Urban Building Energy Modeling Research Articles

A novel acceleration approach to shadow calculation based on sunlight channel for urban building energy modeling

Shadow effect among buildings has great impacts on the building energy consumption and the performance of building photovoltaic, and calculating shadows on building surfaces in urban building energy modeling (UBEM) faces challenges of inefficiency, especially for dense urban areas. In this study, a novel shadow calculation approach based on sunlight channel is proposed that can streamline the surrounding environment and accelerate the shadow calculation process. The sunlight-channel algorithm can further accelerate the shadow calculation process by dynamically predetermining the shading surfaces according to the actual solar position. In a real urban context, the proposed approach can accelerate the computation process by over 10 times over the baseline and over 34 times over the non-accelerated method, with a mean absolute percentage error (MAPE) of 1.13% for the total solar radiation. The proposed approach copes well with both large-scale urban models and the complexity of building structures, particularly for urban models with complex changes in building heights. This approach can significantly enhance the computational efficiency in complex urban environments, facilitating an accurate and rapid analysis of the energy consumption and solar potential of buildings in dense cities.

A new workflow for load forecasting in planning areas: Considering uncertainties in building scenarios

Urban Building Energy Model (UBEM) is a widely used method for load forecasting in urban energy planning. It typically depends on reference buildings to simplify particular scenarios and uses energy calculation software for load forecasting. However, significant challenges exist when using this approach in unbuilt areas, because there is no established distribution of buildings or a widely recognized database of reference buildings. To overcome this technical barrier, this paper introduces a new workflow - Scenario Urban Building Energy Model (S-UBEM). This approach utilizes publicly available databases to establish a reference building database and a stochastic human behavior database. By utilizing these databases and planning documents, the methodology can construct building energy use scenarios for the undeveloped area, ultimately achieving load forecasting. In this paper, the proposed method is validated by transforming the built-up area into an unbuilt state. The results demonstrate that S-UBEM more accurately represents the spatial and temporal distribution of energy consumption. This is evidenced by a 28.3 % reduction in the annual energy prediction error compared to the conventional method, as well as by visualizing the distribution of energy consumption at the building level.

Advancing Urban Building Energy Modeling: Building Energy Simulations for Three Commercial Building Stocks through Archetype Development

Urban building energy models (UBEMs), developed to understand the energy performance of building stocks of a region, can aid in key decisions related to energy policy and climate change solutions. However, creating a city-scale UBEM is challenging due to the requirements of diverse geometric and non-geometric datasets. Thus, we aimed to further elucidate the process of creating a UBEM with disparate and scarce data based on a bottom-up, physics-based approach. We focused on three typically overlooked but functionally important commercial building stocks, which are sales and shopping, healthcare facilities, and food sales and services, in the region of Pittsburgh, Pennsylvania. We harvested relevant local building information and employed photogrammetry and image processing. We created archetypes for key building types, designed 3D buildings with SketchUp, and performed an energy analysis using EnergyPlus. The average annual simulated energy use intensities (EUIs) were 528 kWh/m2, 822 kWh/m2, and 2894 kWh/m2 for sales and shopping, healthcare facilities, and food sales and services, respectively. In addition to variations found in the simulated energy use pattern among the stocks, considerable variations were observed within buildings of the same stock. About 9% and 11% errors were observed for sales and shopping and healthcare facilities when validating the simulated results with the actual data. The suggested energy conservation measures could reduce the annual EUI by 10–26% depending on the building use type. The UBEM results can assist in finding energy-efficient retrofit solutions with respect to the energy and carbon reduction goal for commercial building stocks at the city scale. The limitations highlighted may be considered for higher accuracy, and the UBEM has a high potential to integrate with urban climate and energy models, circular economy, and life cycle assessment for sustainable urban planning.

Developing urban building energy models for shanghai city with multi-source open data

Urban building energy modeling is crucial for guiding carbon reduction policies, but acquiring reliable data at the urban scale remains challenging. This study develops a model for Shanghai City, China, by integrating multi-source open data. Eight data sources were collected, including maps, satellite imagery, and GIS data, covering 609,763 building footprints and 539,119 buildings (1.57 billion m2). Spatial analysis, supervised learning, and unsupervised machine learning methods were used to categorize buildings into 63 prototypes, and classification accuracy reached 95 %. Historical satellite data and community boundaries determined the year built for over 95 % of buildings. Prototypes were modeled in AutoBPS using local energy saving standards and simulated in EnergyPlus to derive energy use intensities aligning with government ranges. This work demonstrates a practical data fusion approach to develop large-scale, reliable urban building energy models. Integrating heterogeneous open data sources expands the coverage of open data and improves accuracy. The framework and insights provide a valuable foundation to leverage open data for advancing city-scale energy modeling and sustainability planning.

