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Abstract

The urban context is often simplified or neglected in Building Energy Models (BEMs) due to the difficulties of taking accurately into account all the heat fluxes emanating from the environment. Oversimplifying the urban context can impact the accuracy of the BEM predictions. Nevertheless, several approaches can be used to allow for the impact of the urban environment on the dynamic behavior of a building, its heating and cooling demands, and thermal comfort. This state of the art review provides a critical overview of the different methods currently used to take into account the urban microclimate in building design simulations. First, both the microclimate and building models are presented, focusing on their assumptions and capabilities. Second, a few examples of coupling, performed between both modeling scales are analyzed. Last, the discussion highlights the differences obtained between simulations that take the urban context into consideration and those that simplify or neglect urban heat fluxes. The remaining scientific obstacles to a more effective consideration of the urban context impacting the BEMs are indicated.

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... The UCM approach employs the same type of models used in numerical weather prediction to parameterize subgrid-scale urban-atmosphere interactions [55]. Urban forms including building forms, materials, and natural surfaces can be taken into account in detail or by averaging parameters (roughness, albedo, etc.) [115]. UCMs can be further subdivided into the single-layer canopy model and the multi-layer canopy model depending on the degree of simplification of the buildings [116]. ...
... The majority of widely used BEMs include shading modules that simulate the direct and diffuse parts of solar radiation to account for the shading of adjacent structures [115]. EnergyPlus calculates the shading on a building's surface at each time step. ...
... Solar radiation fluxes received by the external envelope components of a building (such as exterior walls and windows) are partially absorbed and partially transmitted into the interior. By solar tracking or weighting methods, the radiant flux transmitted to the interior is distributed over the interior building surfaces [115]. However, these shading algorithms are only used to calculate building shading. ...
Article
In order to address the increased urban heat island (UHI) effects and energy demand caused by global urbanization, it is imperative to seek sustainable urban design solutions. It is widely acknowledged that urban green infrastructure (UGI), which includes site-scale vegetation and building-integrated vegetation, influences the energy consumption of urban buildings. In the planning and design phases of UGI, numerical simulations are essential tools for evaluating and optimizing design strategies. However, the methodology for the simulation at various scales is still unclear, necessitating a comprehensive review of relevant studies. This review examined the research conducted on UGI modeling in numerical simulations of building energy consumption over the past 35 years and outlined the general workflow of these simulations. The numerical methods and tools for each step, as well as the coupling and validation methods for these tools, were described in detail. Thus, this study equips researchers with the knowledge necessary to analyze the impact of UGI on the energy consumption of buildings using numerical simulations. According to the review, existing building energy model (BEM) tools have not yet integrated modeling of site-scale vegetation for microclimate and shading. Future collaboration between urban climatologists and building physicists should be encouraged to improve the integration of climate and UGI shading simulations with BEM in order to simplify the use of numerical simulation tools.
... The UCM approach employs the same type of models used in numerical weather prediction to parameterize subgrid-scale urban-atmosphere interactions [55]. Urban forms including building forms, materials, and natural surfaces can be taken into account in detail or by averaging parameters (roughness, albedo, etc.) [115]. UCMs can be further subdivided into the single-layer canopy model and the multi-layer canopy model depending on the degree of simplification of the buildings [116]. ...
... The majority of widely used BEMs include shading modules that simulate the direct and diffuse parts of solar radiation to account for the shading of adjacent structures [115]. EnergyPlus calculates the shading on a building's surface at each time step. ...
... Solar radiation fluxes received by the external envelope components of a building (such as exterior walls and windows) are partially absorbed and partially transmitted into the interior. By solar tracking or weighting methods, the radiant flux transmitted to the interior is distributed over the interior building surfaces [115]. However, these shading algorithms are only used to calculate building shading. ...
Article
Smart metering and the internet of things (IoT) accumulate massive time-series building data. Mining operation patterns from time-series data have huge potential to provide extra information for improving energy efficiency. Previous studies mainly focused on mining control patterns of the heating, ventilation, and air-conditioning system (HVAC). Mining operational patterns from time-series data can help building managers identify energy-wasting operations. This study proposes three methods of mining the operation hours on the time-series data of hundreds of buildings. A key performance indicator (KPI) of facility hours is proposed to indicate the discrepancy between occupants’ requirements and facility hours. The study is carried out on the 240 office buildings of building data genome project 2 (BDG2). The proposed methods are evaluated by comparing mined hours with annotated hours. The impacts of eight operational KPIs are quantified with correlation coefficients and ensemble learning. The practicality of the proposed methods is evaluated on three case buildings. The results show that the cumulative histogram method is effective in mining operation hours. The regression results indicate that mined KPIs can improve the energy prediction accuracy (R²) from 0.35 to 0.41. The impacts of operational KPIs reveal that the KPI of weekends has a tremendous impact on energy consumption. The case study results show that reducing unnecessary facility hours can save from 2.9% to 10.6% energy. This study verifies that operational KPIs mined from time-series data can provide building managers with intuitive knowledge for improving operations.
... However, none of these building thermal models are as detailed as EnergyPlus or TrnSys, and none can accurately predict the indoor comfort conditions within passive buildings. Thus, for the purpose of the present paper, DSMs are applied to refine the thermal loads on a single building of interest ( [24] Lauzet et al., 2019). ...
... Lauzet et al. (2019) [24] provided a broad overview of existing tool chains for the simulation of the thermal behavior of urban buildings. The inventoried tool chains could be classified into two categories: tool chains involving an MMM for the simulation of district-scale effects and tool chains without an MMM. ...
... The pressure coefficients provided by UrbaWind are computed with a wind velocity value obtained at a fixed height (usually, the building height is chosen) along the urban air velocity profile ( [24] Fahssis et al., 2010). However, the EnergyPlus AFN model considers the velocity value at the local opening height to compute pressure loads based on the above pressure coefficients. ...
Article
This paper presents a simulation tool chain for the prediction of thermal comfort in passive urban buildings during summer and under heat wave conditions. The tool chain encompasses EnergyPlus building energy model and the Urban Weather Generator and UrbaWind tools to consider the impacts of the urban environment on building loads. This chain of tools is computationally efficient and does not require notable expertise for the simulations. To assess its accuracy, this simulation results are compared to in situ measurements. This paper describes the measurement setup, analyzes the measurement results and reveals a satisfactory model accuracy through a comparison to the measurement data. The average nighttime urban heat island between July and September 2020 reached 2.31 °C in the city of Lyon. Not considering the urban heat island effect (employing rural weather files) in urban thermal simulations could induce a 1 °C bias in indoor air temperature predictions. This could also result in overpredicting the cooling potential of natural ventilation during summer. Key parameters of the simulation accuracy are identified. These are the action schedule of occupants in regard to opening devices (shutters, windows and doors) and the urban boundary layer height at night.
... In urban areas with cooling demand, the Q fs -T-E PFB forms selfreinforcing loops (Fig. 2), causing adverse effects on UHI mitigation and energy conservation, though urban warming favorably reduces the building energy demand for heating and Q fs in winter. Focusing on the adverse effects, a considerable number of studies [7,8,[12][13][14][15][16][17][18][19][20][21][22] have qualitatively mentioned the Q fs -T-E PFB, as referred to as "classic feedback loop" in an International Energy Agency (IEA) report [23]. Particularly in developing nations, many of which lie in hot climate zones with three-quarters of the world's urban population [1], yearlong urban energy demand for cooling could enhance the adverse effects of the PFB [24,25]. ...
... Specifically, two processes of ΔT → ΔE and ΔQ fs → ΔT have been intensively investigated. Regarding the former, studies have evaluated the magnitude of urban and global warming impacts on E in urban areas [21,26,27], whereas estimates have been made on the latter to quantify the Q fs impact on T through observations and simulations. The observational studies have frequently focused on UHI intensity as T difference between urban and non-urban reference sites, and its statistical increase from weekends to weekdays have been analyzed and thought to represent T increment caused by Q fs gain in the urban area on weekdays due to increased business activities compared to weekends [28][29][30]. ...
... If an urban climate model can reproduce the observational ΔE' wd-we , its applicability would be verified to the quantification of the Q fs -T-E PFB effects. For the validation, the authors selected an UCM-BEM for the following two reasons: (1) UCM-BEMs explicitly consider the Q fs -T-E PFB [21]; (2) comparable performance is shown by UCM-BEMs in simulating buildings' AC energy consumption when compared to detailed BEMs like EnergyPlus [43]. Among UCM-BEMs [12,15,20,[35][36][37][38], this study employs the first developed one named CM-BEM [12,35] with a minor improvement described later. ...
Article
Full-text available
The interaction between urban air temperature (T) and building cooling energy demand (E) generates a well-known positive feedback (PFB), which is mediated by sensible anthropogenic heat (Qfs) and named Qfs-T-E PFB in this study. This PFB could induce self-reinforced warming in urban areas, but its effects have not been completely quantified. Hence, this study aimed to clarify these effects by targeting Osaka, a Japanese major city. Focusing on the from-weekends-to-weekdays increase in urban energy consumption including E increase as an observable trigger of the PFB, its induced T rise due to growth in Qfs was estimated with the fed-back additional E gain on weekdays based on observed ground-level T and district-wise electric power consumption during summer. The result indicated that the weekdays–weekends contrast in energy consumption over Osaka could induce the Qfs-T-E PFB effects, which resulted in fed-back E gain reaching 10% on weekdays. Such observational PFB impact on E was found to be roughly reproducible by the proposed urban meteorological model, named WRF-CM-BEM. Thus, the validated model was applied to the quantification of the climatological PFB impact on T based on feedback gain (gA ) which means a percentage of T variation caused by the PFB. An attempt was made to quantify gA through the two-cases simulations of the weekdays-run and holidays-run for the months of August in 10 years, focusing again on the weekdays–weekends contrast in urban energy consumption. The simulations provided estimates on gA, whose daytime averages reached nearly 10% in the downtown commercial areas and 20% in the leeward-located residential areas, suggesting the influence of sea breeze heat advection of downtown Qfs. Such estimated impacts on T were roughly in the same order of magnitude compared to those in a few earlier studies that were not based on observational validations and seemed to be non-negligible, considering the feedback impacts on global surface warming estimated with gA of approximately 50% by the Intergovernmental Panel on Climate Change.
... This scale allows for the representation of the meteorological phenomena in the urban canopy layer. Building forms, materials, natural surfaces, etc. can be considered explicitly or through mean parameters (rugosity, albedo, etc.) (Lauzet et al., 2019). At the microscale, a smaller zone of a few meters or a hundred meters is considered, in which the physical processes are computed in more detail (1 m). ...
