Urban Energy Performance Research Articles

On the Role of the Building Envelope on the Urban Heat Island Mitigation and Building Energy Performance in Mediterranean Cities: A Case Study in Southern Italy

The urban heat island (UHI) effect is one of the largest climate-related issues concerning our cities due to the localized temperature increase in highly urbanized areas. This paper aims to investigate the impact of UHI mitigation techniques in promoting climate resilience, by reducing urban air temperatures and cooling energy consumption in buildings. To this end, four mitigation solutions regarding the building envelope—green roofs, green walls, cool roofs, and cool walls—were investigated for the city of Bari in Southern Italy and compared with the current baseline scenario. Hence, five scenarios were simulated—using the ENVI-met microclimate software—during three representative summer days, and the resulting microclimate changes were assessed. Based on these analyses, new climate files—one for each scenario—were generated and used as input to run energy simulations in EnergyPlus to estimate the building cooling consumption. Coupling the microclimate and the consumption outcomes, the mitigation strategies were evaluated from both an urban and building point of view. The study shows that urban characteristics, mainly geometry and materials, are crucial for the UHI phenomenon. All the applied technologies seem to be effective. However, green walls proved to be more efficient in reducing outdoor temperatures (1 °C reduction in daily temperatures), while cool walls performed better in reducing cooling energy consumption, with an overall saving of 6% compared to the current scenario.

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.

Assessing effects of future climate change on outdoor thermal stress and building energy performance in severe cold region of China

Future climate change is poised to affect the urban thermal environment and building energy performance in the severe cold region, yet its full impact remains unclear, particularly concerning extreme weather events. This study proposes a multi-scale climate-energy framework to assess the future climate's influence on outdoor thermal stress and building energy efficiency under Shared Socioeconomic Pathways for 2050 and 2080 in this region, using Harbin serving as a case study. The framework also evaluates the adaptation potential of trees and natural ventilation. The results showed that climate change may narrow the physiologically equivalent temperature (PET) range in summer daytime, and increase unacceptable heat exposure duration from 2 h to 7–8 h by 2050 and further to 5–10 h by 2080. While the overall annual building energy demands are expected to increase minimally (<12 %), cooling loads could rise substantially. Trees show diminishing returns in reducing peak PET as climate severity increases, yet can moderately reduce total energy demand (4–6 %) and peak demand (4–5 %) during extreme warm days. The energy-saving potential of natural ventilation is projected to decline, with a potential decrease of up to 40 % by 2080 in the most severe scenario. This study provides new insights into how future climate change could impact outdoor comfort and building energy in the severe cold region, and offers a versatile framework for urban sustainability planning that can be applied to other cities and climates.

The Synergistic Relationship between Urban Building Energy Performance, Microclimate, and Urban Elements

The Synergistic Relationship between Urban Building Energy Performance, Microclimate, and Urban Elements

Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach

Stakeholders such as urban planners and energy policymakers use building energy performance modeling and analysis to develop strategic sustainable energy plans with the aim of reducing energy consumption and emissions from the built environment. However, inconsistent energy data and the lack of scalable building models create a gap between building energy modeling and traditional planning practices. An alternative approach is to conduct a large-scale energy usage survey, which is time-consuming. Similarly, existing studies rely on traditional machine learning or statistical approaches for calculating large-scale energy performance. This paper proposes a solution that employs a data-driven machine learning approach to predict the energy performance of urban residential buildings, using both ensemble-based machine learning and end-use demand segregation methods. The proposed methodology consists of five steps: data collection, archetype development, physics-based parametric modeling, machine learning modeling, and urban building energy performance analysis. The devised methodology is tested on the Irish residential building stock and generates a synthetic building dataset of one million buildings through the parametric modeling of 19 identified vital variables for four residential building archetypes. As a part of the machine learning modeling process, the study implemented an end-use demand segregation method, including heating, lighting, equipment, photovoltaic, and hot water, to predict the energy performance of buildings at an urban scale. Furthermore, the model's performance is enhanced by employing an ensemble-based machine learning approach, achieving 91% accuracy compared to the traditional approach's 76%. Accurate prediction of building energy performance enables stakeholders, including energy policymakers and urban planners, to make informed decisions when planning large-scale retrofit measures.

