different stakeholders in the energy sector such as energy policymakers, energy companies, building portfolio managers, and researchers. Making the modeling and simulation process more efficient in speed, flexibility, cost, and accuracy is critical, and this requires a thorough understanding of the current state of and future trends in UBEM tools and techniques.

UBEM models are developed for different purposes, such as identifying the buildings with high energy use intensity (EUI), regions with high energy consumption, microclimate impact on building energy use, and studying the energy saving of different energy conservation measures (ECMs) on a large scale. Finding the most effective ECMs is one of the essential end goals in energy modeling projects; however, not all the UBEM approaches are suitable for this purpose. This highlights the importance of such systematic literature reviews.

In that context, it is necessary to understand the state-of-the-art physics-based UBEM methods and tools, especially those suitable for assessing different ECMs. Therefore, the objectives of this papentryr are defined as follows: (1) to perform a reproducible systematic review of the literature on research papers on physics-based UBEM case studies; (2) to extract, categorize, and analyze the tools, files schemas, data sources, building systems, and environmental data in these case studies, and (3) to find and categorize the existing challenges in physics-based UBEM and propose future research directions. The papers reviewed in this study either use a physic-based simulation engine or adopt similar types of inputs required for physics-based simulation that are or could be applied in ECM analysis.

2. UBEMLiterature Review

Physics-based UBEM could include multiple aspects and steps proposed by researchers such as data preprocessing (e.g., geometric data, non-geometric data, weather data, and energy use), model generation, simulation, calibration, and application (e.g., urban planning, stock-level carbon reduction, building-level recommendations, and building-to-grid integrations) ^[1][5]. Figure 23 proposes a five-step data extraction/presentation approach for UBEM studies. This data and metadata adoption structure help to take a systematic and standard practice to provide and collect data in UBEM projects. The scale of the UBEM study could determine the necessary tools and scope of the project. Oraiopoulos and Howard (2022) ^[2][6] performed a systematic review and adopted a statistical approach in UBEM to use micro, meso, and macro scales to categorize the UBEM projects based on the number of buildings (Figure 34). IThe literat is shownure review shows that most physics-based UBEM case studies cover under 100,000 buildings (Figure 45). More specifically, 46 out of 61 eligible studies model less than 10,000 buildings. This includes 86% of the eligible case studies reviewed in this papentryr. About 50% of the case studies model less than 1000 buildings (i.e., Microscale), and only about 3% of case studies model more than 500,000 buildings. The methods and tools used in the reviewed papers could be scaled up to model and simulate a large number of buildings, but lack of data could hinder that. Section 4 reviews such challenges in physics-based UBEM. Researchers study the modeling and simulation techniques in UBEM, and their advantages and disadvantages are compared ^[3][4][7,8]. Boghetti et al. (2020) ^[3][7] compared two UBEM development approaches (i.e., physics-based vs. data-driven) and noted that data-driven models rely on many data points and physics-based methods need more extended simulation and preparation time. Because ECM evaluation is one of the areas of study in this papentryr, the selected papers are focused on physics-based modeling. Studies on bottom-up physics-based urban building energy models might simulate multiple buildings independently, focus on microclimate effects (e.g., urban heat island (UHI)), or combine these two and make the necessary adjustments in UBEM to consider the microclimate effects (Figure 56). These effects could include the shading from surrounding objects, increased temperature of the urban area due to the UHI effect, the long-wave radiation from other buildings, or the Heating, Ventilation, and Air Conditioning (HAVC) system’s heat release that could affect the urban climate and other buildings ^[5][9] increasing the outdoor air temperature by 2.8 °C in commercial neighborhoods. Luo et al. (2019) ^[6][10] showed that the thermal interaction between buildings in UBEM for a dense urban area with high-rise buildings based on the long-wave radiations could affect the heating and cooling loads by up to about 3.6%. Therefore, the microclimate could directly impact individual buildings’ energy performance, especially in dense areas. However, this is not necessarily considered in all the UBEM studies; hence, the “individual building simulation” category needs to be identified and studied separately (Figure 56). The interface between these two approaches includes UBEM studies where at least one component of microclimates such as the UHI effect or long-wave radiation between buildings is considered in the physics-based simulation. This could be done by tuning the weather file using an urban weather generator (UWG) ^[7][11] or using external and complementary tools to include additional interactions between buildings ^[8][12]. The majority of papers selected for full-text review in this papentryr could be categorized under the ‘group of individual building simulation’. Review papers study different aspects of physics-based UBEM. Table 1 shows the selected review papers, which could identify the main UBEM field of research. The identified areas include general review papers on tools, methods, and challenges in UBEM, occupant-centric studies, UBEM tools, data acquisition methods, classification approaches, energy-saving potentials, and the accuracy of UBEM. Other than the first category, there are limited review studies on different aspects of UBEM, especially on critical areas such as archetype development, data sources, acquisition techniques, calibration, and energy conservation evaluation. Figure 67 shows the occurrence frequency of keywords and their link strength in UBEM research. Specific dependent keywords such as ‘buildings,’ ‘building energy modeling,’ and ‘urban building energy modeling’ have the highest use frequency and link strength. The It is shownreview of the literature shows that (1) energy utilization, (2) energy efficiency, (3) urban planning, (4) energy management, and (5) urban planning are among the top independent keywords in UBEM research. It could be observed that critical aspects of UBEM such as ‘calibration’ are not studied as often as other aspects. Figure 78 shows certain research areas of UBEM such as climate change, greenhouse gas emissions, offices, housing, and retrofitting are among the topics studied more recently (i.e., after 2020).