Individual Building Level Research Articles

A GIS-Based Approach for Urban Building Energy Modeling under Climate Change with High Spatial and Temporal Resolution

The energy demand and associated greenhouse gas (GHG) emissions of buildings are significantly affected by the characteristics of the building and local climate conditions. While energy use datasets with high spatial and temporal resolution are highly needed in the context of climate change, energy use monitoring data are not available for most cities. This study introduces an approach combining building energy simulation, climate change modeling, and GIS spatial analysis techniques to develop an energy demand data inventory enabling assessment of the impacts of climate change on building energy consumption in Shanghai, China. Our results suggest that all types of buildings exhibit a net increase in their annual energy demand under the projected future (2050) climate conditions, with the highest increase in energy demand attributed to Heating, Ventilation, and Cooling (HVAC) systems. Variations in building energy demand are found across building types. Due to the large number of residential buildings, they are the main contributor to the increases in energy demand and associated CO2 emissions. The hourly residential building energy demand on a typical hot summer day (29 July) under the 2050 climate condition at 1 p.m. is found to increase by more than 40%, indicating a risk of energy supply shortage if no actions are taken. The spatial pattern of total annual building energy demand at the individual building level exhibited high spatial heterogeneity with some hotspots. This study provides an alternative method to develop a building energy demand inventory with high temporal resolution at the individual building scale for cities lacking energy use monitoring data, supporting the assessment of building energy and GHG emissions under both current and future climate scenarios at minimal cost.

Modelling Hourly Thermal Energy Demand: A Machine Learning Approach of Residential District Heating Substations in Turin

This paper explores the application of the XGBoost machine learning model for forecasting the hourly thermal demand in District Heating Systems, aligning with the European Union’s ambitious sustainability targets as outlined in the Renewable Energy Directive (RED) and the Energy Efficiency Directive (EED). Accurate forecasts of thermal demand are crucial for enhancing the efficiency of district heating systems through the integration of renewable energy sources and the adoption of waste heat recovery, thereby contributing significantly to achieving climate neutrality by the year 2050. This study presents a dual approach to forecasting: at the individual building level, and at an aggregated level by considering the average characteristics of the served building stock. Through a comprehensive case study of the Turin district heating system (Italy), which comprises hourly data from approximately 200 heat exchange substations across nine heating seasons, this research evaluates the comparative effectiveness of different forecasting approaches in terms of prediction accuracy and computational efficiency. The findings aim to guide district heating operators and planners in selecting the most suitable forecasting approach based on available input information, desired accuracy, and computational constraints, contributing to the strategic planning and development of sustainable and efficient district heating systems.

Computer vision tools for early post-disaster assessment: Enhancing generalizability

Remote sensing data, particularly satellite imagery, have made early, post-hazard aerial damage assessment possible due to its fast availability and extensive coverage. Despite breakthroughs in using deep computer vision with satellite image inputs, achieving high generalizability across diverse hazards and locations remains the main obstacle to effectively deploying early assessment tools in real-world scenarios, as the same hazard can manifest differently across various landscapes and urban textures. This challenge may be overlooked when working with curated datasets with minimal (test-to-train) shifts in urban textures and hazard damage features (e.g., due to undersized study regions), leaving models ill-prepared for real-world scenarios. The primary objective of this study was to understand non-trivial generalizability challenges by taking the 2023 Turkiye earthquake and the Maui wildfire incidents as “training” and “unseen test” events that simulated a demanding scenario. Subsequently, strategies such as augmenting image channels with damage proxy maps, data fusion, deep ensemble learning, and Test Time Augmentation were exclusively designed and implemented to address those challenges. These measures significantly improved the damage detection, with or without severity classification, with F1 scores increased from 0.71 to 0.82 and 0.40 to 0.87, respectively. Furthermore, and through data fusion, the proposed framework accommodates estimating socioeconomic loss metrics at the individual building level, supporting both response and recovery phases. This research has the potential to enhance the effectiveness of rapid aerial damage assessment models, ultimately aiding in more efficient and targeted disaster response and recovery efforts. Data, models, and codes are available at https://github.com/TRG-AI4Good/Lahaina_Generalizability.

Examining direct and indirect flood damages in residential and business sectors through an empirical lens.