Performance and prospects of urban energy communities conditioned by the built form and function: A systematic investigation using agent-based modeling

Studies on energy communities, as a means to help achieve sustainability goals such as self-sufficiency, rarely consider the influence of urban context (built form and function) on their performance. This work thus studies urban energy performance under the community and individual configuration of prosumer buildings by expanding an agent-based modeling framework, which couples built context with the energy demand and supply of an urban area, and conducting a comprehensive case study in two climates. The performance aspects covered include energy, economics, and grid impact. The case study investigates the performance of compact and open high-, mid-, and low-rise areas that have different land area-wise proportions of common commercial and residential buildings acting as a community and individually. Results show that the self-sufficiency of mixed-use high-rise and mid-rise energy communities remained below 15 % and 30 % respectively. Unlike high-rise areas, mid-rise areas were unable to achieve 100 % self-consumption through the community mode. In contrast, the mixed-use low-rise energy communities achieved almost 45 % average self-sufficiency in some cases but remained far short of reaching their full self-sufficiency potential as their average self-consumption did not exceed 80 %. The findings are vital for developing context-specific plans and policies concerning energy communities and urban sustainability.

Built form and function as determinants of urban energy performance: An integrated agent-based modeling approach and case study

The pursuit of urban energy sustainability is driving growing efforts and pledges to achieve low-energy or net-zero energy performance at urban scales. However, studies covering urban energy performance and renewables integration often lack a systematic consideration of the urban built context (form and function) and its resulting effects on energy demand and supply dynamics. This paper presents a holistic and scalable agent-based modeling framework that incorporates contextual factors in the energy demand and supply evaluation of urban areas using multi-dimensional performance metrics. It leverages the local climate zone (LCZ) classification of typical urban built types, which is commonly used in urban planning or transportation applications (as opposed to energy planning). The framework is demonstrated through a comparative case study of compact high-rise and low-rise urban areas with different proportions of lodging and office buildings equipped with rooftop solar photovoltaic (PV) systems. Results show that the average energy self-sufficiency of the compact low-rise area exceeded 25%. In contrast, the self-sufficiency of the compact high-rise area remained below 5% despite achieving a self-consumption ratio of 99% from locally generated solar energy. The uncovered trends are critical to inform context-sensitive urban energy solutions and policies.

DeepRadiation: An intelligent augmented reality platform for predicting urban energy performance just through 360 panoramic streetscape images utilizing various deep learning models

DeepRadiation: An intelligent augmented reality platform for predicting urban energy performance just through 360 panoramic streetscape images utilizing various deep learning models

Leveraging the Opportunities of Wind for Cities through Urban Planning and Design: A PRISMA Review

Wind has been utilized for passive ventilation and mechanical power since antiquity. As an abundant renewable resource, today, wind is increasingly seen as a critical resource to help tackle issues associated with rapid urbanization and climate adaptation and mitigation, such as improving thermal comfort, providing clean energy, improving air quality, and reducing carbon emissions. Despite the growing importance of wind as an invaluable resource for cities, wind in the context of urban planning and design is a relatively understudied area of research. This study aims to explore the means by which cities that can benefit from wind and ways urban planning and design can help deliver these benefits. The study adopts a systematic literature review methodological approach. The findings disclosed that: (a) improving urban wind environment via sound urban planning and design may enhance urban ventilation and energy performance; (b) better urban ventilation and energy performance enable cities to become climate positive or net zero and relieve the urgent climate crisis; (c) wind sensitive urban design is an emerging research area critical to harvest the benefits of wind for cities. This study offers a novel conceptual framework and research directions for wind sensitive urban design and informs urban planning, design policy and practices.

