Modeling and analysis of heat emissions from buildings to ambient air
Introduction
Urban microclimate is affected by morphology and thermal characteristics of the urban environment (e.g., street width, surfaces’ solar reflectivity, material roughness, evapotranspiration of surfaces) as well as by anthropogenic heat emissions (e.g., heat releases from buildings, humans, transportation, and industries) [1], [2]. In particular, all these features influence the airflow, humidity level, and air temperature of the urban environment [3]. In dense urban areas, the absorption of short-wave radiation from the sun, the increased surface areas capable of storing heat and the resulting anthropogenic heat emissions are boosted [4]. Meanwhile, due to the compactness of the building and street geometry, the long-wave radiative heat loss towards the sky, the evaporation from vegetation and water bodies, and the turbulent heat transport due to the wind are hindered [5]. Moreover, air pollution and the relative greenhouse effect hampers the re-emission of long-wave radiation towards the urban environment [6]. All these factors contribute to the urban heat island (UHI) effect, which is the climatic phenomenon causing higher air temperatures in dense urban areas compared to the surrounding rural open areas [7]. In recent years, UHI became a huge concern in cities [8], stressed by climate change and the increasing risk of heatwaves [9]. The phenomenon is registered in almost all urban areas, regardless of the city size or the climate [10], [11]. For example, in the 1400 square kilometer (km2) humid subtropical climate city of Nanjing, China (Köppen climate classification Cfa [12]), the UHI is indicated as a nocturnal phenomenon that can increase the difference in outdoor air temperature between the urban area and its suburbs by 2.2 °C [13]. Similarly, in the 260 km2 continental climate city of Madison, Wisconsin (Köppen climate classification Dfb [12]), in 2012 the registered mean maximum temperature in July was 1.8 °C higher than it was in the surrounding rural areas [14]. This substantial increase in temperature affects the energy consumption of buildings [15] and the livability of outdoor areas [16].
Buildings play a fundamental role in the UHI, due to their morphology and surface characteristics. Besides, they count for a large portion of the total anthropogenic heat emission of an urban area. At urban scale, anthropogenic heat emission has four main origins: human metabolism, industry, transport, and buildings [17]. People spend, usually, most of the time inside buildings, but they account for only a small portion of the total latent and sensible heat emissions. For this reason, in several studies, only industry, transport, and buildings are recognized as the main sources of anthropogenic heat [18]. Buildings emit heat to the urban environment to maintain certain indoor temperatures and humidity levels for occupant comfort or process and manufacturing needs. The heat sources of building heat emissions include (1) the received solar radiation on the building’s exterior surfaces (walls, roofs) and transmitted through glazing; (2) convective heat transfer between building envelope and ambient air due to temperature difference, and airflow through openings and cracks in the envelope; (3) internal heat gains from energy consumed in buildings to provide services of lighting, HVAC (heating, ventilation, air-conditioning), plug-in equipment, and domestic hot water, and (4) a relatively small fraction of heat from occupants. Buildings emit heat towards the ambient air as sensible and latent heat due to exiting air from the building envelope (e.g., fans, heating and cooling systems, exfiltration, etc.), and as thermal radiation towards the environment (e.g., air, sky, ground, other surfaces) due to differences in surface temperatures. Particularly, in this study, the building heat emission towards the ambient air was considered. Indeed, traditionally in Building Energy Modelling (BEM) the heat emission from buildings is usually not calculated, as it does not directly influence building energy performance. Some specific tools (e.g., Rayman [19], SOLWEIG [20], ENVI-met [21], or CityComfort+ [22]) are usually employed to calculate the effect of buildings on the microclimate. However, in these cases, buildings are simplified as static daily profile of heat sources.
In numerous former studies, the heat emissions from buildings to air is simplified to be equal to their energy consumption [23]. However, recent research confirms that heat emissions from a building could be much greater than its energy use. For example, from the energy simulation of an office building in Houston, Texas (Köppen climate classification Cfa [12]), it emerged that, during summer, the heat emission is between 40% and 70% higher than the energy consumption [24], due to the energy (mainly solar radiation) coming from the environment and the type of cooling system used, such as cooling towers. In recent years, several studies focused on better understanding the relation between heat emissions from buildings and UHI. Numerous studies were conducted to optimize the buildings’ geometry to decrease the impact of UHI on new urban areas [4], [5], [25]. Others are more focused on the modification of the extent of radiative exchange of surfaces (e.g., with green areas [7], cool facades [8], cool roofs [15], or a mixture of these three strategies [26]) or on shifting the heat release in time via buildings’ structural heat storage [27] or phase change materials [28].
However, these adaptation techniques only partially consider the anthropogenic heat from buildings because it is not accurately quantified or understood.