Investment to reduce flood risk for social and economic wellbeing requires quantitative evidence to guide decisions. Direct and indirect flood damages at individual household and business building levels were assessed in this study using multivariate analysis with three groups of flood damage attributes, i.e., flood characteristics, socioeconomic conditions, and building types. A total of 172 and 45 respondents from residential and commercial buildings were gathered through door-to-door interviews at areas in Peninsular Malaysia that were pre-identified to have frequently flooded. Two main findings can be drawn from this study. First, flood damage is greatly contributed by high-income households and businesses, despite them being less exposed to floods than low-income earners. This supports the current use of mean economic damage in engineering-based flood intervention analysis. Second, indirect damages increase with the increase in family size, indicating the importance of strengthening preparedness and social support to those with great social responsibility. Overall, the study highlights the importance of holistic flood management accounting for both direct and indirect losses.

Evaluation of debris-flow building damage forecasts

Abstract. Reliable forecasts of building damage due to debris flows may provide situational awareness and guide land and emergency management decisions. Application of debris-flow runout models to generate such forecasts requires combining hazard intensity predictions with fragility functions that link hazard intensity with building damage. In this study, we evaluated the performance of building damage forecasts for the 9 January 2018 Montecito postfire debris-flow runout event, in which over 500 buildings were damaged. We constructed forecasts using either peak debris-flow depth or momentum flux as the hazard intensity measure and applied each approach using three debris-flow runout models (RAMMS, FLO-2D, and D-Claw). Generated forecasts were based on averaging multiple simulations that sampled a range of debris-flow volume and mobility, reflecting typical sources and magnitude of pre-event uncertainty. We found that only forecasts made with momentum flux and the D-Claw model could correctly predict the observed number of damaged buildings and the spatial patterns of building damage. However, the best forecast only predicted 50 % of the observed damaged buildings correctly and had coherent spatial patterns of incorrectly predicted building damage (i.e., false positives and false negatives). These results indicate that forecasts made at the building level reliably reflect the spatial pattern of damage but do not support interpretation at the individual building level. We found the event size strongly influences the number of damaged buildings and the spatial pattern of debris-flow depth and velocity. Consequently, future research on the link between precipitation and the volume of sediment mobilized may have the greatest effect on reducing uncertainty in building damage forecasts. Finally, because we found that both depth and velocity are needed to predict building damage, comparing debris-flow models against spatially distributed observations of building damage is a more stringent test for model fidelity than comparison against the extent of debris-flow runout.

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.

Good architectural design as a catalyst for improving the quality of life in cities

The growth of cities has led to multiple transformations that have changed the urban fabric; for this reason, many locations in urban areas remain neglected. The present study was undertaken to search for solutions that would minimize adverse changes in the urban fabric, buildings, and urban spaces. The aim of the study was to present examples of good architectural design that can be considered catalysts that improve the quality of life in cities. Architectural design contributes to urban development not only at the level of individual buildings, but also at the level of the overall quality of urban life. The research methodology involved developing a theoretical framework for the research topic, and applying selected indicators to analyse several international examples and investigate the strategies deployed in various countries around the world. The study demonstrated that good architectural design must be inspired by a city’s history, and that the urban context should be considered in order to integrate new urban development with the surrounding environment through catalytic mechanisms and their indicators. Sustainability, health, education, safety, psychology, material sourcing, and building materials are the most important quality of life (QoL) factors for developing a good architectural project.

Integrating post-event very high resolution SAR imagery and machine learning for building-level earthquake damage assessment

