Search Results (44)

Urban Building Energy Modeling (UBEM) offers a scalable, physics-based method to simulate energy demand at the district level, enabling data-driven district energy demand planning and optimization. However, translating UBEM into a reliable, openly replicable workflow remains a significant methodological gap. In particular, limited attention has been devoted to the development of transparent and transferable UBEM workflows capable of systematically quantifying the impact of modeling assumptions on district-scale thermal demand accuracy. This paper presents and validates a five-step UBEM pipeline integrating freely available geospatial data from OpenStreetMap (OSM), archetype-based building characterization, multi-scenario EnergyPlus simulation via the Urban Modeling Interface (UMI) within a structured validation framework. To improve interpretability and reproducibility, a dedicated three-scenario simulation protocol was developed to isolate and quantify the influence of geometry simplifications, archetype assumptions, and weather data fidelity on model accuracy. The workflow is demonstrated through application to a real district heating system (DHS) in northern Italy, encompassing UBEM results validated against monitored consumption data at different temporal resolutions. The refined model achieves a district-scale annual magnitude error of 1.30% between real and simulated data. Persistent limitations in domestic hot water representation and peak load estimation are identified as priorities for future development. Full article

Urban Building Energy Modelling (UBEM) is increasingly recognised as a key tool for bridging building-scale analysis and city-level planning, supporting the decarbonisation of urban areas. It enables the assessment of building performance and the exploration of retrofit strategies, policy scenarios, and renewable integration. Over the past decade, UBEM has continuously evolved, encompassing physics-based, data-driven, and hybrid approaches, often supported by archetype generation and GIS workflows. Recent research also integrates microclimate, mobility, and green infrastructure. While this enriches analysis outcomes, it also leads to fragmentation, limiting tools’ comparability, transparency, and practical use by policymakers. This perspective paper provides a critical interpretation of the recent evolution in UBEM and proposes a goal-oriented classification of modelling approaches based on their final objectives rather than on calculation methodology. The paper also identifies three key gaps: the lack of scalable and consistent data frameworks, the absence of a shared terminology, and the need for criteria to assess whether models are “fit for purpose.” Addressing these issues through improved data infrastructures, conceptual clarity, and reliability assessment can support convergence without limiting innovation, fostering more robust and decision-relevant UBEM applications to support the energy transition. Full article

Accurate urban building energy modeling (UBEM) is constrained by mismatches between standard climate inputs and actual urban microclimate conditions. This study introduces a scalable, bottom-up, framework that integrates EnergyPlus building energy modeling simulation outputs with Earth observational and geographical-based urban morphology data, which are enhanced through machine learning techniques to improve energy demand predictions in urban settings. Applied to Los Angeles (LA), California, we evaluate the representativeness of typical meteorological year (TMYx) sampling sites against actual urban environmental conditions. We find that while satellite-derived surface temperatures show reasonable alignment with average city conditions, significant discrepancies are observed in urban form metrics such as tree cover, street cover, and building density, suggesting that TMYx stations should be placed in denser urban areas. We augment EnergyPlus simulations for 19 single-family buildings, with remote sensing data using machine learning models, to generate city-wide residential energy consumption heatmaps corrected for microclimate conditions. Models capture substantial intra-urban variation, with predicted energy use differing by approximately 10% between neighborhoods. Feature importance analysis highlights land surface temperature as a key predictor, underscoring its relevance to building energy research. We also find the majority of TMY3 sampling sites to be in low-vulnerability areas, underscoring the structural mismatch that is embedded in urban form and climate. This framework offers a scalable path for integrating urban microclimate effects into energy modeling to enable more precise and equitable energy policy and planning. Full article

Traditional urban building energy modeling often overlooks the complexity of spatial configurations and mutual shading effects, thereby limiting its accuracy. This study proposes a novel, interpretable, data-driven framework based on heterogeneous graph neural networks (GNNs) to uncover and characterize the complex interrelationships between building morphology and urban topology. Using a parametric platform, this study generated a graph dataset of 285 residential blocks in Wuhan, structured as a dual-level graph: Building Zone Graphs (BZGs) and Building Layout Graphs (BLGs). Four GNN models were trained based on the dataset, and the evaluated results demonstrate that GraphTransformer outperforms GCN, GAT, and GraphSAGE in capturing long-range spatial relationships―particularly those arising from shading and solar access interactions. On a validation set, GraphTransformer achieved superior predictive accuracy, with R² scores exceeding 0.85 and 0.90 for cooling and heating energy predictions, respectively. After that, post hoc interpretability analysis by GNNExplainer identified three important morphology features influencing building energy consumption. Critically, the model found that shading relationships encoded as graph edges―especially those between southern and western façades―had statistically significant influence on building energy consumption. Finally, this work establishes an efficient, interpretable surrogate modeling framework for urban-scale energy analysis, delivering quantifiable, design-actionable insights to support sustainable urban development. Full article