... At the microscale, a smaller zone of a few meters or a hundred meters is considered, in which the physical processes are computed in more detail (1 m). According to the review (Lauzet et al., 2019), some common used UCMs are classified in Figure 1.10, based on the spatial scale of the represented areas (in terms of urban scale) and the horizontal resolution (the horizontal size of the mesh elements used in the model). In this thesis, the microclimate refers to the local scale or smaller. ...
... On the district scale, two categories of models are distinguished (Lauzet et al., 2019). The first category brings together models that do not represent the urban shapes explicitly but use parameters Chapter 1. Introduction that translate their impact, e.g. ...
Thesis
The urbanisation process in China brings a high pressure on the environment. The highest potential to reduce these impacts corresponds to decisions made during the building’s design phase, which can be supported by numerical simulation. This thesis is dedicated to the study of three boundary conditions related to the energy and environmental performance evaluation of buildings in China: - The ground: a ground coupled heat pump model is proposed integrating a fast calculation ground heat exchanger model for a large-scale boreholes field. This model can be used to improve the energy performance of the system in the design and operation phases. - The microclimate: a site-specific weather file generation method which can provide local hourly air temperature is proposed, accounting for the urban heat island effect. The effects of the microclimate on the building’s energy performance are quantitatively investigated. - The background system for life cycle assessment: the effects of the spatial and temporal variation of the electricity production mix in China on the environmental impacts are investigated. The environmental database is adapted to the Chinese national and local context. The results show that the environmental impacts of buildings could be more reasonably evaluated by considering these three boundary conditions.
... En outre, ils ont pour but d'évaluer les bénéfices que pourraient apporter les différents scénarios architecturaux et urbanistiques, en termes de climat et de confort thermique [121]. Il existe dans la littérature plusieurs modèles qui ont été développés [66,[148][149][150][151]. Dans les paragraphes suivants, une sélection de certains modèles est présentée. ...
... Ce modèle utilise des paramètres d'entrée qui décrivent la morphologie urbaine, la géométrie et les matériaux de surface. Il est composé de quatre modèles couplés [151] (Figure I-26): le modèle de station rurale (température de forçage), le modèle de diffusion verticale, le modèle de couche limite urbaine, et la modélisation de la couverture urbaine et la modélisation énergétique des bâtiments (BEM). Sur la base de cette chaîne de modèles, les températures rurales peuvent être traduites en températures urbanisées pour un site urbain spécifique [151]. ...
... Il est composé de quatre modèles couplés [151] (Figure I-26): le modèle de station rurale (température de forçage), le modèle de diffusion verticale, le modèle de couche limite urbaine, et la modélisation de la couverture urbaine et la modélisation énergétique des bâtiments (BEM). Sur la base de cette chaîne de modèles, les températures rurales peuvent être traduites en températures urbanisées pour un site urbain spécifique [151]. Elle peut être étendue pour des simulations à l'échelle de la ville. ...
Thesis
Face aux défis énergétiques auxquels le monde est aujourd'hui confronté, la conception énergétique des bâtiments est l'un des enjeux majeurs. Le véritable défi des années à venir est sans aucun doute la réduction des besoins en énergie fossile primaire et de l'empreinte carbone dont le secteur du bâtiment est responsable d’une grande partie. Afin de garantir les performances énergétiques et apporter des solutions techniques adéquates, la prédiction de la consommation énergétique doit être de plus en plus réaliste. En l’occurrence, la modélisation thermique des bâtiments doit tenir compte des conditions intérieures et extérieures de ses environnements proches. Ce travail de recherche porte donc sur le développement d’un modèle de simulation du microclimat urbain dans le cas des rues canyons et des cours intérieures. Ce modèle est intégré dans le logiciel de simulation thermique dynamique TRNSYS 18 pour étudier l’impact de ce microclimat sur les performances énergétiques du bâtiment. Une étude bibliographique sur les modèles microclimatiques existants dans la littérature a été réalisée. Ceci a montré que le problème majeur réside dans le temps de calcul et l’interopérabilité. En effet, un modèle nodal a été développé par le langage de programmation Python et puis intégré au logiciel TRNSYS 18 pour étudier l’impact du microclimat dans le cas des rues canyons sur les performances énergétiques des bâtiments. Les résultats montrent une cohérence satisfaisante du modèle développé par rapport à des études expérimentales qui existent dans la littérature. Ce modèle a ensuite été utilisé pour effectuer une étude paramétrique dans le cas de cette typologie de rue et pour quantifier ainsi l’impact sur les besoins énergétiques de chauffage et de refroidissement des bâtiments étudiés. A l’instar du modèle précédent, un modèle zonal a été aussi développé et il porte sur la modélisation des cours intérieures dans le cas des bâtiments types « Riad ». Ce modèle a été comparé avec des simulations CFD par le logiciel ENVI-met. Les résultats obtenus ont été très pertinents. Ensuite une étude a été réalisée sur l’impact de la forme des cours intérieures et des paramètres microclimatiques sur les besoins énergétiques de ces bâtiments. En fin, un quartier type Canyon avec des bâtiments comprenant des cours intérieures situé à la ville de Tanger (Maroc) a fait l’objet d’un cas d’étude par l’application de l’ensemble des modèles développés. Les principaux résultats de ce travail de recherche dévoilent clairement la nécessité de la prise en compte du microclimat urbain pour une meilleure estimation des performances énergétiques des bâtiments. Les modèles développés et intégrés dans le logiciel TRNSYS 18 présentent donc un compromis intéressant par rapport aux logiciels existants. Il a été aussi démontré qu’il y a une forte interaction entre la conception énergétique urbaine et l’enveloppe des bâtiments. Le choix des paramètres microclimatiques peut être déterminant sur le comportement hygro-thermo-aéraulique des bâtiments. Ces modèles développés restent à la portée des ingénieurs concepteurs et des chercheurs pour les implémenter facilement dans les outils de simulations thermiques dynamiques des bâtiments.
... His analysis showed that the energy penalty induced by urban overheating varies between 0.1 kWh/m 2 /year to 20 kWh/m 2 /year per degree of UHI intensity (UHII) per city surface (Santamouris, 2020). In similar areas of analysis, other reviews have been developed, although they mainly refer to tools or methodologies for energy evaluation in urban buildings (Bourdic and Salat, 2012;Allegrini et al., 2015;Lauzet et al., 2019;Ali et al., 2021;Wong et al., 2021). Others considered the analysis of urban buildings' life cycle and carbon emissions . ...
... Moreover, in the literature reviewed, no study was found that has evaluated the possible influence on local thermal regulations from thermo-energy comparative analysis between urban buildings and rural reference. Building thermal standards are usually based on meteorological data taken in areas outside urban centers, such as airports, which can generate uncertainty in the proposal of thermal efficiency strategies (Lauzet et al., 2019;United Nations, 2015b;CEPAL, 2016;Mauree et al., 2019). In this sense, the use of the penalty index could be useful in the segmentation of the expected performance of certain building uses in different sectors of the same city, which can help improve local technical codes according to the impact on performance promoted by the UHI. ...
Article
Cities occupy 3% of the surface of the planet, but account for 60–80% of energy consumption and 75% of carbon emissions. Likewise, buildings consume 35% of the energy and emit 38% of global greenhouse gases. Cities can aggravate such problems further by generating the phenomenon of urban heat islands (UHI). Few studies have evaluated the state of the art in UHIs' influence on buildings' energy performance, so the present research aims to analyze the main studies evaluating the thermo-energy behavior of buildings subjected to UHI. This was done with a systematic literature review and a scientific mapping of the publications present in Web of Science until 2021. 100 articles were selected for analysis in this review. The results point to an important evolution in the study of parameters affecting urban buildings’ performance, in addition to the analysis of different urban land uses as a strategy to sectorize UHI. Such scientific evolution is analyzed and discussed in four-time segments. Fragmentation on building cooling loads and the energy penalty index derived from UHI are discussed, resulting in an average cooling penalty of 6.63 kWh/m²/y/°C and 3.81 kWh/m²/y/°C, for residential and office use respectively. This study presents limitations and proposes applications for the use of this index, as well as prospects for future studies and main research gaps in the area.
... Additionally, the complexity of the urban planning process and the large number of experts and stake holders involved in any development project make it extremely difficult for the climatologist to make a meaningful contribution to the design [45,46]. The localized climate data for building energy simulation could in theory be provided by modelling the urban climate, and there has in fact been progress in this field, summarized in a thorough review by Lauzet et al. [47]. However, despite this progress, Mills' observation [48] that in practice, these are separate disciplines, pursued by different communities with little interaction, remains largely true. ...
... The emphasis here, in contrast, is to improve the building energy model by providing urbanized climate data that incorporates effects such as the urban heat island, which are then used as inputs to EnergyPlus, which is a software tool developed to perform accurate building energy simulations. The only tools used for annual simulations of the urban climate for use in building energy models [47] are the parametric tools UWG, CAT and CIM, though CIM [57] is a large-scale model and its meshing is not suitable for analyzing the impact of local adaptation strategies. Parametric energy balance models are best suited for generation of urbanized weather data for building energy simulation since they can generate hourly data for an entire year. ...
Article
The study examines the potential effects of adding vegetation to an urban neighborhood in Tel Aviv on the microclimate, and subsequently on the potential for cooling by night ventilation and on energy consumption for heating and air conditioning. Computer simulation was employed to first generate modified weather files that account for urban effects of location, surface cover and density in different building configurations. These files were then used to assess the climatic cooling potential (CCP) by night ventilation and as inputs for detailed computer simulation of building energy performance. The microclimate model simulation indicates that elevated urban night-time temperatures will increase summer cooling loads relative to the reference rural site, but this penalty will be more than offset by reduced winter heating loads, resulting in a net decrease of between 2 and 7% in electricity use for heating and cooling (depending on building characteristics). The main impact of the urban heat island in this case is the reduction in the potential for cooling by night ventilation, which is almost completely absent in the summer months. Consequently, the urban climate of Tel Aviv may increase the prevalence of air conditioning use and will make buildings more vulnerable to potential loss of electric power in case of shortages or blackouts during episodes of extreme heat. Implementing a strategy of extensive planting, so that a green surface fraction of 0.5 is obtained, results in a mean annual temperature reduction of about 0.3 °C and an energy saving relative to the current condition of about 2–3%.