AbstractEarthquakes have devastating effects on densely urbanised regions, requiring rapid and extensive damage assessment to guide resource allocation and recovery efforts. Traditional damage assessment is time-consuming, resource-intensive, and faces challenges in covering vast affected areas, often limiting timely decision-making. Space-borne synthetic aperture radars (SAR) have gained attention for their all-weather and day-night imaging capabilities. These advantages, coupled with wide coverage, short revisits and very high resolution (VHR), have created opportunities for using SAR data in disaster response. However, most SAR studies for post-earthquake damage assessment rely on change detection methods using pre-event SAR images, which are often unavailable in operational scenarios. Limited studies using solely post-event SAR data primarily concentrate on city-block-level damage assessment, thus not fully exploiting the VHR SAR potential. This paper presents a novel method integrating solely post-event VHR SAR imagery and machine learning (ML) for regional-scale post-earthquake damage assessment at the individual building-level. We first used supervised learning on case-specific datasets, and then introduced a combined learning approach, incorporating inventories from multiple case studies to assess generalisation. Finally, the ML model was tested on unseen study areas, to evaluate its flexibility in unfamiliar contexts. The method was implemented using datasets collected during the Earthquake Engineering Field Investigation Team (EEFIT) reconnaissance missions following the 2021 Nippes earthquake and the 2023 Kahramanmaraş earthquake sequence. The results demonstrate the method’s ability to classify standing and collapsed buildings, achieving up to 72% overall accuracy on unseen regions. The proposed method has potential for future disaster assessments, thereby contributing to more effective earthquake management strategies.

Analysis of the Operational Outcomes of an Energy-Sharing System for Low-Carbon Energy Community in South Korea

The transition to a net-zero energy system is being promoted in the energy sector, which has led to the creation of energy prosumers. These produce, consume, and trade energy using renewable energy systems installed in buildings or complexes. Here, a community was set as the target to apply the concept of an energy prosumer at the individual building and regional levels. Energy-sharing systems were divided into three categories: energy production, energy storage, and energy management. Energy-sharing systems centered on electrical energy—photovoltaic, battery energy storage, and energy management systems—were installed in two communities located in South Korea, and the energy-sharing effects of the system operation were reported. Monthly power consumption in spring and fall exhibited significant savings of approximately three times that of winter consumption, owing to the energy-sharing systems. Daily hourly power-consumption patterns differed on weekdays and weekends because of the weekday working and building-use hours of the communities. Energy could be shared between communities and buildings because of surplus energy. More surplus power was available for energy sharing on weekends because power consumption was lower. Because energy trading and sharing are restricted, the related laws are being revised. Therefore, a low-carbon community can be realized through surplus energy trading and sharing technology between communities and buildings as renewable energy systems spread owing to low carbonization.

Multi-objective optimization of mitigation strategies for buildings subject to multiple hazards

Natural hazards can have a devastating impact on communities, leading to social and economic losses. These effects are particularly severe in multi-hazard contexts, where multiple disruptive events occur simultaneously or consecutively (such as earthquakes and tsunamis). To reduce the impact of such events, it is critical to enhance community resilience and make it more capable of withstanding and recovering from diverse types of damage. In this study, we propose a multi-objective optimization model to determine optimal retrofitting strategies to enhance community resilience under multiple hazards. We used the proposed model to analyze the impact of earthquake and tsunami hazards on the community of Seaside, Oregon. It assesses the effectiveness of different retrofitting strategies at the individual building level, considering the conflicting objectives of reducing overall economic loss, population dislocation, and building repair times. Our results demonstrate that retrofitting buildings to achieve higher seismic codes can significantly reduce the impact of natural hazards on structural damage, population dislocation, and building repair times. Additionally, our findings reveal the importance of considering geographical location and mitigation measures when optimizing retrofitting strategies. By considering budget constraints and community resilience metrics, our model identifies the most effective retrofitting strategies for individual buildings of Seaside, which ultimately helps the community make informed decisions about investments to reduce the impact of natural hazards. Overall, this study provides valuable insights into the importance of enhancing community resilience in multi-hazard contexts and showcases the use of a multi-objective optimization model to identify optimal retrofitting strategies.

Cloud Modelling of Property-Level Flood Exposure in Megacities

Surface water flood risk is projected to increase worldwide due to the growth of cities as well as the frequency of extreme rainfall events. Flood risk modelling at high resolution in megacities is now feasible due to the advent of high spatial resolution terrain data, fast and accurate hydrodynamic models, and the power of cloud computing platforms. Analysing the flood exposure of urban features in these cities during multiple storm events is essential to understanding flood risk for insurance and planning and ultimately for designing resilient solutions. This study focuses on London, UK, a sprawling megacity that has experienced damaging floods in the last few years. The analysis highlights the key role of accurate digital terrain models (DTMs) in hydrodynamic models. Flood exposure at individual building level is evaluated using the outputs from the CityCAT model driven by a range of design storms of different magnitudes, including validation with observations of a real storm event that hit London on the 12 July 2021. Overall, a novel demonstration is presented of how cloud-based flood modelling can be used to inform exposure insurance and flood resilience in cities of any size worldwide, and a specification is presented of what datasets are needed to achieve this aim.