Retrofitting existing residential buildings is a critical strategy for achieving urban decarbonization while addressing public health disparities, particularly in communities disproportionately affected by environmental and socioeconomic stressors. This study presents a scalable urban building energy modeling framework that integrates physics-based simulations with machine learning to evaluate and prioritize health-driven retrofit strategies across residential building stocks. Synthetic datasets were generated through parametric simulations of representative building archetypes and retrofit scenarios, capturing variations in envelope performance, HVAC systems, infiltration rates, and ventilation strategies. Machine learning models were trained as surrogate predictors of building energy performance, enabling the rapid evaluation of retrofit impacts. A range of algorithms—including decision trees, random decision forests, gradient-boosting machines, support vector machines, k-nearest neighbors, and artificial neural networks—were evaluated. An artificial neural network implemented as a multilayer perceptron was selected for further analysis due to its strong predictive performance (R² = 0.94) and ability to capture complex nonlinear relationships among retrofit variables. The final model used the Port optimization algorithm for stable convergence and improved generalization. The framework is applied to Seattle’s Duwamish Valley, a community experiencing disproportionate environmental and health burdens, and is generalizable and transferable to other cities with comparable residential building stocks across a range of climatic and environmental contexts. The results highlight retrofit priorities—particularly infiltration reduction, HVAC upgrades, and improved envelope performance—that deliver co-benefits for energy efficiency, indoor environmental quality, and occupant health. The results demonstrate that machine learning-enhanced physics-based UBEM can significantly accelerate retrofit evaluation while preserving the interpretability of simulation-based approaches. The proposed framework provides a scalable approach for identifying health-informed retrofit pathways that support equitable urban decarbonization. Full article

Data scarcity limits robust assessment of urban overheating and its implications for building energy use, especially in complex-terrain cities such as those in mountain environments. In this context, Land Surface Temperature (LST) from thermal remote sensing can be used to map urban hotspots at high spatial resolution. Nevertheless, it does not provide the full set of hourly atmospheric variables required to run building energy simulations aimed at quantifying their impact and defining mitigation measures. Given these premises, this study proposes a methodology combining satellite-derived LST with ground meteorological measurements to assess Urban Heat Island (UHI) patterns and quantify how measured weather data selection affects urban building energy modeling (UBEM) outcomes. After selecting as a case study Bolzano, an Alpine city in Northern Italy, ECOSTRESS LST (2019–2025, May–August) was first processed and quality-screened to (1) compute ΔLST (urban–rural) and (2) identify diurnal and spatial overheating patterns across the building stock. Second, four measured weather datasets—one rural station and three urban stations located in the city core, in the industrial district, and in the urban edge—were used as boundary conditions in an EnergyPlus-based UBEM parametric campaign for 253 residential buildings, covering multiple envelope insulation levels and window-to-wall ratios. Results show strong diurnal asymmetry in surface overheating, with the largest contrasts in the afternoon and prominent industrial hotspots. Ground measurements confirm persistent intra-urban microclimatic differences, and the choice of measured weather dataset causes systematic shifts in simulated cooling demand and thermal comfort. The study highlights the need for weather data selection strategies based on microclimatic context rather than simple proximity, improving representativeness in UBEM applications for Alpine and other heterogeneous urban environments. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

Urban Building Energy Modeling (UBEM) plays a critical role in supporting data-driven strategies for the energy transition of cities. However, its application is often hindered by the lack of harmonized, high-quality input data representing the building stock. This paper presents the methodology and outputs of a national research project to construct an Italian library of reference buildings suitable for UBEM applications described with scorecards. The methodological workflow included six key phases: definition of a national data classification framework, acquisition and integration of heterogeneous data sources, data harmonization, statistical analysis and clustering, archetype formalization, and dissemination. The result is a library of 380 scorecards covering residential, educational, office, commercial, and catering buildings across multiple climate zones and construction periods. Each scorecard is based on empirical data from public databases, field surveys, or technical standards, and includes detailed descriptions of geometry, envelope characteristics, HVAC systems, internal gains, and ventilation. The scorecards are shared openly on the project’s website and were built to work with different UBEM platforms. Overall, both the method and the results help bring more consistency to UBEM practice and support better, data-driven urban energy planning. Full article