... These studies have used different indicators: AT and TLS. In this context, microclimate models such as ENVI-met, FLUENT-ANSYS and TEB model have been widely applied to examine the positive impact of various urban green infrastructures on the outdoor thermal environment (Lauzet et al., 2019). ...
... However, it is difficult to have a good parameterization, as for example SOLENE-Microclimat requires leaf area index (LAI) and water availability, as well as soil/buildings characteristics. A major limitation of ENVI-met is that inter-reflections are not accounted for in the calculated shortwave radiation and longwave radiation takes into account averaged temperatures so rendering difficult to assess the local impact of surface temperature change (Lauzet et al., 2019). The main disadvantage is stability issues when simulating winding urban canyons or abutting neighbourhoods (Elwy et al., 2018). ...
Article
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In the context of climate change, Nature-Based Solutions (NBSs), a recently developed concept, are increasingly considered as part of the adaptation strategies of the cities. Studies using expert models and methods (EMM) receive a great deal of scientific attention. Considering EMM increasing use, this study aims to perform an analysis of the reported evaluation results, reflecting the capability of the EMM to accurately tackle urban challenges identified within the EU Nature4Cities project. Then, we propose a set of indicators and recommendations about sixteen EMM to be used by funders, researchers and practitioners when evaluating the performance of NBSs. The coupling of the different components (climate, water and soil) is not a simple matter. The analysis relies on the definition of the range of the reported metrics and on the investigation of the relationship between the various indices, applied for the EMM evaluation. Secondly, the study assesses the existing EMM, indicating the potential of NBSs: (i) to reduce urban heat island, (ii) to limit surface warming, (iii) to increase the thermal comfort of people, (iv) to limit the overheating and runoff of surfaces due to impervious areas, (v) to increase water retention during stormy episodes, (vi) to improve storm water quality at the outlet of the sustainable urban drainage systems, (vii) to promote the filtration and epuration of storm water runoff in soil and (viii) to be a support for vegetation. The analysis reveals that EMM can be considered as helpful tools for urban microclimate, urban soil and water management analysis, provided their limitations and characteristics are taken into account by the user when choosing tools and interpreting results (e.g. application scale). With regard to the performance of NBSs, the most commonly used indicators clearly depend on the scale of the project.
... Many scholars have studied the influence of meteorological factors on building load. Lauzet's [7] research shows that, without considering the urban context and climate, 15-89% of heating energy consumption is ignored, and 131-200% of cooling energy consumption is ignored. Jian Hang [8] used scale experiments to compare surface temperature, air temperature, albedo, and SEB components in deep and shallow valleys under clear, partially cloudy, and cloudy sky conditions in a humid subtropical climate. ...
Article
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An accurate calculation of sol-air temperature (Tsol) is very important for urban environments and building energy consumption. There are various methods that can be used to calculate Tsol by considering sky radiation effects. Climate conditions are vital factors affecting sky temperature (Tsky). In this paper, in order to select an appropriate calculation method to determine long-wave radiation, a theoretical analysis was carried out based on the effect of Tsky on the thermal gain of building envelopes due to long-wave radiation. Typical annual meteorological data were selected to calculate Tsol for 10 meteorological stations covering five building thermal zones in China. The application of the Tsol model was studied using MBE as the measurement standard, and a linear regression equation for the calorific value of the envelope obtained via the Tsky estimation method and the Tsky dynamic calculation method was established. The results show that relative humidity is the key meteorological factor that affects the application of the Tsol model and that the Tsky dynamic calculation should be used to calculate long-wave radiation in regions with low relative humidity. A thermal correction equation for buildings was obtained for use in areas lacking meteorological data and to provide a basis for sustainable building design.
... We can improve the urban microclimate environment through gardening and municipal engineering. Thus, it can achieve a reasonable density of physical space for the urban buildings (Cheung et al. 2021;Lauzet et al., 2019) while specifying the height of the buildings. It can carry out effective photosynthesis for vegetation, and cities can connect the associated water system. ...
Article
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Vegetation cover significantly improves the terrestrial environment by increasing carbon sequestration capacity. It is projected that a major threat to China's terrestrial environment will be happened by 2030 due to the increment in carbon emissions. Identifying reliable techniques to assess carbon absorption by green coverage is necessary to build a resilient environment. This research examines the performance of two weighted regression models to explain the capacity of vegetation carbon sequestration (VCS), spatial distribution, and degree of influence of vegetation coverage for reducing carbon emission. The results demonstrate changes in the VCS capacity from slow to fast, with an average yearly growth rate of 0.043% (2005–2010) to 1.963% (2010–2015) and more obvious growth in local cities. Variables such as the night-time light index, average relative humidity, and length of sunlight substantially impacted VCS capacity, although their effect varied yearly. Finally, the comparative results show that This study can play an influential role in finding specific locations facing issues with carbon emissions and can support local governments through the association of effective measures to mitigate it.
... At the city scale, there is a need to consider urban climate phenomena and the trade-off between spatial characteristics and energy demand for cooling and heating [26]. Chain and coupling methods between urban climate and energy models have been tested at different scales [27] and can also be used for more accurate estimation of the impacts of extreme climate events and temperature change on energy demand of buildings [28]. However, the main limitation concerns the computational costs of microclimate modelling tools, that do not allow an annual simulation on an hourly data resolution [29]. ...
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In order to meet greenhouse gas reduction goals, cities need to develop robust energy transition strategies relying both on the local capacity of combining social, economic and environmental perspectives in the decision-making process and on the collaboration between different actors to achieve knowledge and data integration. Scenarios are well-established methodological instruments to guide decisions in energy and spatial planning and have been employed to compare possible future pathways and envision the consequences of implementing decarbonization measures. However, qualitative and quantitative scenarios approaches are often disconnected. With the primary goal of supporting the implementation of the energy plan, this study develops for the City of Gothenburg a participatory method to support the alignment of qualitative and quantitative scenarios approaches. Decarbonization actions and drivers of change were discussed and prioritized in workshop sessions with representatives from the energy supplier(s), municipal administrations (city planners, environmental department), and researchers to develop relevant qualitative scenarios descriptions. Based on this, a list of requirements for quantitative scenarios analysis is developed to be, in a next step, translated and integrated into urban building energy models. Findings indicate the importance of early knowledge integration from different fields and highlight the lines of advancement in urban energy modelling to facilitate decision-making towards successful implementation of decarbonization targets.
... Although urban context design and building energy performance are interrelated and co-dependent, and despite the availability of numerous BES tools, there is no single tool which can directly evaluate the influence of an actual urban context and its microclimate on a building's energy performance, for the reasons of micro-scale variability noted above. This was concluded by Lauzet et al. [27] detailed literature review on how local climate affects building energy models in urban settings, noting that exchanging boundaries between BES and urban micro-climate models requires to be generalized to improve the accuracy of building simulations. Due to this lack of integration of climatic aspects in the planning and design process, there is an urgent need for interdisciplinary collaboration between urban planners, building designers, and urban simulation experts [28][29][30]. ...
Article
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The relationship between outdoor microclimate and indoor building conditions requires the input of hourly weather data on the typical meteorological characteristics of the specific location. These data, known as typical meteorological year (TMY), are mainly deduced from the multi-year records of meteorological stations outside urban centres, preventing the actual complex interactions between solar radiation, wind speed, and high urban density. These factors create the urban heat island effect and higher ambient air temperatures, skewing the assumptions for energy demand in buildings. This paper presents a computational method for assessing the effect of the urban climate in the generation of typical weather data for dynamic energy calculations. As such, the paper discusses an evaluation method of pairing ENVI-met 4 microclimate and IES-VE building energy modelling software to produce a typical urban specific weather dataset (USWDs) that reflects the actual microclimatic conditions. The ENVI-met results for the outdoor microclimate conditions were employed to determine the thermal boundaries for the IES-VE, and then used to compute the building’s energy consumption. The energy modelling that employed the USWDs achieved better performance compared to the TMY, as the former had just a 6% variation from the actual electricity consumption of the building compared to 15% for the latter.
... The scope of energy consumption is highly complex, and all climate changes determined by the urban environment should be considered when performing simulations. For this reason, some coupling techniques between the building energy model and microclimate CFD model have been proposed (22,23). However, this considers too many measured data (such as climate, wind speed, temperature, building texture, and thermal radiation), which require longer calculation times and higher costs. ...
Article
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Urban heat islands (UHIs) and their energy consumption are topics of widespread concern. This study used remote sensing images and building and meteorological data as parameters, with reference to Oke's local climate zone (LCZ), to divide urban areas according to the height and density of buildings and land cover types. While analyzing the heat island intensity, the neural network training method was used to obtain temperature data with good temporal as well as spatial resolution. Combining degree-days with the division of LCZs, a more accurate distribution of energy demand can be obtained by different regions. Here, the spatial distribution of buildings in Shenyang, China, and the law of land surface temperature (LST) and energy consumption of different LCZ types, which are related to building height and density, were obtained. The LST and energy consumption were found to be correlated. The highest heat island intensity, i.e., UHILCZ 4, was 8.17°C. The correlation coefficients of LST with building height and density were −0.16 and 0.24, respectively. The correlation between urban cooling energy demand and building height was −0.17, and the correlation between urban cooling energy demand and building density was 0.17. The results indicate that low- and medium-rise buildings consume more cooling energy.
... Two methods are commonly used to assess the impact of UHI on building energy use. The first uses on-site meteorological observations as input to energy simulation tools Yang et al., 2020b), and the second uses meteorological files generated by urban climate modeling codes for simulation Lauzet et al., 2019). One of the most commonly used simulation tools is the urban weather generator (UWG) (Bueno et al., 2013), a software based on EnergyPlus (U.S. ...
Article
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In coastal cities, seawater heat pumps (SWHPs) can combine heat pump technology with the availability of seawater to produce the heat and the cold necessary for heating, ventilation, and air conditioning (HVAC) systems installed in buildings. In heating mode, the seawater is used as a cold source and provides the low-temperature heat needed for the operation of the machine. In cooling mode, the seawater removes the heat dissipated by the condenser of the heat pump working for air conditioning. This seawater application seems to be very promising since the temperature trend of the seawater appears to be more favorable than the alternative use of outdoor air, both in winter and in summer. In a case study in Trieste, the performance of a district heating/cooling network supplied with seawater and based on decentralized heat pumps is investigated. For this purpose, annual dynamic simulations were performed, modeling an urban area, the heat pumps, and the network. The energy efficiency evaluation shows a clear superiority of the SWHP solution compared to boilers and airsource heat pumps and thus the possibility to provide a significant contribution to the decarbonization of buildings. Moreover, the results highlight the ability of this GWHP network to reduce the urban heat island (UHI) phenomenon since the heat dissipated by the heat pumps during summer air conditioning is removed from the urban area. Therefore, SWHPs in coastal cities can be among the mitigation measures for UHI to increase outdoor comfort and heat wave resilience in urban areas.