Assessment of Integrated Solutions for the Combined Energy Efficiency Improvement and Seismic Strengthening of Existing URM Buildings

The European building stock is an aging infrastructure, mainly built prior to building codes. Furthermore, 65% of these buildings are located in seismic regions, which need to be both energetic and seismically retrofitted to comply with performance targets. Given this, this manuscript presents integrated constructive solutions that combine both energy efficiency improvement and seismic strengthening. The goal and novelty is to design and to evaluate one-shot, compatible, noninvasive, and complementary solutions applied to the façades of buildings with a minimum cost. To do so, different constraints have been borne in mind: the urban environment, achievable seismic and energy performance targets, and reduced construction costs. The method was applied to an old Spanish neighbourhood constructed in the 1960s. Different retrofitting packages were proposed for an unreinforced masonry case study building. A sensitivity analysis was performed to assess the effects of each configuration. A benefit/cost ratio was proposed to comparatively assess and to rank the solutions. The results of the seismoenergetic performance assessment showed that improving the behaviour of walls leads to higher benefit ratios than improving the openings. However, this latter strategy generates much lower construction costs. Integrating seismic into energetic retrofitting solutions supposes negligible additional costs but can improve the seismic behaviour of buildings by up to 240%. The optimal solution was the addition of higher ratios of steel grids and intermediate profiles in openings while adding thermal insulation in walls and renovating the window frames with PVC and standard 4/6/4 double glazing.

On the combined impact of local, regional, and global climatic changes on the urban energy performance and indoor thermal comfort—The energy potential of adaptation measures

The rapidly growing urban population, along with the increasing urban energy needs and greenhouse gas (GHG) emissions, poses a serious threat to the global climate. Local, regional, and global climatic conditions synergistically interact and affect urban energy performance and thermal comfort. The combined impact of such local, regional and global climatic change on the buildings' thermal performance has never been studied. In the present study, the combined impact of urban overheating (UO) and heatwaves (HWs) on building thermal performance have been investigated by employing the urban building energy model (UBEM). Further, the sensitivity analyses were performed using various adaptive measures to evaluate the impact on cooling needs and indoor comfort. Under the synergistic impact of UO and HWs, the cooling penalties were recorded up to 650%, the increase in mean indoor temperature was up to 5.8 °C, and the deterioration in passive survivability was registered up to 31%. Under the combined impact of various adaptive measures, the cooling savings were recorded up to 97%, the drop in mean indoor temperature was up to 2.34 °C, and the improvement in passive survivability was up to 20% during HWs. The present study might assist in revising the building codes to counteract the global climatic change and improve building resilience during extreme heat conditions.

Review of Urban Heat Island and Building Energy Modeling Approaches

Abstract In this paper, a review of the current literature in modeling urban heat island (UHI) phenomena including its main causes and effects is summarized. The main goal of the review is to assess the current modeling capabilities to accurately determine the impacts of UHI on outdoor comfort levels and urban building energy demands. In particular, the analysis techniques and modeling approaches are overviewed to estimate the mutual thermal interactions between urban atmosphere and buildings. In addition, the applications and the limitations of various modeling methods are discussed to predict outdoor thermal comfort and urban building energy consumption. The specific capabilities of the reviewed modeling approaches are highlighted to assess the effectiveness of mitigation strategies of the UHI effects. As part of the review analysis, recommendations are outlined to improve current modeling approaches to predict more accurately the impacts of UHI phenomena on urban building energy performance.

Concept Evolution and Multi-Dimensional Measurement Comparison of Urban Energy Performance from the Perspective of System Correlation: Empirical Analysis of 142 Prefecture Level Cities in China.

In order to clarify the evolution characteristics and direction of urban energy performance concepts, reveal the research dimensions, determine the performance results and differences, and clarify the reference benchmark, this study depicts the main systems involved in the process of urban energy utilization, demonstrates their relevance guided by the system view, and proposes the measurement indicators in the economic, environmental, and well-being dimensions. The measurement model of each dimension is constructed using the corresponding models of Data Envelopment Analysis. Taking 142 prefecture level cities in China as examples, the energy performance in different dimensions is measured and compared. The energy performance levels are close in the economic and environmental dimensions. However, the results of the well-being dimension are different from these first two dimensions, and the performance levels among cities differ more. In the economic, environmental, and well-being dimension, 22, 28 and 16 cities have reached the effective frontier, respectively, and the performance benchmark cities of 15, 15 and 5 provinces are non-provincial capital cities, respectively. Based on the above analysis, the “chain” framework evolution direction of concept and measurement is proposed, and this study provides benchmarks and policy suggestions to improve energy performance.