OMOD: An open-source tool for creating disaggregated mobility demand based on OpenStreetMap

This paper introduces OMOD (OpenStreetMap Mobility Demand Generator), a new open-source activity-based mobility demand generation tool. OMOD uses a data-driven approach, calibrated with household travel survey data, to generate a population of agents with detailed daily activity schedules that state what activities each agent plans to conduct, where, and for how long. The temporal aspect of the output is wholly disaggregated, while the spatial aspect is given on the level of individual buildings. In contrast to other existing models, OMOD is freely available, open-source, works out-of-the-box, can be applied to anywhere in Germany with the ambition to widen the scope to other countries, and only requires freely available OpenStreetMap (OSM) data from the user. With OMOD, it is easy for non-experts to create realistic mobility demand, which can be used in transportation studies, energy system modeling, communications system research, et cetera. This paper describes OMOD’s architecture and validates the model for three cities ranging from 200,000 to 2.5 million inhabitants.

Personalized Vulnerability Assessment of Customized Low-Rise Wood-Frame Residential Structures under Hurricane Wind Loads: A Flexible Scenario-Based Simulation Approach

Hurricanes are one of the costliest and deadliest weather and climate disasters that impact the United States, largely affecting the residential sector. Residential structures are essential for the health and safety of the community during a hurricane event. However, homeowners typically have to rely on general information and instructions to understand their level of risks. The knowledge of structural vulnerability to hurricanes at the individual residential building level is often limited, which hinders personalized disaster preparation and mitigation decision-making. To this end, a vulnerability assessment method is proposed for individual homeowners to create customized finite-element models that quantify wind damage of low-rise wood-frame residential structures under various scenarios using ANSYS 2020 R1 parametric design language. A case study was conducted to test the proposed methodology and investigate prevailing failure modes under different structural characteristics and wind scenarios. Specifically, the impacts of roof framing features and previous exposure to environmental conditions on the potential damage of residential buildings under hurricane wind loads were studied. Analysis results, including the percentage of failed sheathing, nailed connections and framing members, are used to inform personalized mitigation recommendations. The proposed customized vulnerability assessment may increase homeowners’ situational awareness of disasters and promote rational retrofitting decisions.

A latent auction model of firm establishment location choice in the Greater Toronto Area

We present a maximum bid model for business establishments in the Greater Toronto Area (GTA). The model is based on a latent auction approach, wherein price and utility are jointly modeled in a random utility-maximizing framework. Structural relationships between establishments by their industry classification and type (i.e., headquarters, subsidiary, branch, or single location) are explored through a generalized nesting correlation structure. This structure provides a mechanism for differentiating between branch and single-location establishments, having seemingly similar functions, but differing in their allocation between industry and type nests. Maximum bid models are estimated for the full establishment population in the GTA at the level of individual buildings. The model scope allows us to provide a range of insights into establishment location choice behavior, which was previously infeasible due to data limitations. We find that professional service establishments tend to locate near passenger rail stations (i.e., subway and LRT), while industrial establishments tend to locate near major highway interchanges. We confirm the finding by Kenworthy and Laube (1999) that Canadian cities are less auto-dependent than US cities, albeit from the perspective of establishment location rather than individual vehicle travel data.

Assessment of Subsidization Strategies for Multi-Objective Optimization of Energy Efficiency Measures for Building Renovation at District Scale

In recent years, public authorities around the world have used incentive strategies to encourage the renovation of the existing building stock to meet the set carbon neutrality targets. However, the design of the incentives typically does not consider that the subsidized energy efficiency measures should result in robust long-term improvements with respect to various objectives. Moreover, building energy retrofit analyses are commonly conducted at the individual building level rather than at urban scale, which could instead significantly accelerate the renovation rate. In this context, the current research aims to combine these different factors to support the design of building energy retrofit programs. We developed 21 subsidization strategies and their impact was evaluated on a parametric multi-objective optimization with respect to energy, economic, and environmental performance for a district located in Bolzano, Northern Italy. The optimization was performed considering a set of energy efficiency measures, pertaining to building envelope, climate change, economic scenarios, and two types of energy supplies. The results showed that (1) the impact of climate change is limited for the climate of Bolzano; (2) the type of energy supply strongly influences the economic feasibility of the retrofit investments; (3) when the investment is profitable, the optimal solutions include those measures with the largest impact on energy efficiency; and (4) subsidization strategies modify the number and composition of the Pareto solutions.