... Another meteorological software adopting UCMs is weather research and forecasting (WRF) model. It represents the integration of three schemes: slab model without any canopy layers, UCM developed by Kusaka et al. [41] with properties similar to TEB, and building effect parameterization (BEP), which is a multi-layered canopy model allowing interactions with planetary boundary layer [42]. The software is used for a wide range of meteorological applications, providing the output on a scale varying from 10 m to thousands of kilometres. ...
Article
The urban heat island (UHI) phenomenon is a serious concern for urban planners and policymakers, requiring effective and efficient mitigation policies. To develop such policies, accurate and pre-emptive estimations of current and future UHI manifestations are vital elements that help determine efficient policies and mitigation techniques. There are two fundamental approaches for modelling overheating in an urban environment: white-box and black-box based methods. The first one is characterized by the easily interpretable working process, while the unclear working procedure defines the second one. The present study comprehensively reviews the commonly used white-box and black-box based approaches applied for UHI predictions, analyses the existing literature adopting these tools for UHI prediction, and discusses the effectiveness of fusing both methods at the design and operation stages of the urban area for effective prediction and mitigation of UHI effect. The literature analysis showed that the transparent working process and high prediction accuracy of the physical-based white-box models make them a popular and reliable tool for UHI evaluation. Nevertheless, some white-box based simulation tools are too complex and require a high level of expertise to operate, leading to potential inaccuracies in the obtained outcomes. Black-box models, in turn, despite their opaque working process, are more straightforward in use and require less computation time. The fusion of these two methods is a novel approach that may benefit both UHI prediction and mitigation at the design and operation stages, respectively.
... Multiple computational domains are usually nested to involve climate phenomena on different scales [26]. From a meteorological point of view, the UTWE can be classified as meso-scale, local scale, and micro-scale, generally corresponding to the urban/region scale, district/neighbourhood scale, and block/street scale in urban morphology [17,27,28]. Wong et al. stated that the meso-scale ranges from a few to several hundred kilometres, the local scale from a few hundred meters to a few kilometres, and the micro-scale from a few meters to several hundred meters [27]. ...
Article
The urban thermal and wind environment (UTWE) has become a major concern in urban planning design. Consideration of the UTWE involves steps of modelling, assessment, and improvement on multiple scales. However, the method specifications of each step on different scales are unclear, and a comprehensive review of relevant studies is thus required. On this basis, the modelling, assessment, and improvement methods of the UTWE are comprehensively reviewed according to their applicable scales. The review indicates that the scale is systematically considered by studies of the UTWE modelling and improvement, but not by studies of UTWE assessment. On the meso-scale, the UTWE of plot units are usually evaluated in isolation and the interactions among them are neglected. Studies of the UTWE improvement cover urban morphology, urban green and blue infrastructure, and urban materials, but some of their conclusions contradict each other. Current UTWE assessments cannot directly guide the selection of proper improvement strategies. In the future, studies of the UTWE improvement may be based on urban typologies such as the Local Climate Zone, and the data-driven approaches provide an opportunity to link the results of UTWE assessment and the corresponding improvement strategies.
... In building simulation programs such as EnergyPlus, the coefficient may vary according to the wind speed velocity and/or the height. Nevertheless, as mentioned in [14], the building simulation programs cannot handle spatially variable boundary conditions. ...
Preprint
Full-text available
A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the Du Fort–Frankel approach that provides an explicit scheme with a relaxed stability condition. The model is first validated using an analytical solution and then compared to three other standard schemes. Results show that the proposed model offers a good compromise in terms of high accuracy and reduced computational efforts. Then, a more complex case study is investigated, considering non-uniform shading effects due to the neighboring buildings. In addition, the surface heat transfer coefficient varies with wind velocity and height, which imposes an addition non-uniform boundary condition. After showing the reliability of the model prediction, a comparison over almost 120 cities in France is carried out between the two- and the one-dimensional approaches of the current building simulation programs. Important discrepancies are observed for regions with high magnitudes of solar radiation and wind velocity. Last, a sensitivity analysis is carried out using a derivative-based approach. It enables to assess the variability of the solution according to the modeling of the two-dimensional boundary conditions. Moreover, the proposed model computes efficiently the solution and its sensitivity to the modeling of the urban environment.
... This heat flux is composed of the short and long-wave radiative heat flux. The short-wave radiative heat flux transmitted through the building windows is generally taken into account and distributed to the building interior surfaces (by solar tracking or with a weighted method) [29]. For the long-wave radiative heat flux, it calculation requires the introduction of non-linear terms, most building simulation tools proposed then simplifications. ...
Preprint
Full-text available
Estimating the temperature field of a building envelope could be a time-consuming task. The use of a reduced-order method is then proposed: the Proper Generalized Decomposition method. The solution of the transient heat equation is then re-written as a function of its parameters: the boundary conditions, the initial condition, etc. To avoid a tremendous number of parameters, the initial condition is parameterized. This is usually done by using the Proper Orthogonal Decomposition method to provide an optimal basis. Building this basis requires data and a learning strategy. As an alternative, the use of orthogonal polynomials (Chebyshev, Legendre) is here proposed.
... Essentially, this semi-coupled approach resides to only insert a weather file to the BES tool, which, instead of a default file of the wider climate zone, is now being produced in a control volume close to the district/building of interest from the micro-climate model. In such an approach, normally a UCM tool is preferred due to its simplicity and fast calculation [156]. To date, the main steps of such semi-coupled approach are the following: ...
Article
Full-text available
This paper presents basic principles of built-environment physics’ modelling, and it reviews common computational tools and capabilities in a scope of practical design approaches for retrofitting purposes. Well-established simulation models and methods, with applications found mainly in the international scientific literature, are described by means of strengths and weaknesses as regards related tools’ availability, easiness to use, and reliability towards the determination of the optimal blends of retrofit measures for building energy upgrading and Urban Heat Island (UHI) mitigation. The various characteristics of computational approaches are listed and collated by means of comparison among the principal modelling methods as well as among the respective computational tools that may be used for simulation and decision-making purposes. Insights of coupling between building energy and urban microclimate models are also presented. The main goal was to provide a comprehensive overview of available simulation methods that can be used at the early design stages for planning retrofitting strategies and guiding engineers and technical professionals through the simulation tools’ options oriented to the considered case study.
... Recent years have witnessed an increased research interest in urban climate at varied spatial scales [15]. Numerical models can be broadly categorized into three categories on the basis of spatial scale [16]; ...
Article
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Various micro-scale models for comparing alternative design concepts have been developed in recent decades. The objective of this study is to provide an overview of current user-friendly micro-climate models. In the results, a vast majority of models identified were excluded from the review because the models were not micro-scale, lacking a user-interface, or were not available. In total, eight models met the seven-point inclusion criteria. These models were ADMS Temperature and Humidity model, advanced SkyHelios model, ANSYS FLUENT, ENVI-met, RayMan, SOL-WEIG, TownScope, and UMEP. These models differ in their complexity and their widespread use in the scientific community, ranging from very few to thousands of citations. Most of these models simulate air temperature, global radiation, and mean radiant temperature, which helps to evaluate outdoor thermal comfort in cities. All of these models offer a linkage to CAD or GIS software and user support systems at various levels, which facilitates a smooth integration to planning and design. We detected that all models have been evaluated against observations. A wider model comparison , however, has only been performed for fewer models. With this review, we aim to support the finding of a reliable tool, which is fit for the specific purpose.
... CitySim considers the effect of longwave radiation by calculating the temperature of the surfaces. For instance, by changing the cut-off angle (see Chapter 5), as an important urban geometric parameter, the amount of radiative cooling to the sky is changed (Lauzet et al., 2019) which affects building energy demand. These are the peculiar features of this software that are important considerations since this study is interested in what happens outside buildings more than inside. ...
Thesis
Cities are recognized as the main consumers of energy on the planet, and to optimize their energy consumption and enhance the potential of using renewable energy sources, built form and density are considered highly influential factors. The energy efficiency of compact built forms has been debated by many studies. Meanwhile, urban density, as an attribute of urban form, has yet to be well defined due to the diversity of density indicators used in literature. Hence, there is a lack of integrated guidelines for urban density indicators and their relationships with urban built forms in urban energy studies. This thesis establishes a framework to demonstrate the inter-correlation of urban built form, density and energy for residential buildings, and the impact of climate as an influential parameter is investigated by adopting a mixed methods research approach. It primarily identifies the relationship between the urban built form and density by introducing a novel indicator of urban form termed the Form Signature. It demonstrates the simultaneous correlation of two selected density indicators with influential variables developed from the geometry of four selected urban built forms. An urban energy simulation software package, CitySim, is adopted to conduct sensitivity analyses. The simulation models are validated against data from a known building group. An energy indicator, termed Energy Equity, is also introduced that simultaneously considers the amount of building energy demand as well as energy generation by building-mounted PVs. Cross case study analysis is undertaken to examine the impact of climate on urban energy performance, where four cities (London, Singapore, Helsinki and Phoenix) are chosen based on the specific climatic criteria. Meteonorm software is adopted to generate climate file relating to each case study. The investigation is further complemented by analysing future scenarios to examine the impact of climate change and technological developments (i.e. the penetration of EVs into the transportation sector) on the energy efficiency of urban areas of the future. Graphical results of the Form Signature indicator prove that the term ‘high density’ is crucially dependent on the definition of the density indicator. The resulting graphs provide a robust platform for the analysis of contexts such as climate, economy, social issues and energy. Overlaying results of building energy simulations over the Form Signature graphs indicates the relationship of energy with urban built form and density. Results show that buildings with a greater number of storeys and greater plan depth (equivalent to low values of plot ratio and variable values of site coverage) have lower energy demand. When PV generation is also considered, low number of storeys and great plan depth can improve the energy performance of buildings (equivalent to low plot ratio and high site coverage). Having identical geometric variables, tunnel-court form (that is introduced in this study) provides the greatest density while pavilion form provides the lowest (~80% lower than tunnel-court). The energy performance of tunnel-court form is also the highest in all considered climates, while pavilion form shows the lowest energy performance (between 27% and 67% for cooling-dominated buildings and between 7% and 32% for heating-dominated buildings). Nevertheless, if density remains constant and geometric variables are changed, the opposite becomes true. An important conclusion is that the site plans with similar built forms and densities may have different energy performance since the same value of density can be achieved by different combinations of geometrical variables. Increasing the cut-off angle reduces building energy demand in cooling-dominated buildings (i.e. in Singapore and Phoenix) between 6% and 56%, while increase building energy demand in heating-dominated buildings (i.e. in London and Helsinki) between 2% and 16.5%. Therefore, increasing density through cut-off angle is not always energy efficient as it depends on climate. In general, building energy demand in London is the lowest among the case studies, while it is the highest in Singapore (up to 219% higher than London). London also shows the highest value of Energy Equity (demonstrating the best energy performance) and Helsinki shows the lowest (up to 51% lower than London). Considering future scenarios, the total building energy demand in 2050 will be 48% higher than at present, on average. A recommendation for future urban planning in London, for instance, is that court and tunnel-court forms will be more energy efficient, and possessing a lower number of storeys, small cut-off angle and greater plan depth will further improve their energy performance and reduce their emissions. The holistic outcome of this study provides urban energy planning guidelines that can be used by various stakeholders in the built environment.