Balconies are widely recognized for enhancing urban livability, making them attractive elements to incorporate in building renovation projects. However, their impact on energy performance remains insufficiently studied, particularly in temperate climates, like the Mediterranean, where both heating and cooling demands must be considered. This article evaluates the energy impacts of integrating open balconies into housing retrofits on the space conditioning demand of dwellings through spatialized analysis at the urban block scale. Focusing on Barcelona’s Eixample district, a parametric Urban Building Energy Modeling (UBEM) was employed to assess how balcony design interacts with urban morphology (orientation, obstructions), building features (window-to-wall ratio, WWR), and balcony length. Results reveal a seasonal trade-off at the block scale: balconies increase heating demand (0.1–1.6 kWh/m²·yr) by reducing winter solar gain but decrease cooling demand (0.1–3.8 kWh/m²·yr) through summer shading. Net effects vary by unit position, with south-facing and moderately glazed dwellings benefiting the most. Deeper balconies (1.5–2 m) amplify both effects, while optimal depth depends on the window-to-wall ratio. Under future climates, retrofits combining insulation and balconies mitigate rising cooling demands more effectively than insulation alone, reducing block-level demand by up to 16%. Although balconies alone show modest energy savings at the block scale, they enhance localized thermal resilience. The study highlights the need for integrated retrofit strategies that balance thermal insulation with solar protection to address both current and future energy challenges while enhancing occupant well-being. Full article

With the high concentration of people in urban areas and their significant contribution to greenhouse gas emissions, along with the unmet needs of rural settlements, it is crucial to place greater emphasis on designing more energy-efficient cities through the use of renewable energy sources. The present study reviews the advantages of using urban building energy models (UBEMs) to simulate and design urban energy systems, providing valuable insights to researchers and decision-makers working towards an energy transition to more efficient and cleaner forms of energy. This review presents the current state-of-the-art of physics-based UBEMs, including common approaches, necessary components, tools developed, and applications that have benefited from their use to date. Additionally, this study highlights current limitations and gaps, encouraging academics and developers to pursue future research and development opportunities. Finally, it proposes three topics of interest that can benefit from the implementation of building-to-grid urban energy system models, showing promising future applications. Results of this review have shown that further research on UBEM-oriented urban district Digital Twins, endowed with Energy Communities and Positive Energy District abilities, along with the use of mature open-access and user-friendly tools, can accelerate the design and planning of modern district energy systems. Full article

Urban building energy prediction is a critical challenge for sustainable city planning and large-scale retrofit prioritization. However, traditional data-driven models struggle to capture real urban environments’ spatial and morphological complexity. In this study, we systematically benchmark a range of graph-based neural networks (GNNs)—including graph convolutional network (GCN), GraphSAGE, and several physics-informed graph attention network (GAT) variants—against conventional artificial neural network (ANN) baselines, using both shape coefficient and energy use intensity (EUI) stratification across three distinct residential districts. Extensive ablation and cross-district generalization experiments reveal that models explicitly incorporating interpretable physical edge features, such as inter-building distance and angular relation, achieve significantly improved prediction accuracy and robustness over standard approaches. Among all models, GraphSAGE demonstrates the best overall performance and generalization capability. At the same time, the effectiveness of specific GAT edge features is found to be district-dependent, reflecting variations in local morphology and spatial logic. Furthermore, explainability analysis shows that the integration of domain-relevant spatial features enhances model interpretability and provides actionable insight for urban retrofit and policy intervention. The results highlight the value of physics-informed GNNs (PINN) as a scalable, transferable, and transparent tool for urban energy modeling, supporting evidence-based decision making in the context of aging residential building upgrades and sustainable urban transformation. Full article