Mapping the CityGML Energy ADE to CityGML 3.0 Using a Model-Driven Approach

With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to better understand the energy performance of the building stock and can contribute to defining refurbishment strategies. However, UBEM applications require lots of heterogeneous data, eventually advocating for standards for data interoperability. The Energy Application Domain Extension has been created to cope with UBEM data requirements and offers a standardised data model that builds upon the CityGML standard. The Energy ADE 1.0, released in 2018, creates new classes and adds new properties to existing classes of the CityGML 2.0 Core and Building modules. CityGML 3.0, released in 2021, has introduced several changes to the data model and its ADE mechanism. These changes render the Energy ADE incompatible with CityGML 3.0. This article presents how the Energy ADE has been ported to CityGML 3.0 to allow, on the one hand, for a lossless data conversion and, on the other hand, to exploit the new characteristics of CityGML 3.0 while keeping a logical symmetry between the ADE classes of both CityGML versions. The article describes the chosen methodology, the mapping strategies, the implementation steps, as well as the data conversion tests to check the validity of the “new” Energy ADE for CityGML 3.0.

Impact of time resolution on estimation of energy savings using a copula-based calibration in UBEM

Urban Building Energy modelling (UBEM) has emerged in the last decade as an important tool to accelerate energy transition in the building sector. To fulfil one of its major purposes - forecasting energy savings for potential energy conservation measures at an urban scale, several challenges are still to be addressed. Two key challenges include: 1) the need to calibrate models with a large number of unknown parameters across numerous buildings (either individually or through archetype representation); 2) the limited availability of high-resolution measured data, which raises concerns about calibrated models based solely on yearly values for accurate energy savings forecast. This study addresses these challenges using a case study of 35 buildings in a district in Stockholm, Sweden. Firstly, a new iterative Approximate Bayesian Computation (ABC) method for calibration is proposed, incorporating a copula-based sampling process at each iteration. Secondly, the new calibration process is applied with three different time resolutions to examine the impact of data resolution on forecasted energy usage, particularly when applied to a classical energy conservation measure. Results demonstrate the efficiency of the new method across the 35 buildings, facilitating the rapid population of a final joint distribution for the nine unknown parameters considered in each building. While the marginals may be strongly influenced by the time resolution, the forecasted energy consumption remains identical across the three analysed time resolutions. However, a noticeable difference is observed when the ECM pertains to a formerly unknown or known parameter.

An ontology-driven method for urban building energy modeling

The field of urban building energy modeling, embracing diverse aspects such as geography, construction, and materials. There is a lack of a comprehensive information integration framework to streamline cross-domain data in a systematic manner and generate simulation files that are easily calculable. This study addresses this gap by proposing two key ontologies: Building Template Ontology for managing building energy simulation templates and Urban Building Ontology for organizing building physics information. Utilizing this ontology-driven method, this study enhances flexibility in information fusion, exploits logical relationships between simulation inputs, and provides a lightweight solution for structured urban energy performance analysis. Energy simulations were conducted on over 5,000 buildings across three cities, and the impact of energy retrofit measures was further quantified, revealing potential savings of 0.1 % to 4.0 %, 1.2 % to 10.2 %, and 2.5 % to 6.9 % for building envelope, lighting, and air conditioning improvements, respectively. This study empowers urban stakeholders, designers, and managers to streamline the model construction and provides valuable insights into energy consumption patterns. Acknowledging limitations in current ontologies, including restricted building type templates, lack of real-time instance information, and absence of direct energy verification, this study underscores the imperative to address these for future advancements.

Validating ‘GIS-UBEM’—A Residential Open Data-Driven Urban Building Energy Model

The study of energy consumption in buildings, particularly residential ones, brings with it significant socio-economic and environmental implications, as it accounts for approximately 40% of CO2 emissions, 18% in the case of residential buildings, in Europe. On a number of levels, energy consumption serves as a key parameter in urban sustainability indicators and energy plans. Access to data on energy consumption is crucial for energy planning, management, knowledge generation, and awareness. Urban Building Energy Models (UBEMs), which are emerging tools for simulating energy consumption at neighborhood scale, allow for more efficient intervention and energy rehabilitation planning. However, UBEM validation requires reliable reference data, which are often challenging to obtain at urban scale due to privacy concerns and data accessibility issues. Recent advances, such as automation and open data utilization, are proving promising in addressing these challenges. This study aims to provide a standardized UBEM validation process by presenting a case study that was carried out utilizing open data to develop bottom-up engineering models of residential energy demand at urban scale, with a resolution level of individual buildings, and a subsequent adjustment and validation using reference tools. This study confirms that the validated GIS-UBEM model heating and cooling demands and consumption fall within the confidence bands of ±15% and ±12.5%, i.e., the confidence bands required for the approval of official alternative simulation methods for energy certification. This paves the way for its application in urban-scale studies and practices with a well-established margin of confidence, covering a wide range of building typologies, construction models, and climates comparable to those considered in the validation process. The primary application of this model is to determine the starting point and subsequent evaluation of improvement scenarios at a district scale, examining issues such as massive energy rehabilitation interventions, energy planning, demand analysis, vulnerability studies, etc.