... Ferrando et al. [27] provided a user-oriented overview of the tools of physics-based urban scale energy modelling. Lauzet et al. [28] reviewed the strategies of chaining urban microclimate models and urban building energy models. Despite the importance of these review efforts in identifying the generic modelling approaches and application of urban-scale simulation, an overall review from the perspective of how the urban environment is incorporated into the simulation is missing. ...
Article
While more and more cities are planning towards sustainable development and climate resilience, a thorough understanding of the spatiotemporal pattern of building energy demand can be valuable for evidence-based city design and climate change mitigation. Energy demand in buildings is heavily influenced by its surrounding built and climatic environment. This requires simulation that is sensitive to the heterogeneity of buildings and climatic complications in dense urban settings. This paper provides a comprehensive review that documents and cross-compares the major methods to simulate building energy use at urban scale. The reviewed literature were acquired by using the search strings "urban-scale, city-scale or large-scale", "building energy, energy use, electricity use, energy consumption or thermal load" and "simulation, forecast, modelling or mapping" in the Web of Science database from 2010 to 2021. The result highlighted major differences in strengths, limitations and field of application of different methods based on modelling inputs, outputs and approaches to incorporate urban environment to the modelling. It also identified that future development of urban-scale building energy use should explore more ways to incorporate the spatial variation in weather and morphological conditions, especially in dense urban settings that experience greater environmental challenges.
... Coupling Building and atmospheric models: Simulation engines and aspects to be modelled For urban applications, two simulation models should be coupled at least: an atmospheric model and a building energy model. An extensive review of coupling and chaining simulation strategies has been done by Lauzet et al. [19]. Atmospheric models are normally classified depending on the scale, thus separating mesoscale, local scale and microscale models [20]. ...
Chapter
While performing building energy simulations, weather data are among the most important pieces of information needed by models and tools. In urban conditions, the typical rural weather files should be modified to take into account the microclimatic effects in urban areas. This chapter presents an overview of the most relevant effects to be considered: shadows, long-wave exchange, urban heat island, urban ventilation patterns, and urban greening, presenting a modelling chain procedure developed by the authors and to be used in TRNSYS tool, as well as the result of simulations done for the cases of Rome (Italy) and Antofagasta (Chile).
... In this way, the sensitivity analysis of meteorological parameters on energy consumptions has been comprehensively studied (Ayoub, 2020, Yi andPeng, 2017). Lauzet et al. (2019) conducted a detailed literature review on how local climate accounts for building energy models in the urban context and said that the chaining between building energy models and urban micro-climate models needs to be generalized to increase the accuracy of the building simulation. Mirasgedis et al. (2006)incorporated weather influences in the electricity demand forecasting model and found that the electrical energy consumption generally increases about 2.6% during summer due to the daily temperature rise. ...
Article
Accurate meteorological data play a substantial role in the building energy estimation process and projected energy savings retrofitting. The present study presents predicted micro-climates parameters with long short-term memory (LSTM) network based on the long-term on-site measurement and its significance in the building energy analysis. The one-day-period-ahead prediction results demonstrated approving performance that the average RMSE of predicted on-site temperature is 0.75 °C, corresponding to 4.11% in MAPE while RMSEs of EPW data (the common embedded datasets representative of the typical meteorological year) and suburban meteorological station data are 5.23 °C and 5.18 °C, respectively; the similar applied to relative humidity and solar radiation. The predicted meteorological parameters were therefore passed into building energy estimation models. The comparisons of energy consumption for building heating and cooling against reference models with suburban station climates and EPW datasets are statistically investigated, with the underlying propagation of bias from meteorological inputs being analyzed. For the typical building where the micro-climate station located, the estimation biases are as follows (i) LSTM predicted datasets: Δ = -1.58% for cooling, Δ = -2.51% for heating; (ii) EPW climate datasets: Δ = -29.68% for cooling, Δ = +129.88% for heating; (iii) suburban station climate datasets: Δ = -5.1% for cooling, Δ = +235.95% for heating.
... Since then, urban climate studies evolved from urban-rural comparisons of multiple meteorological variables (e.g. Chandler, 1965;Geiger, 1950;Kratzer, 1937) to complex urban land surface models that can be coupled with meso-climatic (Ching, 2013;Jandaghian and Berardi, 2020) and building energy models (Lauzet et al., 2019;Mirzaei, 2015). Nowadays, the latter are also being used for in-depth exploration and unravelling of the most complex climatic processes at the urban scale, which would otherwise be Table 1 Previous cities in which urban temperature variability across different LCZs was compared. ...
Article
Full-text available
Field measurement campaigns have grown exponentially in recent years, stemming from the need for reliable data to validate urban climate models and obtain a better understanding of urban climate features. Also contributing to this growth is the Local Climate Zone (LCZ) scheme, firstly developed to enhance the accuracy in the contextualisation of urban measurements, and lately used for characterising urban areas. Due to its relative novelty, researchers are still investigating the potential of LCZs and its indicators for urban temperature variability detection. In this respect, the present study introduces the results of an extensive monitoring campaign carried out in the city of Madrid over a two-year period (2016–2018). The aim of this work is to further examine the relationships between LCZs and air temperature differences, with emphasis on their hourly and seasonal evolution. A graphical and statistical analysis to identify temperature variability trends for each LCZ is performed. Results support the existing evidence suggesting a high level of effectiveness in capturing the heat island (UHI) profile of different urban areas, while underperforming when it comes to capturing diurnal temperature variability. The incorporation of indicators that explain the daytime temperature variation phenomenon into the LCZ scheme is therefore recommended, warranting further research.
... Despite the complexities, urban canyons are often considered as simple layouts composed of two box-shaped blocks, which is an understandable simplification at the early stages of the development of this type of models or for urban climate modelling. This simplification is reported as one of the possible sources of the building performance gap (Lauzet et al., 2019). In a typical urban canyon model, identical optical properties are considered for all adjacent buildings' exterior surfaces. ...
Article
The incident solar radiation on building facades is strongly affected by the urban characteristics, however frequently overlooked in the assessment of indoor environments due to limited data availability. Here, we show that a simplified representation of the urban environment can drastically affect the estimation of the incident solar radiation within urban canyons. We associate uncertainties with the canyon’s geometry, built surfaces, optical properties, as well as vegetation, and resort to a hybrid probabilistic-possibilistic approach to quantify the effects on thermo-visual comfort. Contrasting complex against simplified urban canyons shows that the pattern of incident solar radiation is uneven along the building façade and strongly correlated to the canyon’s characteristics. We also demonstrate that simplified models of urban canyons could underestimate the number of thermally comfortable instances by even 365 h a year (i.e., ~ 4% of the time). Similarly, a simplified canyon underestimates the visually comfortable occurrences, especially during the intermediate seasons. While a simplified canyon estimates a glare probability of 1.0, uncertainties within a complex canyon can lower the glare probability to 0.28 throughout the day. We show that for visual comfort, geometric characteristics alone, such as the canyon’s skyline, can outweigh optical properties such as the transmissivity of trees. This uncertainty, especially in the estimate of glare probability, may lead to different decisions about building envelope design, including the need for a more or less adaptable façade.
... Local weather conditions determine the heat and mass flow between buildings and their environment through (1) conductive and convective heat flux at the urban surfaces, (2) solar and long-wave radiation exchange, and (3) sensible and latent heat transfer through ventilation and infiltration (Lauzet et al., 2019). Hence, urban climate and microclimate can strongly influence building energy use, demand, and building thermal resilience. ...
Article
Urban microclimate exerts an increasing influence on urban buildings, energy, and sustainability. This study uses 10-year measured hourly weather data at 27 sites in San Francisco, California, to (1) analyze and visualize the urban microclimate patterns and urban heat island effect; (2) simulate annual energy use and peak electricity demand of typical large office buildings and large hotels to investigate the influence of urban microclimate on building performance; (3) simulate indoor air temperature of a single-family house without air-conditioning during the record three-day heatwave of 2017, to quantify the divergence of climate resilience due to urban microclimate effect. Results show significant microclimate effects in San Francisco with up to 11℃ outdoor air temperature difference between the coastal and downtown areas on September 1, 2017, during the record three-day heatwave. The simulated energy results of the prototype large office and large hotel buildings using the 2017 weather data show over 100% difference in annual heating energy use and 65% difference in annual cooling energy use across different stations; as well as up to 30% difference in peak cooling electricity demand. The impacts on annual site or source energy use are minimal (less than 5%) as cooling and heating in a mild climate are a relatively small portion of overall building energy use in San Francisco. Results also show the microclimate effects influence indoor air temperature of unconditioned homes by up to 5℃. Newer buildings and homes are much less affected by microclimate effects due to more stringent performance requirements of the building envelope and energy systems. These findings inform that San Francisco microclimate variations should be considered in urban energy planning, building energy codes and standards, as well as heat resilience policymaking.
... These weather files might severely affect the outcomes of energy simulation. This becomes even more relevant in the actual context of climate change [3], and it might be aggravated for cities in which the heat island effect takes place [4]. ...
Conference Paper
Full-text available
Present research compiles existing weather files for Madrid, Spain. It explores whether they can still be considered representative of the actual climatic conditions of the region. We compare the accuracy of existing weather files using a brand-new weather file, created following the standard ISO 15927-4 [5] with 10 recent years of the nearby meteorological observatory of Barajas Airport. Table 1 identifies all the weather files used in this research, including the period of record, the first time they were published, and the source from which they were extracted. All of these weather files were tested using the IEA Building Energy Simulation Test (BESTEST) procedure [6], a normalised set of models which is also used by the ANSI-ASHRAE Standard 140- 2017 [7] for testing computer programs.
