Heating and cooling degree day prediction within the London urban heat island area

Kolokotroni, Maria; Zhang, Y.; Giridharan, Renganathan

doi:10.1177/0143624409104733

Cited by 33 publications

(24 citation statements)

References 28 publications

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“…27,34 Generally, the LSSAT model is capable of predicting outdoor temperature results with suitable accuracy: the mean relative error (RE) and the mean Pearson correlation coefficient are RE ¼ 3.5%, r ¼ 0.99 and RE ¼ 5%, r ¼ 0.98 for night time and daytime, respectively. 21 Night time predictions are more accurate than the daytime ones. Furthermore, daily and monthly mean predictions are much more accurate than hourly predictions.…”

Section: Lssatmentioning

confidence: 95%

“…For example, work has been undertaken with a series of fixed point temperature measurements -this work is not reported here but details can be found elsewhere. 21 …”

Section: Lucid Data Gathering and Preparationmentioning

confidence: 99%

“…21 The model was developed using a back-propagation ANN model based on hourly air temperature (dependent variable) measurements at 77 fixed temperature …”

Section: Lssatmentioning

confidence: 99%

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The comfort, energy and health implications of London’s urban heat island

Mavrogianni

Davies

Batty

et al. 2011

Building Services Engineering Research and Technology

Self Cite

109

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The urban heat island (UHI) is a well-known effect of urbanisation and is particularly important in world megacities. Overheating in such cities is expected to be exacerbated in the future as a result of further urban growth and climate change. Demonstrating and quantifying the impact of individual design interventions on the UHI is currently difficult using available software tools. The tools developed in the LUCID ('The Development of a Local Urban Climate Model and its Application to the Intelligent Design of Cities') research project will enable the related impacts to be better understood, quantified and addressed. This article summarises the relevant literature and reports on the ongoing work of the project. Practical applications: There is a complex relationship between built form, urban processes, local temperature, comfort, energy use and health. The UHI effect is significant and there is a growing recognition of this issue. Developers and planners are seeking advice on design decisions at a variety of scales based on scientifically robust, quantitative methods. The LUCID project has thus developed a series of tools that (1) quantify the effect of urbanisation processes on local environmental conditions, and (2) quantify the impact of such conditions on comfort, energy use and health. The use of such tools is vital, both to inform policy but also to be able to demonstrate compliance with it.

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Section: Lssatmentioning

confidence: 95%

“…For example, work has been undertaken with a series of fixed point temperature measurements -this work is not reported here but details can be found elsewhere. 21 …”

Section: Lucid Data Gathering and Preparationmentioning

confidence: 99%

See 1 more Smart Citation

The comfort, energy and health implications of London’s urban heat island

Mavrogianni

Davies

Batty

et al. 2011

Building Services Engineering Research and Technology

Self Cite

109

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show abstract

“…The thermal mass of a neighborhood has also been shown to have an effect on the magnitude of the urban heat island (Kolokotroni et al, 2009). By nightfall, as the temperature of the air decreases, the stored energy is dissipated to the air.…”

Section: Neighborhood Building Thermal Massmentioning

confidence: 99%

Urban neighborhood characteristics influence on a building indoor environment

Mirzaei

Olsthoorn

Torjan

et al. 2015

Sustainable Cities and Society

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The urban heat island (UHI) is exacerbated during heat waves, which have been reported to be more frequent in recent years. Unwanted consequences of the UHI not only include an increase in mean/peak energy demand, but an escalation in the heat-related mortality and disease. Although UHI mitigation strategies are being implemented by cities, they serve as mid to longterm solutions. The implementation of short-term mitigation strategies is paramount for cities to reduce the immediate risks of the heat-related hazards. Various prognostic tools have been developed to empower urban planners and decision makers in minimizing the related risks. These tools are mainly based on stationary parameters, such as the average surface temperature of a city, and are independent of land-use/land-cover (LULC). Furthermore, the outdoor temperatures are utilized to develop such models. However, heat-related risks occur mostly in indoor spaces, and correlations between indoor and outdoor spaces are rarely considered.In this study, a predictive model for the indoor air temperature of buildings is developed using the artificial neural network (ANN) concept. A four-month measurement campaign was conducted to obtain indoor temperatures of more than 50 buildings located on the island of Montreal. The area is then separated into 11 regions, each containing at least one of the measured buildings. The ANN model is then trained to be sensitive to the neighborhood's characteristics and LULC of each region. The surrounding radial area that influences the building's indoor temperature is first defined within an effective radius, by analyzing areas with radii ranging from 20m to 500m in 20m increments. Hence, the effective radius is found for each region to be within a radial area, where the environment beyond its limit does not significantly impact the building indoor air temperature. This technique trains a single model for the city, encompassing the unique characteristics of the sub-regions that contain buildings under study. An effective radius was established to lie within 320m to 380m. Analysing surrounding radial areas within this range enabled the network to effectively forecast future indoor conditions resulting from UHI effects, producing hourly indoor temperature predictions with an MSE of 0.68. Furthermore, the ability of the developed tool in the city planning is investigated with an additional case study.

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“…Thus there still are several researches in recent years to study and predict variations and trends of HDD and CDD at a particular location, such as China [2], London [3], and Turkish [4].…”

Section: Introductionmentioning

confidence: 99%

An Investigation of Cooling and Heating Degree-Hours in Thailand

Assawamartbunlue¹

2013

JOCET

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Abstract-The simplest well-known method that can be used to preliminarily estimate energy consumption of buildings is the degree-days method that usually requires the knowledge of either annual or monthly cooling and heating degree-days. In this paper, annual and monthly degree-days of 4 major cities in Thailand are investigated based on hourly temperature data in term of "degree-hours." Long-term hourly temperature data for 15 years (1994-2008) are used to calculate degree-hours at various base temperatures. The Sandia method is used to make annual hourly temperature dataset that can represent a typical hourly temperature year instead of using long-term average hourly temperature. The results show that Bangkok has the highest annual and monthly cooling degree-hours followed by Songkla, Ubonratchathani, and Chiangmai. In all cities, the number of cooling degree-hours is much more than one of heating degree-hours which implies that energy consumption of buildings is used for space cooling much more than space heating. Regression models are also developed for determining annual cooling and heating degree-hours at any base temperature.

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Heating and cooling degree day prediction within the London urban heat island area

Cited by 33 publications

References 28 publications

The comfort, energy and health implications of London’s urban heat island

The comfort, energy and health implications of London’s urban heat island

Urban neighborhood characteristics influence on a building indoor environment

An Investigation of Cooling and Heating Degree-Hours in Thailand

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