Three main approaches are used to quantify building heat emissions: (1) assessment through inventories, (2) assessment through heat energy balances and (3) assessment through building energy models [17]. The inventory approach exploits registered or surveyed energy consumption data to quantify heat emissions from buildings and other sectors in cities [18]. The registered data are directly converted into heat emissions, without considering time delays, heat storage or differentiation between latent and sensible heat. Data are usually spread spatially within the area through different indexes (e.g., district gross domestic product and population density [29], energy statistics related to the building typology [30]). The assessment through inventories approach relies on major assumptions and simplifications, making it easy to implement. Moreover, the needed data are usually available. For these reasons, this method was widely employed in early studies related to anthropogenic heat emission and its impact on urban microclimate [31], [32], [33]. However, the main critical problems are the lack of temporal resolution, leading to erroneous temporal distribution, and the assumption that the heat emission from a building is instantly equal to its energy consumption [18]. Besides these weaknesses, this method has been used for numerous studies for cities and large regions (e.g., by Harrison et al. [23] for London, by Klysik [34] for Poland, and by Lee et al. [35] for Korea).
The second approach, assessment through heat energy balances, exploits the idea of defining a control volume containing the urban area under study, and it tracks, via measurements, all the occurring energy inputs and outputs. The energy entering and exiting from the control volume is assessed via measurements and approximations, while the micrometeorological data can be tracked via net radiometers and eddy covariance techniques, for the net radiation and sensible and latent heat fluxes, respectively. The heat emission is then approximated to the difference between the latent and sensible heat fluxes crossing the control volume [18]. This approach, being applied using measurement data, can be used to validate or compare the other methodologies. However, the measurement campaign could be expensive and time-consuming, and it is impossible to split the anthropogenic heat among its sources. This approach was used for numerous city studies (e.g., by Sailor and Hart [36] for 50 cities in the United States and by Hamilton et al. [37] for London). This approach assumes small or no differentiation between the industry/vehicles sector and the building sector. For this reason, numerous researchers started to study this phenomenon by developing the third approach: assessment through building energy models.
The building energy models methodologies are usually bottom-up and try to assess the heat fluxes towards the outdoor environment from different building components [17]. Building energy models are able to assess the building performance for space heating and cooling and ventilation considering internal loads and the environmental boundary conditions. These models are quite accurate in assessing building energy use because they employ largely verified methods with high spatial and temporal resolutions [38]. However, usually, major simplifications are made to allow the representation of the dynamics of loads and the consequent heat emissions from buildings. Actually, the heat emitted from a building as long-wave radiation can hit other surrounding surfaces (e.g., ground, other buildings) which absorb or re-emit eventually, with a time delay, the absorbed heat. Understanding and considering this dynamic is fundamental to assess effective mitigation strategies for heatwaves and UHI [18]. A building emits heat towards the whole surrounding environment (e.g., the air, the sky, the ground, other buildings, trees, water bodies). In particular, to better study the impact of building heat emission on UHI and microclimate in general, the heat emitted by the buildings towards the ambient air is the predominant topic.
This study aims to develop a novel approach to calculating heat emissions from buildings using a detailed physics-based model, and assess the characteristics and dynamics of building heat emissions across building types, climates, and energy efficiency levels. The outcome of this study provides new insights into the importance and extent of heat emissions from buildings. The study focused on sixteen United States Department of Energy (U.S. DOE) commercial reference building models [39] in four selected typical climate zones with two levels of energy efficiency (corresponding to minimum requirements of ASHRAE Standard 90.1 in 2004 and 2016). These diverse combinations enabled detailed simulation of heat emissions from different building components (e.g., envelope, HVAC systems, exfiltration, cooling towers) and comparative analyses across building types, climates, and energy efficiency levels. The study’s outcomes can inform urban planning and mitigation strategies towards UHI, heatwaves, and overall urban overheating due to urban development, climate change, and extreme weather events. Numerous publications exist studying more in detail the heat emission from buildings regarding UHI. The majority of them are focused on the envelope [40], [41], and a very limited number are focused on other aspects such as the systems [42] or the ventilation [43]. However, none of the existing methodologies address all the aspects together and with a bottom-up approach. The main novelties of this paper are (1) the use of physics-based equations at a user-defined timestep (implemented in EnergyPlus), (2) the assessment of heat emissions by each individual building component (i.e., envelope, systems and zones), (3) the differentiation between the sensible and latent heat emissions, and (4) the detailed evaluation of heat emisssions across different building typologies, climates and energy efficiency levels.
It is worth noticing that in this study a nomenclature consistent with EnergyPlus was used. Site energy is chosen as the building performance metric in the analyses. Site energy is the sum (converted to have the same energy unit) of all types of fuels (e.g., electricity, natural gas, fuel oil) consumed on-site by a building—that is, the amount of fuel and electricity consumed by a building as reflected in the utility bills. Corresponding to site energy is the source energy, which is the fuel or resources consumed at the power plant, including generation inefficiency and transmission and distribution losses along the energy network (e.g., natural gas pipeline and electric grid).