... As examined in Section 2.6, a commonly used method to estimate the impact of the urban microclimate to building energy demand, is the usage of asynchronous coupling schemes or "Chaining" method [225]. sending the requested variable to the other at every time-step. ...
Thesis
This PhD work investigates the complex links between urban physical processes, through the development of coupled simulation platforms to account simultaneously for building energy demand, individual or district energy systems, and urban microclimate. The spatial and temporal scales correspond to urban neighborhoods under explicit geometries, and annual simulations respectively. Several coupling strategies have been evaluated, regarding thermal efficiency indicators, and the determination of the diversity of coupled phenomena. The synchronous coupling schemes can effectively assess the dynamical interactions between buildings and the local microclimate. Nevertheless, the coupling variable is sensitive to the thermal properties of the building. The simplification of the urban canopy layer to a single-node description reveals significant variability in building energy demand. Besides, the developed model has been employed to assess the thermal performance of an urban neighborhood in La Rochelle. The transition from local energy systems to the district energy network eliminates anthropogenic heat from buildings, and improves the outdoor thermal comfort conditions, acting as a local heat island mitigation strategy. However, it is associated with an energy penalty due to the ground losses of the piping circuit. This energy penalty is amplified when a passive mitigation strategy (cool materials) is implemented concurrently.
... In this case, the sky view factor decreases, which limits the radiation cooling of the sky. In addition, solar and infrared interflections are increasing with the surfaces of neighboring buildings [2]. The surrounding buildings significantly alter the airflow and redirect it. ...
Article
Full-text available
Urbanization leads to dramatic changes in urban microclimate, and becomes a serious problem in terms of ensuring comfortable and healthy living of city dwellers. The main factors affecting the microclimate of urban environment are not only the geographical features of cities, but also the density of buildings, the environmental concerns, the thermal response of buildings, the influence of plants and water bodies. The problem of the urban microclimate optimization is multifaceted since various factors affect changes in the urban environment. Thus an integrated multilevel systematic approach to studying the problems of the formation of the urban microclimate is required. Integrating the accumulated knowledge and practices in the research domain with design work is important.
... For improving the thermal efficiency of houses and reducing their annual energy demand, several measures and policy implications have been performed. This includes the energy savings through LEHs (Low energy house) concept, integration of renewables, and smart metering technologies [7]. In Australia, every newly constructed household must achieve a standard of at least a 6-star building under the Development Act 1993. ...
Article
Full-text available
Buildings and Residential sectors are amongst the major energy consumers of Australia. But the maximum portion of the energy consumed by these buildings is lost due to construction, design, or use of appliances. A significant amount of energy can be saved through this sector, which will not only reduce energy demand, it would further remove a major load from the National Grid. This study assessed a building in the residential sector of Australia and proposed how the use of Passive, Active techniques and adoption of the NZEB concept can help save energy consumption of residential houses. Different techniques and their implementation in the building were performed through both qualitative and quantitative analysis. The results obtained from the study show the house load of the designed building, a solar system that can take up the entire load, its financial assessment, and how the use of energy-efficient appliances and the use of passive techniques can result in improvement of energy efficiency.
... Since then, urban climate studies evolved from urban-rural comparisons of multiple meteorological variables (e.g. Chandler, 1965;Geiger, 1950;Kratzer, 1937) to complex urban land surface models that can be coupled with meso-climatic (Ching, 2013;Jandaghian and Berardi, 2020) and building energy models (Lauzet et al., 2019;Mirzaei, 2015). Nowadays, the latter are also being used for in-depth exploration and unravelling of the most complex climatic processes at the urban scale, which would otherwise be impossible to discern in a real urban environment. ...
Preprint
Full-text available
Field measurement campaigns have grown exponentially in recent years, stemming from the need for reliable data to validate urban climate models and obtain a better understanding of urban climate features. Also contributing to this growth is the Local Climate Zone (LCZ) scheme, firstly developed to enhance the accuracy in the contextualisation of urban measurements, and lately used for characterising urban areas. Due to its relative novelty, researchers are still investigating the potential of LCZs and its indicators for urban temperature variability detection. In this respect, the present study introduces the results of an extensive monitoring campaign carried out in the city of Madrid over a two-year period (2016-2018). The aim of this work is to further examine the relationships between LCZs and air temperature differences, with emphasis on their hourly and seasonal evolution. A graphical and statistical analysis to identify temperature variability trends for each LCZ is performed. Results support the existing evidence suggesting a high level of effectiveness in capturing the heat island (UHI) profile of different urban areas, while underperforming when it comes to capturing diurnal temperature variability. The incorporation of indicators that explain the daytime temperature variation phenomenon into the LCZ scheme is therefore recommended, warranting further research.
... Mosque is included in the type of building which of the condition inside is influenced by climate. Local climate is used in various modeling of buildings and affects thermal comfort as well as the cooling or heating demand of the room [9]. This will affect energy consumption as well as energy requirements for lighting [10]. ...
Conference Paper
Full-text available
The Mosque is a prayer place for Muslims, which has daily usage. For urban communities in Indonesia, mosques play an important role in social life. The Muslims carry out various kinds of activities in mosques besides praying. With its various activities, the mosque also has unique energy use characteristics when compared to other types of buildings such as houses, offices, and commercials. This paper presents an analysis of energy use in mosques in urban areas with tropical climate. Data was collected through a survey of mosques during 2018 to 2019 in Yogyakarta, Indonesia. The results show that the use of energy in mosques in urban areas in Yogyakarta on average consumes 182.2 kWh per day. The highest use is for air conditioning by 29.16%. Overall, the mosque energy consumption intensity varies from 0.16 to 4.54 kWh per square meter monthly and all of them meets criteria as very efficient buildings. In the other side, only 6 out of 15 mosques meet the national standard for lighting. In the term of green building standard, there are 9 out of 15 mosques meet the criteria in energy consumption aspect. This paper concludes that energy consumption in mosques in urban area with tropical climate is dominated by the air conditioning purposes.
... In building simulation programs such as ENERGYPLUS, the coefficient may vary according to the wind speed velocity and/ or the height. Nevertheless, as mentioned in [14], the building simulation programs cannot handle spatially variable boundary conditions. Even with the drawbacks identified, the development of two-or three-dimensional heat transfer model in building envelope is still a difficult task. ...
Article
A two-dimensional model is proposed for energy efficiency assessment through the simulation of heat transfer in building envelopes, considering the influence of the surrounding environment. The model is based on the Du Fort–Frankel approach that provides an explicit scheme with a relaxed stability condition. The model is first validated using an analytical solution and then compared to three other standard schemes. Results show that the proposed model offers a good compromise in terms of high accuracy and reduced computational efforts. Then, a more complex case study is investigated, considering non-uniform shading effects due to the neighboring buildings. In addition, the surface heat transfer coefficient varies with wind velocity and height, which imposes an addition non-uniform boundary condition. After showing the reliability of the model prediction, a comparison over almost 120 cities in France is carried out between the two- and the one-dimensional approaches of the current building simulation programs. Important discrepancies are observed for regions with high magnitudes of solar radiation and wind velocity. Last, a sensitivity analysis is carried out using a derivative-based approach. It enables to assess the variability of the solution according to the modeling of the two-dimensional boundary conditions. Moreover, the proposed model computes efficiently the solution and its sensitivity to the modeling of the urban environment.
... urban microclimate model and energy performance simulation tools running at the same time till convergence is achieved so as to go through the next time step simulation) could increase accuracy, but in this study, this was hard to achieve. The main limitation is due to the high precision of ENVI-met describing explicitly the study area, leading thus to high computational cost and the need to simulate only short study periods [92]. Still, in spite of the above mentioned limitations, coupling microclimate models with dynamic energy performance simulation tools is an up to date solution so as to accurately evaluate the role of urban climate on the buildings energy needs, from the one hand and to propose suitable strategies for further improvement, on the other hand. ...
Article
This study presents an one-way coupling approach between the ENVI-met microclimate model and the EnergyPlus building energy simulation program, to assess the effect of the urban greenery on the improvement of the buildings' cooling energy needs, in a dense urban area in Thessaloniki, Greece. Three commonly encountered urban tree species with different foliage densities are analyzed, whereas 2 different planting patterns are also considered. The obtained results indicate that the potential of trees on cooling the ambient air temperature and regulating the buildings cooling energy needs is mainly attributed to the radiative shading and the respective reduction of the solar heat gains of the exposed building façades. Moreover, the reduction of the building's cooling energy demand due to the addition of trees is directly related to their foliage density and their planting pattern. The higher energy savings up to 54% have been achieved when the trees formed a continuous shading canopy and for the Leaf Area Density of 2.5m 2 /m 3. Yet, the cooling potential of street trees has been found rather minor when they were not tall enough to shade the biggest part of the outer building façade.
Chapter
The Urban Heat Island (UHI) phenomenon commonly occurs in cities worldwide and may affect the health of millions of people, especially during heatwaves. To design and propose mitigation strategies, accurate urban microclimate simulation tools are needed but retrieving information for creating reliable models is often challenging. Sensitivity (SA) and uncertainty (UA) analysis can help to achieve valuable information and to produce informed decisions even in an uncertain scenario. However, to date, that are few studies that perform SA and UA on microclimate models. In the present study, the Urban Weather Generator (UWG) software has been used to apply SA and UA to the city of Athens, Greece. The urban model is established by considering the variation of input parameters that may occur within three of the most diffused Local Climate Zones (LCZ) in city centers. Results showed that the highest average Urban Heat Island Intensity (UHII) is obtained in LCZ2 followed by LCZ3 and LCZ5 (2.92, 2.78 and 2.60 ℃, respectively). However, due to the overlapping of UHII output values among different LCZs, it is not possible to state with absolute certainty that a “lower” LCZ corresponds to a higher UHII in a specific city. The SA confirmed that LCZ parameters such as building surface fraction, aspect ratio and mean height of buildings are the most influential ones in this case study. The percentage of buildings with an operating Air Conditioning system is also a key parameter in LCZ2, which however is also among the most uncertain ones. Thus, this value is required to be well established in future research.KeywordsSensitivity analysisUncertainty analysisUrban heat islandLocal climate zoneUrban weather generator
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Increasing efforts are addressed to improve heating, ventilation and air conditioning systems to achieve the decarbonization goal also in the building sector. But less attention is actually devoted to their impact on the urban heat island (UHI) phenomenon. Starting from the UHI assessment and its impact on building heating and cooling demand, a performance comparison is presented between groundwater heat pumps (GWHP) supplied by a district network and air source heat pumps (ASHP_1). Hybrid systems with small ASHPs and condensing boilers (ASHP_2) are also considered. An urban area, network and heat pumps were modeled for this study. In summer, the removal from the urban area of the heat dissipated by condensers thanks to GWHPs reduces the average UHI intensity with ASHPs by 45% and the relative cooling demand increment by 42.6%. A more favorable temperature of the groundwater than outside air permits a net superiority of the GWHP efficiency. At annual level, the GWHP provides energy savings of 27% and 34.5% and CO2 emission reductions of 27% and 39.5% compared to ASHP_1 and ASHP_2 respectively. The UHI mitigation to improve comfort and resilience in urban areas is an important added value for the contribution to decarbonization that a GWHP network can provide.