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

Systematic review of the efficacy of data-driven urban building energy models during extreme heat in cities: Current trends and future outlook

Analysis of Peak Demand Reduction and Energy Saving in a Mixed-Use Community through Urban Building Energy Modeling

Energy saving in buildings is essential as buildings’ operational energy use constitutes 30% of global energy consumption. Urban building energy modeling (UBEM) effectively understands urban energy consumption. This paper applied UBEM to assess the potential of peak demand reduction and energy saving in a mixed-use community, using 955 residential buildings, 35 office buildings and 7 hotels in Shenzhen, China, as a case study. The building type and period were collected based on the GIS dataset. Then, the baseline models were generated by the UBEM tool—AutoBPS. Five scenarios were analyzed: retrofit-window, retrofit-air conditioner (AC), retrofit-lighting, rooftop photovoltaic (PV), and demand response. The five scenarios replaced the windows, enhanced the AC, upgraded the lighting, covered 60% of the roof area with PV, and had a temperature reset from 17:00 to 23:00, respectively. The results show that using retrofit-windows is the most effective scenario for reducing peak demand at 19.09%, and PV reduces energy use intensity (EUI) best at 29.96%. Demand response is recommended when further investment is not desired. Retrofit-lighting is suggested for its low-cost, low-risk investment, with the payback period (PBP) not exceeding 4.54 years. When the investment is abundant, retrofit-windows are recommended for public buildings, while PV is recommended for residential buildings. The research might provide practical insights into energy policy formulation.

A novel resistance-capacitance model for evaluating urban building energy loads considering construction boundary heterogeneity

To address the problems associated with the accuracy of existing resistance-capacitance (RC)-based urban building energy modeling (UBEM) being low, a novel RC-based UBEM was developed. This model takes into account construction boundary heterogeneity by clustering and lumping together building construction elements with similar boundary conditions. In addition to buildings with single thermal zone, the novel RC model was extended to buildings with multiple thermal zones by using voltage-controlled voltage sources (VCVSs). The model was validated by four ASHRAE 140 cases with single thermal zone and two real urban building cases with multiple thermal zones. To verify the effectiveness of the proposed lumping concept, comparisons between the proposed RC model and the existing RC models were conducted. The results show that the novel RC-based UBEM could reach the balance between accuracy and efficiency. The hourly heating/cooling loads of the novel RC model agree well with those produced by the benchmark model EnergyPlus. The novel model has shown an efficiency improvement of approximately 78% compared to EnergyPlus. The 4R1C model is more accurate than 3R2C model especially for cases with large capacitance and resistance differences between interior and exterior construction components. The accuracy of the proposed RC-based UBEM is significantly improved compared with the existing RC-based UBEM. Thus, it is necessary to consider the construction boundary heterogeneity in the RC models for evaluating urban building energy loads.

Addressing building related energy burden, air pollution, and carbon emissions of a low-income community in Southern California

This study examines the impact of low-income assistance and electrification programs on a disadvantaged community in Southern California. An urban building energy model is paired with an AC power flow and electric distribution system degradation model to evaluate how the cost of energy, carbon emissions, and pollutant emissions change after applying building weatherization, energy efficiency, and electrification measures to the community. Results show that traditional weatherization and energy efficiency measures (upgrading lighting and appliances, improving insulation to current building code standards) are the most cost-effective, reducing the cost of energy and carbon emissions by 10–20 % for the current community. Heat pump water heaters offer a 40 % average reduction in carbon emissions and almost 50 % decrease in criteria pollutant emissions, but at a cost increase of 17–22 %. Appliance electrification also reduces carbon emissions 5–10 % but increases cost by 7 % to 25 %. For reducing carbon, government programs that support building electrification are most cost-effective when they combine switching from natural gas to electricity with high efficiency system. Electrifying hot water and appliances effectively reduces emissions but must be paired with improved low-income assistance programs to prevent increased energy burden for low-income families. The urban building energy model and electrical distribution simulations used in this study can be replicated in other low-income communities.

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.