The remaining of the paper is organized as follows. Section 2 describes the methodology of calculating the heat emissions by three main components (envelope, HVAC, and zones). Section 3 covers verification of the simulated heat emissions. Section 4 describes a case study with results presented in Section 5. Discussion is provided in Section 6, and conclusions are drawn in the last section.
Section snippets
Methodology
The proposed methodology relies on a physics-based heat balance model. The model considers the heat emission towards ambient air from three main building components (Fig. 1): (1) envelope, (2) zones, and (3) HVAC system. Exploiting physics-based heat balance equations, the proposed model provides both spatial and temporal flexibility. Thus, it can be used with any weather dataset and any building model, and it can be exploited at any user-defined time step, from 1 min to 60 min for an annual
Model results verification
In this study we used the U.S. DOE’s commercial reference models [39]. These models already have been validated from the energy use perspective, however, the heat emissions from buildings are not covered in the validation process defined in ASHRAE Standard 140 [48]. Thus, a simple results verification approach was here proposed to verify the calculations. EnergyPlus undergoes verification tests before each release to the public [49], [50]. For this reason, the individual parameter values used
Case study
The U.S. DOE developed the commercial prototype building models to represent about 70% of the commercial buildings in the United States, and they are popularly used in building simulation as a reliable baseline of comparison [39]. The prototype models include 16 commercial building types characterized for 17 climate locations (across all eight U.S. climate zones) and five different energy efficiency levels in accordance to recent editions of ASHRAE Standard 90.1. In this study, the 16
Results
The 16 U.S. DOE commercial prototype building models that meet the ASHRAE Standard 90.1–2004 and 2016 were run with the four analyzed weather datasets (Table 2). Annual simulations were conducted with a time step of one hour. The hourly results were eventually aggregated to understand annual and monthly trends in the heat emissions for the different combinations of building typology, energy efficiency level, and climate zone (location). In this section, the annual, monthly, and hourly results
Energy savings versus heat emission reduction
Fig. 13 reports the ratios between the annual site energy and the annual heat emission of the two energy efficiency levels from ASHRAE 90.1–2004 and 90.1–2016. From the site energy perspective, all the ratios were greater than one. This means that the site energy for the 2004 efficiency level is usually higher than that for 2016. Except for the Fairbanks Full Service Restaurant models, the ratio was slightly lower than one (i.e., 0.98). This is because, in the heating-dominated climate of
Conclusions
This study presents a method to calculate the heat emission towards ambient air from buildings, which can be grouped into three main contributions by components of: (1) building envelope, (2) zones, and (3) HVAC system. The heat released by the envelope component includes exterior surfaces’ convective heat to the ambient air and their long-wave radiation towards the moisture and particles in the air including, both in the convection and in the radiation, the solar energy absorbed and re-emitted
CRediT authorship contribution statement
Tianzhen Hong: Conceptualization, Methodology, Writing - original draft, Supervision, Funding acquisition. Martina Ferrando: Investigation, Writing - original draft. Xuan Luo: Methodology, Writing - review & editing. Francesco Causone: Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This research was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technologies of the United States Department of Energy, under Contract No. DE-AC02-05CH11231, and by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 691895 - project SHAR-LLM (Sharing Cities).
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2022, Journal of Cleaner ProductionCitation Excerpt :This phenomenon is known as an urban heat island (UHI), which is understood as the rise in urban areas' temperatures compared to the surrounding rural areas (Oke et al., 2017a). UHIs' causes are associated with various reasons, including land use changes or replacement of natural covers by non-permeable surfaces, construction materials’ properties (e.g. low albedo, high thermal inertia, etc.); altered roughness and morphology, which inhibits, for example, natural ventilation; and anthropogenic heat or heat emitted by buildings, people, transport and industries (Hong et al., 2020; Woong Kim and Brown, 2021). UHIs are associated with various socio-environmental and economic impacts, including increased photochemical smog and urban pollution (Ulpiani, 2021); Li et al., 2018); increased morbidity and mortality from air pollution or heatwave intensification (Gholizadeh Touchaei, 2015; Santamouris and Fiorito, 2021; Chen et al., 2014); decreased thermal comfort conditions (Giridharan and Emmanuel, 2018; He et al., 2020); energy consumption for building conditioning (Boccalatte et al., 2020; Palme et al., 2017); rainfall index alterations (Shepherd, 2005; Keuser, 2014; Yang et al., 2014); contributing to climate change (Woong Kim and Brown, 2021), and alterations in the phenology of animal and/or plant species inserted in urban environments (Luo et al., 2007; Lu et al., 2006; Shochat et al., 2007), among other effects.