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Weather is essential for building energy simulation and energy efficient retrofitting since building energy use is highly weather-dependent. Weather data provided by EnergyPlus weather (EPW) data or generated by urban climate stations are used in most simulation tools. However, weather varies temporally and spatially, and the gaps may exist in the typical-year weather, weather from peri-urban areas, and local actual weather. This paper compares three types of weather datasets, from EPW data, from an urban climate station (STATION), and from long-term measurement with a local micro-climate station at Southeast University (SEU) campus. Results show there are significant gaps between the three weather datasets and compared with the STATION and EPW data, the average temperature in the micro-climate (SEU) is higher by 1.2 °C and 2.2 °C, respectively. The cooling degree day in SEU weather data is 20.4% and 40.8% higher, with heating degree day 15.7% and 26.3% lower. Comparing average relative humidity shows a big difference of 18.3% and 22.1% between SEU, STATION and EPW, and the former is comparatively lower than the latter two and no clear correlated trend. The SEU weather data has 23.1% more solar radiation hours than STATION weather data. As for wind speed, the average wind speed in the SEU weather data is lower by 1.14m/s and 1.29m/s, respectively, when compared with the STATION and EPW data. This study could be the reference for different trials for energy performance dynamic simulation for building energy analysis.
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To cope with the exacerbating housing problem due to urban population increase and rapid urbanization, the number of multi-unit residential complexes (MURCs) is sharply rising. MURCs account for approximately 30% or more of housing stocks in the New York and Los Angeles metropolitan areas; and much higher in developing world, for example, 58% in Seoul, Korea. Likewise, the share of electricity consumption of MURCs in cities is increasing. Most existing literature analyzed the electricity consumption at household or house level and neglected the electricity consumption in publicly shared spaces and utilities such as outdoor lighting, parking system, elevators (vertical transportation), playgrounds, site water and sewage, management offices, security offices, and so forth. Unlike single houses, MURCs with hundreds or thousands of houses tend to be planned and built all at once, under one site plan. Therefore, urban design or site planning can crucially affect the aggregated electricity consumption at neighborhood level with different land use patterns and spatial configurations, which change light, shade, wind, heat island, reflection for individual houses and public domain. Site plan attributes can also affect residents’ choice of indoor and outdoor activities. A few simulation studies tried to examine the effect of urban design at neighborhood level, but empirical analysis can be hardly found mainly due to data limitation. This study aims to empirically investigate the effect of urban design on aggregated electricity consumption at a neighborhood level. We collected the aggregated electricity consumption data of 1122 MURCs in Seoul during the first half of 2016 and adopted the panel analysis. This study confirmed that more building coverage roads and pavements increase the aggregated electricity consumption; more green open space reduces it. Interestingly, mid-rise and mid-open plan would be the optimum design because of the effect of the number of floors. As the number of floors increases, electricity consumption for vertical transportation increases faster and offsets the benefit of high-rise and high-open plan. The study is significant, because, unlike previous studies, it empirically analyzed the collective consumption of electricity at a neighborhood level, considering the site plan features. Furthermore, this study provides evidence-based implications for sustainable urban design, especially for MURCs.
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In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns.
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Cities account for over 75% of primary energy use in the world, with buildings making up a significant share of this energy use. Previous simulation-based research has established that building energy use can be greatly impacted by surrounding urban systems such as other buildings, vegetation, and roads. Understanding these relationships is thus critical to enhancing the efficiency of energy-intensive urban environments. Taking advantage of the recent profusion of urban data, this paper proposes a novel Context-aware Urban Energy Analytics (CUE-A) framework to empirically extract and quantify the relationships between building energy use and the spatial proximity of multiple surrounding urban systems. We apply the CUE-A framework to a case study of 477 buildings in a mid-size U.S. city to demonstrate its merits and the statistical significance of explored relationships. Results show that spatial proximity of other buildings, trees, and roads is associated with varied and significant changes in both the central tendency and variability of building energy use, indicating that empirical frameworks, which are a growing field of work, can serve as useful complements to existing simulation models. Further, our paper demonstrates that energy-aware urban planning and design has the potential to unlock energy efficiency and low-carbon pathways for cities around the world.
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Cities are an integral part to meeting the world's sustainable energy goals. Specifically, retrofits have been implemented to improve energy efficiency and reduce carbon emissions in the buildings sector. Recent simulation, reduced-order, and data-driven approaches have been used to predict the current energy consumption of urban buildings. However, these efforts are limited in their ability to evaluate potential impacts of future retrofits as they are unable to account for inter-building energy interactions that can influence urban building energy performance. To overcome these limitations, we extend a previously developed hybrid data-driven urban energy simulation (DUE-S) model that leverages building energy simulations and deep learning models by now predicting the impact of various building energy retrofits on multiple spatiotemporal scales across a city. We evaluate this approach on a case study of 29 densely co-located buildings in downtown Sacramento, California, USA. Our results indicate that accounting for urban context can compound the impact of retrofits on individual buildings by up to 7.4% as they also influence the electricity use of their surroundings. Finally, we show how DUE-S can provide insights on how to select buildings for retrofit that captures a potential compounding energy savings effect. We develop a greedy optimization algorithm that minimizes the number of required retrofits needed to achieve maximal energy savings across an urban study area. As a result, this work underscores how a flexible urban energy prediction model such as DUE-S can help inform energy-related decisions for a variety of urban-minded stakeholders including architects, engineers, planners, and policymakers.
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Constructing a new building inevitably modifies the microclimate in its vicinity. We propose a metamodel-based optimization method to devise optimal designs that minimize building’s adverse impacts on nearby pedestrians’ wind comfort in hot and cold seasons. Four design parameters, i.e., building width, building depth, building height, and building orientation, are considered for a greatly simplified building design task. Specifically, computational fluid dynamics (CFD) analyses are performed to calculate the mean wind velocity, while all CFD experiment samples are determined through the Box-Behnken design of experiment method. Based on 54 CFD experiments, the relationships between building design variables and the summer and winter mean velocities within a specified assessment area are learned using the response surface methodology. Finally, the desirability function and a genetic algorithm are combined to identify optimum design options under the robustness control (i.e., family error rate λ) for response surface models. The framework is applied to an infill development project to highlight its suitability in a real application. The experiment results show that as λ gradually decreases from 1 to 0.05, the overall desirability index dwindles from 0.33 and 0.22, consequently generating a more conservative optimum design decision.
Chapter
This chapter presents a review bringing a critical overview of the different ways in which the urban microclimate is currently considered in building design simulations. Therefore, different building energy models (BEMs) and urban climate models (UCMs) are presented. The ways these tools communicate are presented and the impact of UHI and the microclimate is assessed. For example, when conducting simulations neglecting the UHI, the variations can range from 10% to 200% for cooling demand and from 3% to 89% for heating demand with respect to simulations considering the UHI. Microclimate boundary conditions show to reduce loads by 130% for heating and 25% for cooling. The remaining scientific obstacles for better consideration of the urban climate context affecting the BEMs are discussed in this chapter. Some findings are the following: in some papers the boundary conditions of the BEM are only partially corrected; the BEM does not systematically introduce a feedback to the UCM; the simulation for a coupling project is usually launched for some days but longer simulation periods are necessary for an appropriate design of bioclimatic buildings. This could be done with parametric UCM tools. Future work should be about how to validate with experimental measurements the co-simulation results obtained.
Chapter
Building thermal performance and its energy consumption are affected by the energy exchange processes taking place between the outer skin or envelope of the building and the surrounding environment. It is a dynamic system in which there are continuous changes in a daily and seasonal range. Quantity and quality of the exposed envelope as well as albedo, vegetation, and urban geometry are significant factors in determining the impact of urban microclimates on energy building consumption. Existing buildings and their microclimates can be monitored in situ. This practice is very useful but time and resource consuming. Only some punctual cases can be evaluated thoroughly, and it is impossible to measure buildings that are still in project. Building energy simulation (BES) programs are capable of modelling building energy performance in detail in a dynamic model. The weather variables in an urban microclimate may be subtly different from the conditions prevailing over the area as a whole. Nevertheless, the input of meteorological conditions is usually taken from long-term averages provided by local weather stations. These data series ignore the modifying effect on the surroundings. This chapter presents a case study in a high-density area in the city of Mendoza, Argentina, in which year-round in situ measurements of temperature, humidity, radiation, and air movement were taken in two different scales: within the streets in a neighborhood and outside and inside a building. The micro-urban scale and the building scale were covered. A specific weather file was created for each scale, to be integrated in simulation software ENVI-met and EnergyPlus, respectively. Models were calibrated with the monitored data, to be run again with the information provided by local weather stations. Also, as a third term of comparison, the simulation workflow moves from the micro-urban- to a building-scale assessment by linking the ENVI-met software microclimatic results to the building energy simulation program EnergyPlus. Results obtained (a) with the local weather stations average climate input, (b) with the on-site microclimatic measurements, and (c) with ENVI-met software are compared in order to assess each case reliability in assessing the impact of local urban climate on building energy performance. Simulated-monitored results present differences of ±3.5%. This study reveals the capabilities and advantages of working with this tool for the generation of microclimatic data, which when integrated with EnergyPlus presents a less expensive and fast alternative to in situ monitoring.