Utilizing Building Offset and Shadow to Retrieve Urban Building Heights with ICESat-2 Photons

Building height serves as an essential feature of urban morphology that provides valuable insights into human socio-cultural behaviors and their impact on the environment in an urban milieu. However, openly accessible building height information at the individual building level is still lacking and remains sorely limited. Previous studies have shown that the ICESat-2′s ATL03/08 products are of good accuracy for urban building heights retrieval, however, these studies are limited to areas with available data coverage. To this end, we propose a method for extracting urban building height by using ICESat-2 ATL03 photons and high-resolution remote sensing images. We first extracted the information of building roof to footprint offsets and building shadows from high resolution imagery using multitasking CNN frameworks. Using the building height samples calculated from ICESat-2 ATL03 photons, we developed a building height estimation method that combines building offset and shadow length information. We assessed the efficacy of the proposed method in the Wujiaochang area of Shanghai city, China. The results indicated that the proposed method is able to extract building height with a MAE of 4.7 m, and outperforms the traditional shadow-based and offset-based method. We believe that the proposed method is a good candidate for accurately retrieving building heights on a city-wide scale.

Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R2 of 0.99. At the hourly level, the MAPE of 6.2% and R2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.

Prediction of residential and non-residential building usage in Germany based on a novel nationwide reference data set

Building usage is an important variable in modelling the energetic, material and social properties of a building stock. Gathering this data on large geographical scale, and in the necessary temporal and spatial resolution, that means, on building level, is a challenging task. Machine Learning algorithms like Random Forest have proven useful in predicting building-related features in the past but often resort to training sets of limited geographic scope, for example, cities. This study presents a workflow of predicting the semantic attribute of usage on the level of individual buildings. Based on screening data of the previous ENOB:dataNWG project, a novel building ground-truth data set distributed across Germany, a Random Forest algorithm is used to assess how the German building stock can be classified according to its residential or non-residential use. Different sampling strategies had been applied in order to find a robust evaluation metric for the classifier. Furthermore, the relevance of the feature set is highlighted and it is examined whether regional differences in classification quality exist. Results show that a classification of residential and non-residential building footprints has good prospects with an AUC of up to 0.9.

Low-cost and rapid sensors for wastewater surveillance at low-resource settings

Wastewater surveillance enables tracking infectious disease dynamics and community prevalence quantification for public health. However, the testing requirement of centralized laboratories and well-trained staff challenges underserved areas and low-resource settings. The development of new rapid and low-cost sensors enables in-field testing of wastewater from the community to the individual building levels for early warning of pandemics.

Quantifying urban heat exposure at fine scale - modeling outdoor and indoor temperatures using citizen science and VHR remote sensing

Global warming and advancing urbanization lead to an increased heat exposure for city dwellers. Especially during summertime heatwaves, extreme daytime as well as high nighttime temperatures expose vulnerable people to potentially deadly heat risk. This applies specifically to indoor air temperatures, since people spend a lot of their time indoors. Against this background, this study relates outdoor and indoor air temperature measurements to area-wide geospatial data regarding summertime urban heat in the city of Augsburg, Germany. Air temperature data is collected from formalized as well as citizen science measurements, while remote sensing data with very-high spatial resolution (VHR) is utilized for assessment of their drivers and influencing factors. A land use regression approach is developed for city-wide modeling of outdoor and indoor air temperatures at the level of individual residential buildings. Daytime outdoor temperatures could be largely explained by vegetation parameters and imperviousness, whereas nighttime temperatures were more related to the building stock and radiation properties. For indoor temperatures, building density as well as building height and volume are additionally relevant. Outdoor air temperatures could be modeled with higher accuracies (mean absolute error (MAE) < 0.5 °C) compared to indoor temperatures (MAE < 1.5 °C), whereas outdoor and indoor modeling results are consistent with well-known patterns across different local climate zones (LCZ).