CityGML model generation using parametric interpolations

With an upsurge in energy demand throughout the globe, efficient energy simulations and smart energy-management solutions are imperative. Often a lack of substantial three-dimensional geometrical data restricts simulation scientists and urban planners to analyse energy demand patterns on a city scale. Therefore, in this paper, the authors describe the open-source City Geographical Markup Language (CityGML) Building Interpolation Tool (CityBIT) for the creation of user-defined and interpolated CityGML LoD2 building models for planned and/or existing buildings. CityBIT can be used to generate CityGML models with different roof types, orientations and elevations. A brief explanation of the functionalities of the tool and the methodologies adapted to develop the tool is provided in this paper. CityBIT aims to facilitate CityGML geometric model development focusing on urban energy performance simulations.

Informing the design of courtyard street blocks using solar energy models: a case study of a university campus in Singapore

This study discusses the interplays between urban form and energy performance using a case study in Singapore. We investigate educational urban quarters in the tropical climate of Singapore using simulation-based parametric geometric modelling. Three input variables of urban form were examined: street network orientation, street canyon width, and building depth. In total, 280 scenarios were generated using a quasi-Monte Carlo Saltelli sampler and Grasshopper. For each scenario, the City Energy Analyst, an open-source urban building energy simulation program, calculated solar energy penetration. To assess the variables’ importance, we applied Sobol’ sensitivity analysis. Results suggest that the street width and building depth were the most influential parameters.

Computational Approach in Investigating Surface and Site Radiation in the Early Phase of Designing Two-Story Wooden House in Orio District, Kitakyushu, Japan

ABSTRACT Along with the rise of awareness in implementing the environmentally conscious urban design, the classical process in approaching urban environmental analysis should be shifted to the performance-based considerations. In architecture and urban design, the tools developed recently holds promise to assist the decision-making process in the early phase of designing. Thus, the further performance of the intended architecture project, particularly related to energy consumption and its environmental impact, can be nearly accurately predicted. Although many studies contain the computer design process for analyzing urban energy performance, the Multi-Objective Optimization (MOO) process in the sub-tropical climate of Japan has not been widely used. This research investigates hypothetically the surface and site radiation in the early phase of designing a two-level wooden house in Orio District, Kitakyushu, Japan, through parametric and generative algorithm. The research designed to map the best possible design solution of free-form loft-twisted structure through iterating design variables related to design elements such as building orientation, base radius, twisting and scaling factor and the angle of the roof slope. The optimization targeting the minimum quantity of solar radiation both for the site and the building surface affected by the geometry. The simulation uses an EPW file of Shimonoseki as the input weather file and being scheduled for fitting the period in the extreme hot week during the summertime. The findings are successful to produce a design with better performance compared to the benchmark model even though with insignificant improvement.

Exploring the Connection between Urban 3D Form and Building Energy Performance and the Influencing Mechanism