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Estimating the temperature field of a building envelope could be a time-consuming task. The use of a reduced-order method is then proposed: the Proper Generalized Decomposition method. The solution of the transient heat equation is then re-written as a function of its parameters: the boundary conditions, the initial condition, etc. To avoid a tremendous number of parameters, the initial condition is parameterized. This is usually done by using the Proper Orthogonal Decomposition method to provide an optimal basis. Building this basis requires data and a learning strategy. As an alternative, the use of orthogonal polynomials (Chebyshev, Legendre) is here proposed. Highlights • Chebyshev and Legendre polynomials are used to approximate the initial condition • Performance of Chebyshev and Legendre polynomials are compared to the POD basis • Each basis combined with the PGD model is compared to laboratory measurements • The influence of four different parameters on the accuracy of the basis is studied • For each approximation basis, CPU calculation times are evaluated and compared
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Reviewed assessment methods for the urban environment • Critically analysed papers working on urban climate and energy demand, outdoor thermal comfort and the urban energy systems. • Demonstrated the links between the processes • An integrated workflow is proposed for assessment of the urban environment. Abstract The current climate change is calling for a drastic reduction of energy demand as well as of greenhouse gases. Besides this, cities also need to adapt to face the challenges related to climate change. Cities, with their complex urban texture and fabric, can be represented as a diverse ecosystem that does not have a clear and defined boundary. Multiple software tools that have been developed, in recent years, for assessment of urban climate, building energy demand, the outdoor thermal comfort and the energy systems. In this review, we, however, noted that these tools often address only one or two of these urban planning aspects. There is nonetheless an intricate link between them. For instance, the outdoor comfort assessment has shown that there is a strong link between biometeorology and architecture and urban climate. Additionally, to address the challenges of the energy transition, there will be a convergence of the energy needs in the future with an energy nexus regrouping the energy demand of urban areas. It is also highlighted that the uncertainty related to future climatic data makes urban adaptation and mitigation strategies complex to implement and to design given the lack of a comprehensive framework. We thus conclude by suggesting the need for a holistic interface to take into account this multi-dimensional problem. With the help of such a platform, a positive loop in urban design can be initiated leading to the development of low carbon cities and/or with the use of blue and green infrastructure to have a positive impact on the mitigation and adaptation strategies.
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The development of Urban-Scale Energy Modelling (USEM) at the district or city level is currently the goal of many research groups due to the increased interest in evaluating the impact of energy efficiency measures in city environments. Because USEM comprises a great variety of analysis areas, the simulation programs that are able to model urban-scale energy systems actually consist of an assemblage of different particular sub-models. In order to simulate each of the sub-models in USEM, one can choose to use either existing specific simulation engines or tailor-made models. Engines or tools for simulation of urban-scale energy systems have already been overviewed in previous existing literature, however the distinction and classification of tools according to their functionalities within each analysis area in USEM has not been clearly presented. Therefore, the present work aims at reviewing the existing tools while classifying them according to their capabilities. The ultimate goal of this classification is to expose the available resources for implementing new co-simulation approaches in USEM, which may reduce the modelling effort and increase reliability as a result of using established and validated simulation engines.
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This paper is a contribution towards the link between urban microclimate and building energy modelling using the Town Energy Balance model TEB as a new component embedded into the non-stationary building energy model TRNSYS. A number of comparative features between TEB and TRNSYS motivated this work, which includes commensurate time processing speed, similarity in describing the building facets, comparable inputs and simulation time scope, and the versatile modular architecture of TRNSYS. The paper describes the parameter tabs, inputs and outputs of TEB-Type 201, which offers (i) a user-friendly graphical interface, (ii) short time for data pre-processing with consistency check of inputs, (iii) versatility in selecting and storing outputs in small-sized files and (iv) easy installation. Besides illustrating the capabilities and practicality of this new component, an extensive sensitivity analysis highlights in a hierarchical form the main decisive urban and building parameters responsible in the formation of urban canopy heat or cool islands. The anthropogenic heat, the canyon geometry (aspect ratio, roof plan density) as well as the thermo-physical and radiative properties of the building envelope (thermal insulation, thermal inertia, albedo, emissivity) considered individually or in combination with each other appear to have clear effects on the formation of a microclimate in-canyon on the one hand, and in the magnitude and frequency of canopy heat or cool island episodes at daily and monthly basis on the other hand. The warming of canyon air is heavily influenced by the combination of high urban density (deep canyons and high plan density), high level of anthropogenic heat and weak thermal insulation. Low emissivity, no or low anthropogenic heat, better thermal insulation and low thermal mass favour the cooling of the canyon. These findings reveal the decisiveness of urban and building design choices on the outdoor thermal environment and hence on the energy demand for heating and cooling indoors. As outlook, the paper also notes the necessity of a synchronised coupling of urban canopy and building energy models.
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Urban parametrizations have been recently developed and integrated in mesoscale meteorological models for a better reproduction of urban heat islands and to compute building energy consumption. The objective of the present study is to evaluate the value of the use of a module able to produce highly resolved vertical profiles of these variables. For this purpose, the Canopy Interface Model (CIM) was integrated as an additional urban physics option in the Weather Research and Forecasting model. The coupling method is here detailed and its evaluation is done using a reference run based on a fine resolution WRF simulation. In order to keep both the CIM and the mesoscale model coherent, an additional term is added to the calculation of the CIM. Finally, the BUBBLE dataset is used to validate the simulation of the profiles from CIM. It is demonstrated that the proposed coupling improves the simulations of the variables in an urban grid and that the WRF + CIM + BEP-BEM system can provide highly resolved vertical profiles while at the same time improving significantly computational time. The data from these preliminary results are very promising as it provides the foundation for the CIM to act as an interface between mesoscale and microscale models.
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This paper presents the Meso-NH model version 5.4. Meso-NH is an atmospheric non hydrostatic research model that is applied to a broad range of resolutions, from synoptic to turbulent scales, and is designed for studies of physics and chemistry. It is a limited-area model employing advanced numerical techniques, including monotonic advection schemes for scalar transport and fourth-order centered or odd-order WENO advection schemes for momentum. The model includes state-of-the-art physics parameterization schemes that are important to represent convective-scale phenomena and turbulent eddies, as well as flows at larger scales. In addition, Meso-NH has been expanded to provide capabilities for a range of Earth system prediction applications such as chemistry and aerosols, electricity and lightning, hydrology, wildland fires, volcanic eruptions, and cyclones with ocean coupling. Here, we present the main innovations to the dynamics and physics of the code since the pioneer paper of Lafore et al. (1998) and provide an overview of recent applications and couplings.
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Rapid growth of cities, concerns on global warming and depletion of fossil fuel resources call for sustainable energy solutions for cities. Distributed energy systems such as energy hubs offer promising solutions in this context. Evaluating the energy demand at urban scale is vital to support the design of energy hubs. However, most of the recent studies are based on bottom-up models and do not consider the energy demand in detail. More specifically, the influence of the urban climate on urban energy demand has not been considered so far in the energy system design process. In order to address this research gap, a novel computational platform is developed in the first part of this study, combining an urban climate model with a building simulation tool and an energy system optimization model. The second part of the manuscript is devoted to quantifying the impact of urban climate on energy system design and assessing the consequences of neglecting this specific aspect on energy system performance. Three case studies are conducted considering three building densities for the city of Nablus (building density at the periphery, center and future center of the city) in Palestine. Three scenarios representing (1) standalone buildings (present practice) (2) shadowing and longwave reflection (radiation heat transfer from the walls and the roofs of the buildings to the urban climate and to the sky) of neighboring buildings and (3) urban climate are considered for each case study when computing the energy demand. Subsequently, the energy system is optimized considering Net Present Value (NPV) and system autonomy level as the objective functions (Pareto optimization). The results of the study reveal that the urban climate has a notable impact on the energy demand and energy system design. More importantly, it is shown that the influence of urban climate results in higher fluctuations in the energy demand, which in turn results in a notable increase in the NPV (by up to 40%). This further magnifies the increase in annual or peak demand. The study reveals that neglecting the influence of urban climate in the energy system design process can result in a performance gap in NPV, grid integration level, https://doi. T and greenhouse gas emissions and can impose reliability issues. The design tool introduced in this study can be used for urban planning to mitigate the aforementioned adverse effects.
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Building more energy-efficient and sustainable urban areas that will both mitigate the effects of climate change and anticipate living conditions in future climate scenarios requires the development of new tools and methods that can help urban planners, architects and communities achieve this goal. In the current study, we designed a workflow that links different methodologies developed separately, to derive the energy consumption of a university school campus for the future. Three different scenarios for typical future years (2039, 2069, 2099) were run, as well as a renovation scenario (Minergie-P). We analyzed the impact of climate change on the heating and cooling demand of buildings and determined the relevance of taking into account the local climate in this particular context. The results from the simulations confirmed that in the future, there will be a constant decrease in the heating demand, while the cooling demand will substantially increase. Significantly, it was further demonstrated that when the local urban climate was taken into account, there was an even higher rise in the cooling demand, but also that a set of proposed Minergie-P renovations were not sufficient to achieve resilient buildings. We discuss the implication of this work for the simulation of building energy consumption at the neighborhood scale and the impact of future local climate on energy system design. We finally give a few perspectives regarding improved urban design and possible pathways for future urban areas.
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A building influences its surrounding external environment, and its indoor environment is usually sensitive to its surroundings. In order to obtain a more accurate prediction of the indoor thermal behaviour of a building or a building stock, it is necessary to consider the interactions between the indoor and outdoor thermal environments. The modelling approach presented in this paper is based on a building energy model and the urban modelling tool SOLENE-Microclimat. A sensitivity analysis is undertaken to highlight the parameters which influence the performance of the building model. Then, the thermal indoor behaviour of a building is compared to in situ experimental measurements and the outdoor thermal environment is evaluated in terms of external surface temperatures. The model's accuracy and behaviour is evaluated and a crossover approach with the sensitivity analysis results is proposed. The model obtains a good level of performance for almost all variables, unless some external surface temperatures intervals.
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A new methodology is proposed to couple a meteorological model with a building energy use model. The aim of such a coupling is to improve the boundary conditions of both models with no significant increase in computational time. In the present case, the Canopy Interface Model (CIM) is coupled with CitySim. CitySim provides the geometrical characteristics to CIM, which then calculates a high resolution profile of the meteorological variables. These are in turn used by CitySim to calculate the energy flows in an urban district. We have conducted a series of experiments on the EPFL campus in Lausanne, Switzerland, to show the effectiveness of the coupling strategy. First, measured data from the campus for the year 2015 are used to force CIM and to evaluate its aptitude to reproduce high resolution vertical profiles. Second, we compare the use of local climatic data and data from a meteorological station located outside the urban area, in an evaluation of energy use. In both experiments, we demonstrate the importance of using in building energy software, meteorological variables that account for the urban microclimate. Furthermore, we also show that some building and urban forms are more sensitive to the local environment.
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