Urban Building Energy Modeling Research Articles

Exploring the impacts of heterogeneity and stochasticity in air-conditioning behavior on urban building energy models

Heterogeneity and stochasticity, two main aspects of uncertainty in occupant behavior (OB), at the urban scale are not featured in most current urban building energy modeling (UBEM) platforms, and their respective impacts on urban-scale building energy consumption remain unclear. We aimed to introduce OB uncertainty into the UBEM workflow and assess the differences in the impacts of heterogeneity and stochasticity on cooling demand. Different OB models were integrated into the cooling demand simulation of residential building stocks, considering the heterogeneity and stochasticity in both occupancy and energy-use behavior. The impacts of heterogeneity and stochasticity on urban-scale cooling demand and its applications for different purposes in UBEM are discussed. We found that stochasticity in occupancy decreased the peak cooling demand by 54%, whereas the uncertainty in air-conditioning (AC) behavior had little effect. Heterogeneity is the main reason for the diversity in cooling demand, whereas stochasticity better reflects the dynamics of cooling demand., which Occupancy and AC behavior models with higher fidelity are required to obtain results with higher spatial or temporal resolution should be selected according to the UBEM applications. The proposed approach will contribute to the development of appropriate urban-scale occupant behavior models and bridge urban mobility with occupant-centric UBEMs.

Ranking building design and operation parameters for residential heating demand forecasting with machine learning

The European Union's Energy Performance in Buildings Directive has made significant strides in enhancing building energy efficiency since its inception in 2002. However, approximately 75% of EU buildings still fall short of energy-efficient standards. Furthermore, there is a growing momentum to extend the concept of nearly zero-energy buildings to entire districts, thereby fostering Net-Zero Energy Districts. This underscores the necessity for large-scale urban building energy modelling to identify and improve underperforming buildings and energy transition planning. Given the increasing interest in black box models for building energy performance, this study aims to identify common input variables in buildings energy demand literature, analyse their influence, and develop a heating demand prediction model using different algorithms: Random Forest, XGBoost, and Extra Trees. Four large datasets generated from white-box simulation in three Spanish cities were used for training and testing the models. Four features consistently stand out as the most important for prediction: shape factor, infiltration rate, south equivalent surface, and internal gains, regardless of the training algorithm or the climatic zone. The multi-location XGBoost algorithm with an optimizer emerged as the best-performing model, with an average Mean Absolute Percentage Error value hovering around 40%. Analysis employing SHapley Additive exPlanation (SHAP) values showcases the model's ability to identify factors that drive higher heating energy demand, alongside its strong predictive performance. This suggests potential integration of the model into programmes to identify key variables to be addressed during building renovation. Additionally, results show the potential of XGBoost-based software's potential for identifying building renovation targets.

Predicting building energy consumption in urban neighborhoods using machine learning algorithms

Assessing building energy consumption in urban neighborhoods at the early stages of urban planning assists decision-makers in developing detailed urban renewal plans and sustainable development strategies. At the city-level, the use of physical simulation-based urban building energy modeling (UBEM) is too costly, and data-driven approaches often are hampered by a lack of available building energy monitoring data. This paper combines a simulation-based approach with a data-driven approach, using UBEM to provide a dataset for machine learning and deploying the trained model for large-scale urban building energy consumption prediction. Firstly, we collected 18,789 neighborhoods containing 248,938 buildings in the Shanghai central area, of which 2,702 neighborhoods were used for UBEM. Simultaneously, building functions were defined by POI data and land use data. We used 14 impact factors related to land use and building morphology to define each neighborhood. Next, we compared the performance of six ensemble learning methods modeling impact factors with building energy consumption and used SHAP to explain the best model; we also filtered out the features that contributed the most to the model output to reduce the model complexity. Finally, the balanced regressor that had the best prediction accuracy with the minimum number of features was used to predict the remaining urban neighborhoods in the Shanghai central area. The results show that XGBoost achieves the best performance. The balanced regressor, constructed with the 9 most contributing features, predicted the building rooftop photovoltaics potential, total load, cooling load, and heating load with test set accuracies of 0.956, 0.674, 0.608, and 0.762, respectively. Our method offers an 85.5%-time advantage over traditional methods, with only a maximum of 22.75% of error.