Urban Building Energy Modelling (UBEM) represents a comprehensive approach to investigate the intricate interplay of the various factors impacting energy use of groups of buildings, offering invaluable insights for urban planners, architects, building engineers, and policymakers. Nonetheless, available UBEM tools are still “research tools” and lack a unified standard addressing input, output, nomenclature, and calculation approaches. In this context, this study aims to conduct a comprehensive comparative analysis of two of the most used UBEM tools: Integrated Computational Design (iCD), the commercial tool provided by the Integrated Environmental Solutions (IES) company, and Urban Modelling Interface (umi), developed by the Massachusetts Institute of Technology (MIT). The comparative analysis includes each step of the UBEM workflow: the creation of the model, the assignment of input data, energy simulation, and visualisation and exportation of results. The tools are tested through the simulation of a case study to provide insights on the rationale and informed use of the tools, highlighting the risks associated with use by modellers with different levels of expertise. Moreover, this study provides tool developers and the scientific community with suggestions for major areas of improvement and standardisation in the field of UBEM, since substantial differences are still reported with respect to output, input, nomenclature, and calculation approaches. Full article

Typical Meteorological Year (TMY) datasets, widely used in building energy modeling, overlook Urban Heat Island (UHI) effects and future climate trends by relying on long-term data from rural stations such as airports. This study addresses this limitation by integrating Urban Weather Generator (UWG) simulations with CCWorldWeatherGen projections to produce microclimate-adjusted and future weather scenarios. These datasets were then incorporated into an Urban Building Energy Modeling (UBEM) framework using Urban Modeling Interface (UMI) to evaluate energy performance across a low-income residential neighborhood in Des Moines, Iowa. Results show that UHI intensity will rise from an annual average of 0.55 °C under current conditions to 0.60 °C by 2050 and 0.63 °C by 2080, with peak intensities in summer. The UHI elevates cooling Energy Use Intensity (EUI) by 7% today, with projections indicating a sharp increase—91% by 2050 and 154% by 2080. The UHI will further amplify cooling demand by 2.3% and 6.2% in 2050 and 2080, respectively. Conversely, heating EUI will decline by 20.0% by 2050 and 40.1% by 2080, with the UHI slightly reducing heating demand. Insulation mitigates cooling loads but becomes less effective for heating demand over time. These findings highlight the need for climate-adaptive policies, building retrofits, and UHI mitigation to manage future cooling demand. Full article

The concept of digital twins (DTs) has expanded to encompass buildings and cities, with urban building energy modeling (UBEM) playing a crucial role in predicting urban-scale energy consumption via modeling individual energy use and interactions. As a virtual model within urban digital twins (UDTs), UBEM offers the potential for managing energy in sustainable cities. However, UDTs face challenges with regard to integrating large-scale data and relying on bottom-up UBEM approaches. In this study, we propose an AI agent-based intelligent urban digital twin (I-UDT) to enhance DTs’ technical realization and UBEM’s service functionality. Integrating GPT within the UDT enabled the efficient integration of fragmented city-scale data and the extraction of building features, addressing the limitations of the service realization of traditional UBEM. This framework ensures continuous updates of the virtual urban model and the streamlined provision of updated information to users in future studies. This research establishes the concept of an I-UDT and lays a foundation for future implementations. The case studies include (1) data analysis, (2) prediction, (3) feature engineering, and (4) information services for 3500 buildings in Seoul. Through these case studies, the I-UDT was integrated and analyzed scattered data, predicted energy consumption, derived conditioned areas, and evaluated buildings on benchmark. Full article

To effectively decarbonize Europe’s building stock, it is crucial to monitor the progress of energy consumption and the associated emissions. This study addresses the challenge by developing a national-scale urban building energy model (nUBEM) using artificial intelligence to predict non-renewable primary energy consumption and associated GHG emissions for residential buildings. Applied to the case study of Spain, the nUBEM leverages open data from energy performance certificates (EPCs), cadastral records, INSPIRE cadastre data, digital terrain models (DTM), and national statistics, all aligned with European directives, ensuring adaptability across EU member states with similar open data frameworks. Using the XGBoost machine learning algorithm, the model analyzes the physical and geometrical characteristics of residential buildings in Spain. Our findings indicate that the XGBoost algorithm outperforms other techniques estimating building-level energy consumption and emissions. The nUBEM offers granular information on energy performance building-by-building related to their physical and geometrical characteristics. The results achieved surpass those of previous studies, demonstrating the model’s accuracy and potential impact. The nUBEM is a powerful tool for analyzing residential building stock and supporting data-driven decarbonization strategies. By providing reliable progress indicators for renovation policies, the methodology enhances compliance with EU directives and offers a scalable framework for monitoring decarbonization progress across Europe. Full article