Continuous growth of building energy consumption CO2 emission (BECCE) threatens urban sustainable development. Urban form is an important factor affecting BECCE. Compactness is a significant urban morphological characteristic. There is currently a lack of research on the effect of urban three-dimensional (3D) compactness on BECCE. To clarify the research value of 3D compactness, we investigated whether 3D compactness has a stronger impact on BECCE than two-dimensional (2D) compactness. A total of 288 buildings of the People’s Bank of China (PBOC) were divided into 5 zones according to building climate demarcation. As BECCE is affected mainly by four aspects (socioeconomic condition, building features, macroclimate, and urban form), the BECCE driven by urban form (BECCE-f) in each zone was calculated firstly using the partial least square regression model. Normalized compactness index (NCI) and normalized vertical compactness index (NVCI) were calculated with Python to quantify urban 2D and 3D compactness within a 1 km buffer of PBOC buildings. The mean NCI and NVCI values of each zone were adopted as 2D and 3D compactness of this zone. Gray correlation analysis of the five zones showed that the connection between the NVCI and BECCE-f is stronger than that between NCI and BECCE-f. Based on this, we believe that the emphasis of later research should be shifted to urban 3D form, not just 2D elements. 3D form can describe the real urban form in a more accurate and detailed manner. Emphasizing 3D morphological characteristics in studies of the relationship between urban form and building energy performance is more meaningful and valuable than only considering 2D characteristics. The impact mechanism of urban form on BECCE-f should also be analyzed from the perspective of 3D form. This study also provides beneficial solutions to building energy saving and low-carbon building construction.

Effect of urban form on microclimate and energy loads: Case study of generic residential district prototypes in Nanjing, China

To conduct a more comprehensive evaluation regarding the correlations between urban form, microclimate and energy performance of buildings, this study investigates eight generic residential district prototypes in Nanjing, China. Cases are characterized based on form indices such as the floor area ratio (FAR), surface area ratio (SAR) and mean sky view factor (SVF). A co-simulation method combining ENVI-met and EnergyPlus is proposed to simulate the microclimate, represented by average conditions around the exterior surfaces of all buildings in the studied district, and building energy loads during typical summer and winter days. The results show that, generally, air temperature and direct shortwave radiation are negatively correlated with the SAR. The cooling and heating loads are negatively correlated with the FAR and SAR, and positively correlated with the mean SVF. The differences in energy loads can reach 23.4 % with obvious deviation of the regression analyses of hourly heating loads and air temperature if neglecting the microclimate effect.

Context-specific urban occupancy modeling using location-based services data

Energy-related occupant behavior is a major source of uncertainty in building and urban energy performance simulations. Standardized assumptions, published by ASHRAE and others in the form of occupancy schedules, are widely used in research and practice, especially on the district-scale. In this work, we gathered location-based services data to create context-specific, data-driven occupancy schedules. Using a web mapping service, we collected data for retail and restaurant uses in the downtown neighborhoods of 13 different U.S. cities to create data-driven schedules for each context. The schedules were compared to ASHRAE standard assumptions using the earth mover's distance approach and the schedules' energy-related features. We found that standard schedules seem to significantly overestimate weekly building occupancy, although the shapes of the schedules are generally similar. The use of standard schedules could therefore, have significant impacts on district-scale energy demand simulations, as the overestimation will be cumulative.As compared to the differences between data-driven and standard schedules, the differences between different locations are significantly smaller. However in extreme cases, the weekly cumulative occupancy and the number of occupied hours differ by more than 30% between locations, which means that context-specific differences together with climatic differences might also impact building performance simulation results. Furthermore, we found differences in daily data between the different days of the week. In particular, the observed behavior on Fridays is significantly different from other weekdays for both considered use-types. This indicates that the conventional categorization of occupant behavior models into three day-types: weekday, Saturday, and Sunday, should be reconsidered.

Synergetic urban microclimate and energy simulation parametric workflow

Although the interrelations between urban microclimates and energy demand have been acknowledged, few workflows integrate microclimatic boundary conditions to predict energy demand in parametric morphological studies. This paper helps bridge this gap by introducing a novel workflow which brings together energy and microclimatic modelling for a synergetic assessment at the block scale. The interrelation between form, energy and urban microclimatic conditions is explored here in the climatic context of Tel Aviv by coupling Envimet and EnergyPlus. The potential of this coupling is explored in three different block typologies, each tested for four different density scenarios focusing on the cooling demand on a typical hot day. Results show the substantial increase of as high as 50% in cooling demand when the microclimatic weather data is taken into account and indicate the potential to capitalize on new computational tools which allow to quantify the interrelations between urban form, microclimate and energy performance more accurately.