Assessing the impacts of urban morphological factors on urban building energy modeling based on spatial proximity analysis and explainable machine learning

The investigation of the relationship between urban morphology and building energy consumption on a broad scale has garnered significant scholarly interest. Particularly in the early phases of urban building design, the optimization of urban morphological factors (UMFs) has demonstrated its efficacy and cost-effectiveness in enhancing the energy efficiency of urban buildings. This paper presents a framework for exploring the relationship between urban morphology and energy consumption in urban buildings. The framework encompasses defining and quantifying UMFs using a spatial proximity analysis approach, constructing an urban building energy model, and employing explainable artificial intelligence (AI) methods to analyze the impact of each factor on energy consumption. The findings identify the potential impact zones surrounding target buildings and identify 26 UMFs related to urban buildings and the road network. The study reveals high-impact UMFs significantly influencing energy consumption and provides corresponding recommendations for urban building planning. Moreover, the impact of these factors on energy consumption is similar across different building types, although there are variations in their contributions. The research contributes to identifying influential UMFs and provides practical implications for early urban building planning. The proposed methodology can be generalized to other cities, enabling broader applications of the framework.

Integrated energy demand-supply modeling for low-carbon neighborhood planning

As the building stock is projected to double before the end of the half-century and the power grid is transitions to low-carbon resources, planning new construction hand in hand with the grid and its capacity is essential. This paper presents a method that combines urban building energy modeling and local planning of renewable energy sources (RES) using an optimization framework. The objective of this model is to minimize the investment and operational cost of meeting the energy needs of a group of buildings. The framework considers two urban-scale RES technologies, photovoltaic (PV) panels and small-scale wind turbines, alongside energy storage system (ESS) units that complement building demand in case of RES unavailability. The urban buildings are modeled abstractly as “shoeboxes” using the Urban Modeling Interface (umi) software. We tested the proposed framework on a real case study in a neighborhood in Chicago, Illinois, USA. The results include estimated building energy consumption, optimal capacity of the installed power supply resources, hourly operations, and corresponding energy costs for 2030. We also imposed different levels of CO2 emissions cuts. The results demonstrate that solar PV has the most prominent role in supplying local renewables to the neighborhood, with wind power making only a small contribution. Moreover, as we imposed different CO2 emissions caps, we found that ESS plays an increasingly important role at lower CO2 emissions levels. We can achieve a significant reduction in CO2 emissions with a limited increase in cost (75% emissions reduction at a 15% increase in overall energy costs). Overall, the results highlight the importance of modeling the interactions between building energy use and electricity system capacity expansion planning.

Urban Building Energy Modeling to Support Climate-Sensitive Planning in the Suburban Areas of Santiago de Chile

Greenhouse gas emissions depend on natural and anthropic phenomena; however, to reduce emissions, we can only intervene in terms of anthropic causes. Human activity is very different in various countries and cities. This is mainly due to differences in the type of urban environment, climatic conditions, socioeconomic context, government stability, and other aspects. Urban building energy modeling (UBEM), with a GIS-based approach, allows the evaluation of all the specific characteristics of buildings, population, and urban context that can describe energy use and its spatial distribution within a city. In this paper, a UBEM is developed using the characteristics and consumption of eight typical buildings (archetypes) in the climate zone of Santiago de Chile. The archetype-based UBEM is then applied to the commune of Renca, a critical suburb of Santiago, with the use of QGIS to analyze the energy demand for space heating and the potential for energy saving after four retrofitting interventions. Knowing the costs of the retrofitting interventions and the energy price, the simple payback time was evaluated with the reduction in GHG emissions. Starting from the actual building stock, the results show that the most effective retrofitting intervention for the commune of Renca is the thermal insulation of walls and roofs; due to the type of dwellings, this particular intervention could be more convenient if associated with the installation of solar technologies. This methodology can be replicated with the data used by urban planners and public administrations available for many Chilean cities and in other countries.

