Urban Heat Island and its Impact on Building Energy Consumption

2009 ◽  
Vol 3 (1) ◽  
pp. 261-270 ◽  
Author(s):  
Rajagopalan Priyadarsini
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Urban Heat

scholarly journals Review on Urban Heat Island in China: Methods, Its Impact on Buildings Energy Demand and Mitigation Strategies

2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Design Stage ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Rapid Urbanization ◽  
Substantial Impact ◽  
Urban Heat ◽  
The Impact

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.

scholarly journals Analysis of the urban heat island effects on building energy consumption

2014 ◽  
Vol 6 (1) ◽  
pp. 91-99 ◽  
Author(s):  
Susanna Magli ◽  
Chiara Lodi ◽  
Luca Lombroso ◽  
Alberto Muscio ◽  
Sergio Teggi
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Urban Heat

Urban heat island impacts on building energy consumption: A review of approaches and findings

Energy ◽  
2019 ◽  
Vol 174 ◽  
pp. 407-419 ◽  
Author(s):  
Xiaoma Li ◽  
Yuyu Zhou ◽  
Sha Yu ◽  
Gensuo Jia ◽  
Huidong Li ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Building Energy ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Urban Heat

scholarly journals A Statistical–Dynamical Downscaling for the Urban Heat Island and Building Energy Consumption—Analysis of Its Uncertainties

2020 ◽  
Vol 59 (5) ◽  
pp. 859-883 ◽  
Author(s):  
Robert Schoetter ◽  
Julia Hidalgo ◽  
Renaud Jougla ◽  
Valéry Masson ◽  
Mario Rega ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Dynamical Downscaling ◽  
Building Energy ◽  
Weather Type ◽  
Statistical Uncertainty ◽  
Heat Island ◽  
Building Energy Consumption ◽  
Urban Heat ◽  
Dynamical Uncertainty

AbstractHigh-resolution maps of the urban heat island (UHI) and building energy consumption are relevant for urban planning in the context of climate change mitigation and adaptation. A statistical–dynamical downscaling for these parameters is proposed in the present study. It combines a statistical local weather type approach with dynamical simulations using the mesoscale atmospheric model Meso-NH coupled to the urban canopy model Town Energy Balance. The downscaling is subject to uncertainties related to the weather type approach (statistical uncertainty) and to the numerical models (dynamical uncertainty). These uncertainties are quantified for two French cities (Toulouse and Dijon) for which long-term dense high-quality observations are available. The seasonal average nocturnal UHI intensity is simulated with less than 0.2 K bias for Dijon, but it is overestimated by up to 0.8 K for Toulouse. The sensitivity of the UHI intensity to weather type is, on average, captured by Meso-NH. The statistical uncertainty is as large as the dynamical uncertainty if only one day is simulated for each weather type. It can be considerably reduced if 3–6 days are taken instead. The UHI reduces the building energy consumption by 10% in the center of Toulouse; it should therefore be taken into account in the production of building energy consumption maps.

The effect of sky conditions and urban morphology on urban heat island in Seoul city

2020 ◽  
Author(s):  
Soo Joeng Joen ◽  
Jin woo oh ◽  
Jack Ngarambe ◽  
Patrick Nzivugira Duhirwe ◽  
Mi Aye Su ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Urban Areas ◽  
Building Energy ◽  
Area Ratio ◽  
Heat Island ◽  
Urban Geometry ◽  
Building Area ◽  
Urban Heat ◽  
Sky Conditions

<p>The urban heat island (UHI) is a serious climatological phenomenon that is likely to exacerbate the effects of climate change. It has adverse effects on the thermal comfort of urban dwellers, building energy consumption and the general health of vulnerable demographics (i.e. older people). To understand the effects of UHI and therefore devise efficient methods to mitigate it, it is important that we understand the many factors affecting UHI and the magnitude of their contribution on the manifestation of UHI, especially in urban areas. Consequently, in the current study, we study the effect of sky conditions and urban geometry on UHI in Seoul city, South Korea. The climatic data detailing diverse sky conditions, categorized by the amount of cloud cover, was collected from 28 Automatic Weather Stations (AWS) located in Seoul city. Information on urban geometry such as building density, gross floor area ration and building area ratio was obtained from satellite imagery. Our results indicate that the levels of UHI, quantified using urban heat island intensity (UHII), are dependent on the prevailing sky conditions. We found that, UHII was highest under cloudy sky conditions (r = 0.71) and lowest under clear sky conditions (r = 0.66). Furthermore, we found that UHII was correlated with building area ratio and gross area ratio; areas with high building area ratios and gross area ratios tended to also experience high UHII levels. The results presented in the current study are useful to policy makers or urban designers that wish to curb the increasing effects of UHI in urban areas and consequently improve thermal comfort in urban areas, reduce building energy consumption for space cooling purposes and prevent heat-related mortalities in old and vulnerable populations.</p><p> </p><div> </div>

scholarly journals A Building Retrofit and Sensitivity Analysis in an Automatically Calibrated Model Considering the Urban Heat Island Effect in Abu Dhabi, UAE

2019 ◽  
Vol 11 (24) ◽  
pp. 6905 ◽  
Author(s):  
Lindita Bande ◽  
Adalberto Guerra Cabrera ◽  
Young Ki Kim ◽  
Afshin Afshari ◽  
Mario Favalli Ragusini ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Urban Heat Island ◽  
Urban Heat Island Effect ◽  
Abu Dhabi ◽  
Weather Data ◽  
Mean Bias Error ◽  
Heat Island ◽  
Island Effect ◽  
Urban Heat ◽  
Urban Weather

Villas are a very common building typology in Abu Dhabi. Due to its preponderance in residential areas, studying how to effectively reduce energy demand for this type of building is critical for Abu Dhabi, and many similar cities in the region. This study aims to show the impact of proposed energy efficiency measures on a villa using a calibrated model and to demonstrate that to be accurate, the model must be driven using urban weather data instead of rural weather data due to the significance of the urban heat island effect. Available data for this case study includes construction properties, on-site (urban) weather data, occupancy-related loads and schedules and rural weather data. Four main steps were followed, weather data customisation combining urban and rural weather variables, model calibration using a genetic algorithm-based tool and simulating retrofit strategies. We created a calibrated model for electricity demand during 2016–2017 with a 4% normalized mean bias error and an 11% coefficient of variation of the mean square error. Changing from none to all retrofit strategies results in a 34% reduction in annual energy consumption. According to the calibrated model, increased urban temperatures cause a 7.1% increase in total energy consumption.

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