Exploiting geographic open data to improve urban building energy simulations: The Padova city center case study

In recent years, building stock models have been developed by researchers to examine the aggregate performance of stacks of buildings within large areas, thereby giving rise to the concept of urban building energy models (UBEMs). The input data for such models consists of geometric and non-geometric attributes of the buildings, in addition to meteorological information. In this perspective, the acquisition of precise and comprehensive data poses a challenge, as the existing datasets often lack certain parameters or are not in a standardized format. This study aims to address the challenges by proposing a workflow for generating an input data frame tailored for incorporation into UBEMs. The data frame should include all the essential parameters of the buildings, and its constitution should be reflective of the real-world data. Moreover, the proposed workflow should remain consistent with databases available at national or regional levels. Acknowledging this non-uniformity in databases across regions, the methodology proposed in this study strategically considers various alternatives. For this reason, the proposed automatized workflow ensures flexibility and adaptability to changes in data availability. The workflow proposed in this study is a QGIS based geographical calculation method. The method can combine data from various sources into one shapefile that can be used to simulate the energy performance of buildings in urban areas.

UAV-based automated 3D modeling framework using deep learning for building energy modeling

Many approaches to the Urban Building Energy Model (UBEM) have been developed to analyze urban-scale energy demand patterns. The main goal of UBEM is to minimize manual work and improve modeling accuracy when building 3D modeling procedures that deal with vast amounts of data. To do so, it is important to build an automated process and increase the modeling efficiency throughout the process. This study proposes a new framework for automatically generating 3D models for building energy modeling. This framework collects geographic coordinate system (GCS) data by applying algorithms based on a convolutional neural network (CNN), Haversine formula, unmanned aerial vehicle (UAV), and geographic information system (GIS) information. The collected GCS data were used to generate a 3D model using EnergyPlus, resulting in a 3D model capable of a Level of Detail 3. Subsequently, we compared and verified the size and energy performance of the actual building with those of the generated model. The model size errors generated without drawing information are as follows: buildings, 3.69%, windows1 16.75%, and windows2 19.43%. The error range evidenced through the energy performance evaluation indicator showed that the MBE values for the cooling and heating energies were 5.54% and 5.77%, respectively.

Identification of representative building archetypes: A novel approach using multi-parameter cluster analysis applied to the Swedish residential building stock

Building archetype identification is crucial for Urban Building Energy Modeling (UBEM), but is still considered one of the biggest challenges in this field. New methods of data acquisition, along with data mining techniques such as clustering, have recently received attention for the possibility of significantly increasing identification reliability and archetype accuracy. This paper aims to establish a new and simple clustering methodology for developing building archetypes for hybrid UBEM, using open data sets and multiple diverse variables, that is still reliable and possible to validate without the use of metered energy use or real building data. The methodology uses k-means clustering for 10 building parameters simultaneously, including socio-economic parameters obtained using spatial interpolation of statistical values. Building archetypes are successfully developed for the residential building stocks of two case study areas in Sweden. The results also show that the error metric values for multiple iterations diverge after a certain number of clusters, even when using the same clustering methodology on the same data set. This discovered effect, along with the combined use of one well-known and one novel error metric, constitutes a framework well adapted to accurately determining the optimal number of building archetypes.

Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses

Research effort in analyzing the energy consumption of buildings in districts or cities is increasing as new paradigms of distributed energy production and sharing are spreading. In this work, the Urban Building Energy Model of the Quad, an area of the University of California Davis campus, is presented, validated, and analyzed for possible actions to reduce the district cooling energy consumption. Energy conservation measures involve buildings' air handling units and district chilled water generation. To investigate this system, a district cooling networks module has been developed in EUReCA, the Urban Building Energy Modeling tool used for the analyses. The model has been validated with energy demand and temperature data from 2018 to 2020, resulting in a district cooling demand deviation lower than 7% for 2018 and 2019. Normalized Root Mean Square Error is lower than 35% for each building, proving the reliability at the hourly time scale. Systems energy reduction actions like heat recovery units' installation and heuristic control of the air supply cooling temperature are beneficial in reducing the cooling demand, and they allow a more efficient discharging of the chilled water storage, reducing the average electricity price by load shifting during peak demand.

The interactive indoor-outdoor building energy modeling for enhancing the predictions of urban microclimates and building energy demands

There is a lack of an urban building energy modeling framework that considers the influence of surrounding buildings and local urban climate on building thermal performance. This can lead to inaccurate results since the thermal performance of individual buildings is heavily influenced by their surrounding built and climatic environment. This study establishes an interactive indoor-outdoor building energy modeling method to enhance the predictions of urban microclimates and building energy demands by coupling an urban physics model with a physics-based building energy model. Validation of the interactive coupling scheme uses field measurement datasets. Parametric simulation and analysis are conducted to understand the influence of the roof-to-canyon width ratio, canyon orientation, and ground vegetation fraction on canyon temperature, building energy consumption, and energy demand. Furthermore, the impacts of building energy model complexity (e.g., detailed vs. simplified building models) and coupling approaches on canyon temperature and building energy profiles are demonstrated using two case study buildings. In comparison with the one-way coupling approach, cooling energy consumption predicted with the dynamic two-way coupling approach varies by 3.5 % and 0.5 % for the detailed medium office building model and high-rise building model, respectively, and peak cooling demand varies by 8.4 % and 7.0 % for the detailed medium office building model and high-rise building model, respectively. This study also suggests that adopting a complex two-way coupling approach with environmental data exchange at various elevations is necessary for modeling tall buildings at the urban scale.

Use of publicly available data to create a dataset for data-driven urban commercial building energy intensity classification: Model accuracy, interpretation, and implications of an open data framework in Hong Kong

Previous studies relied on structured databases containing energy use intensity (EUI) and building features to develop data-driven urban building energy models (D-UBEMs). However, such databases are not available in most cities. In Hong Kong, energy audit is required for commercial buildings, but only EUI is publicly released without any building information. We assessed the adequacy of input features collected and integrated from public sources to develop a precise D-UBEM. We separated Hong Kong commercial building samples into “below-median” and “above-median” EUI categories based on the bimodal distribution of log10EUI and developed a data-driven binary classifier to predict the category. Using publicly available input features, the average classification accuracy is 78.73%. We used the D-UBEM to reveal the association of “above-median” EUI category with input features, and spatially visualize floor areas of Hong Kong commercial buildings belonging to the “above-median” EUI category. In the discussion, we examined the causes of misclassified buildings and highlighted the limitations of our database from public sources in developing accurate and useful D-UBEMs. We suggested an open-data policy framework for releasing building information alongside EUI data and establishing a central database that contains more precise values for building geometries, usage, and thermal performance in Hong Kong.

Smart meter-based archetypes for socioeconomically sensitive urban building energy modeling

Urban building energy modeling (UBEM) and its associated tools have proliferated in recent years, leading to various model development, simulation, calibration approaches, and use cases. UBEM is becoming a valid policy support tool to guide planning and intervention efforts at the neighborhood and city scale. However, current UBEM workflows focus primarily on the physical properties of buildings and geospatial geometry data without consideration for socioeconomic factors or demographic characteristics of the modeled/studied areas. This limitation impedes UBEM's effectiveness as a policy support tool. This paper presents a novel method – using a combination of supervised and unsupervised learning techniques on smart meter and census data – to develop hourly usage schedules for different socioeconomic personas. The schedules can be used to refine building archetypes for UBEMs. The method is piloted in two cities with similar building stocks but different socioeconomic compositions. It was found that socioeconomic indicators are important in classifying building archetypes. Considering these indicators changes the predicted city-wide energy use for residential buildings by up to 10%. The method presented is scalable and applicable to cities and municipalities worldwide (large or small) and elucidates the importance of accounting for demographic and socioeconomic indicators to reflect lived realities accurately.

Building archetype characterization for mass-housing energy efficiency through a UBEM approach

Building archetypes characterization is one of the main sources of inaccuracy in Urban Building Energy Modelling (UBEM). This study aims to implement an effective approach to developing a building archetype for estimating energy efficiency in social housing districts. The goal is to support researchers and decision-makers analyzing strategies for similar buildings, evaluate energy use in buildings, and design different retrofit scenarios. UBEM is a practical approach in large-scale building energy modeling used by researchers and stakeholders to analyze and develop different design and retrofit scenarios for buildings with similar construction features and occupancy. The archetype approach requires a subset of buildings representing a cluster with similar properties (e.g., building type, construction features, occupancy, and age), which is used to extrapolate the total energy consumption at the urban scale. The research focused on clusters of four in-line multi-story building types in Rome to conceptualize the study, characterized by reinforced concrete structures. The most diffused systems are the traditional building systems using a gas boiler and radiators for domestic hot water and heating. The model was implemented following a UBEM approach, relying on Urban Modeling Interface (UMI), a tool allowing the creation of building templates to evaluate their energy use at neighbourhood and city-scale. The overall results obtained in this study are described to characterize the archetype for the mass-housing building that researchers and administrations can use to evaluate different strategies for buildings with similar characteristics.

The more the better? Archetype segmentation in urban building energy modelling

Urban building energy modelling is gaining traction as a planning tool to support widespread decarbonization of the built environment. Building-scale models allow for the evaluation of specific emission reduction policies at an urban scale. Given the limited availability of building-by-building data on construction standard and program, aggregating building information through archetypes is key, but a poorly understood step in the urban energy modelling process. In this study, different levels of archetype segmentation are explored for the city of Oshkosh, WI (∼13,000 buildings). A comparison of actual, city-level energy with UBEM simulations suggests higher levels of archetype segmentation do not necessarily lead to higher accuracy, leading to models that are both accurate and nimble enough to explore a variety of upgrade scenarios. Informing archetypal segmentation with policy-informed metrics is beneficial, but pursuing increased detail could dangerously reduce accuracy without ground-truth data.

Evaluating the capabilities of a simplification algorithm for Urban Building Energy Modeling in performing building-level Multi-Objective Optimizations at district scale

Since Urban Building Energy Modeling and Multi-Objective Optimization require large computational resources, both could benefit of simplification techniques able also to make them more affordable to professionals. In this work, the capabilities of a simplification algorithm for urban scale application are assessed in the context of the optimization of energy efficiency measures for buildings. A group of buildings from different districts were selected along with a set of energy efficiency measures and four objectives, such as heating and cooling needs, thermal comfort, and costs. The performance of the simplification was assessed by using precision, recall and F1-score as metrics to check whether the simplified models could obtain the same optimal solutions as the detailed ones. Overall, the simplification produced adequate results with an F1-score greater than 0.8 for 85 % of the cases considered in all climates, allowing the simulation time to be reduced up to 15.7 times.

Urban Building Energy Modeling with Parameterized Geometry and Detailed Thermal Zones for Complex Building Types

Urban building energy modeling (UBEM) has attracted wide attention to the requirement for global carbon emission reduction. This paper presents a UBEM tool, AutoBPS-Param, to generate building energy models (BEMs) with parameterized geometry and detailed thermal zones, especially for complex building types, considering the shading effect from surrounding buildings simultaneously. Three building number scales and four scenarios were analyzed in the hotel-related buildings in Changsha, China. For the prototype modeling of Scenario 1, eighteen prototype building energy models for six building types in three vintages were created, and their simulation results were aggregated based on their representative floor areas. For AutoBPS-Param of Scenario 4, the method created one EnergyPlus (Version: 9.3.0) model for each building. The geometry of the prototype model was scaled and modified based on the target building’s length, width, and number of stories. The surrounding buildings were also added to the AutoBPS-Param simulation to better capture the urban dynamic impact. The results showed that the annual electricity and natural gas energy use intensity (EUI) of the pre-2005 HotelOffice prototype model was 172.25 and 140.45 kWh/m2. In contrast, with the AutoBPS-Param method, the annual electricity EUIs of 71 HotelOffice buildings constructed before 2005 ranged from 159.51 to 213.58 kWh/m2 with an average of 173.14 kWh/m2, and the annual gas EUIs ranged from 68.02 to 229.12 kWh/m2 with an average of 108.89 kWh/m2. The proposed method can better capture the diversity of urban building energy consumption.

An eight-step simulation-based framework to help cities reach building-related emissions reduction goals

With buildings accounting for 40% of global carbon emissions, cities striving to meet sustainability targets aligned with the Paris Agreement must retrofit their existing building stock within 30 years. Previous studies have shown that urban building energy models (UBEMs) can help cities identify technology pathways—combinations of energy efficiency retrofits and renewable energy deployment strategies—to meet emissions reduction goals. UBEMs are currently limited by cost to only the largest cities but must be expanded to all cities if society is going to meet scientifically-identified emissions reduction goals. This manuscript presents an eight-step framework to scale technology pathways analyses using UBEMs to all communities in a repeatable, affordable manner. The roles and responsibilities of three key personas, the sustainability champion, GIS manager, and an energy modeler, for each step are identified. The eight-step process is tested with a case study of 13 100 buildings in Oshkosh, WI, USA. The case study identified a technically-feasible path to nearly net zero emissions for Oshkosh’s buildings. Constraints in the workforce, supply chain, and retrofit adoption to attain this goal were identified to inform policymakers. The case study suggests that the eight-step process is a blueprint for action in communities around the world.

Study of Urban Thermal Environment and Local Circulations of Guangdong‐Hong Kong‐Macao Greater Bay Area Using WRF and Local Climate Zones

AbstractThe Guangdong‐Hong Kong‐Macao Greater Bay Area (GBA), a cluster of world‐class cities, is undergoing rapid urbanization. However, the heterogeneity of the urban thermal environment resulting from the diversity of urban forms is not yet fully understood. This paper assesses the heterogeneity of the urban heat island (UHI) effect in the GBA using the coupled Weather Research and Forecasting (WRF) model/multi‐layer urban canopy and building energy model (BEP/BEM), with high‐resolution local climate zone (LCZ) map as urban land use/land cover data. The average UHI intensity is found to peak at 1.8 ± 0.4°C in the evening, when the average UHI intensity of LCZ 2 can reach a maximum of 2.4 ± 0.58°C. Properly setting air‐conditioning temperatures can effectively prevent the enhancement of the UHI phenomenon at night by the anthropogenic heat (AH) released from air‐conditioning. The UHI‐induced local circulations and enhanced surface roughness inhibit the penetration of sea breezes inland, and surface wind speed decreases in all LCZs, with a maximum change of more than 0.8 m s−1. However, the increased thermal difference between land and sea leads to enhanced sea breezes offshore, especially in the Pearl River estuary. In addition, a series of sensitivity experiments have been conducted in this paper on initial and boundary conditions, building drag coefficients and urban fractions, which paves the way for further analyzing urban climate in GBA using WRF model and LCZs.

Modelling the impacts of emission changes on O3 sensitivity, atmospheric oxidation capacity, and pollution transport over the Catalonia region

Abstract. Tropospheric ozone (O3) is an important surface pollutant in urban areas, and it has complex formation mechanisms that depend on the atmospheric chemistry and on meteorological factors. The severe reductions observed in anthropogenic emissions during the COVID-19 pandemic can further our understanding of the photochemical mechanisms leading to O3 formation and provide guidance for policies aimed at reducing air pollution. In this study, we use the Weather Research and Forecasting model with Chemistry (WRF-Chem) coupled with the urban canopy building effect parameterization and building energy model (BEP + BEM) to investigate changes in the ozone chemistry over the metropolitan area of Barcelona (AMB) and its atmospheric plume moving northwards, which is responsible for the highest number of hourly O3 exceedances in Spain. The trajectories of the air masses from the AMB to the Pyrenees are studied with the Lagrangian FLEXible PARTicle dispersion model with WRF (FLEXPART-WRF). The aim is to investigate the response of ozone chemistry to reduction in precursor emissions (NOx – nitrogen oxides; VOCs – volatile organic compounds). The results show that, with the reduction in emissions, (1) the ozone chemistry tends to enter the NOx-limited or transition regimes, but highly polluted urban areas are still in the VOC-limited regime; (2) the reduced O3 production is overwhelmed by reduced nitric oxide (NO) titration, resulting in a net increase in the O3 concentration (up to 20 %) in the evening; (3) the increase in the maximum O3 level (up to 6 %) during the highest emission-reduction period could be attributed to an enhancement in the atmospheric oxidants hydroxyl and nitrate radical (OH and NO3) given their strong link with O3 loss or production chemistry; (4) the daily maximum levels of ozone and odd oxygen species (Ox) generally decreased (4 %) in May – a period with intense radiation which favours ozone production – with the reduced atmospheric OH and NO3 oxidants, indicating an improvement in the air quality; and (5) ozone precursor concentration changes in the urban plume of Barcelona contribute significantly to the level of pollution along the 150 km south-to-north valley in the Pyrenees. Our results indicate that O3 abatement strategies cannot rely only on NOx emission control but must include a significant reduction in anthropogenic sources of VOCs. In addition, our results show that mitigation strategies intended to reduce O3 should be designed according to the local meteorology, air transport, particular ozone regimes, and oxidation capacity of the atmosphere of the urban area.