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Maria Kolokotroni

Researcher at Brunel University London

Publications -  113
Citations -  3878

Maria Kolokotroni is an academic researcher from Brunel University London. The author has contributed to research in topics: Urban heat island & Ventilation (architecture). The author has an hindex of 28, co-authored 109 publications receiving 3269 citations. Previous affiliations of Maria Kolokotroni include University College London & Building Research Establishment.

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The effect of the london urban heat island on building summer cooling demand and night ventilation strategies

TL;DR: In this paper, the authors investigated the effect of increased air temperature due to the London heat island has on the effectiveness of stack night ventilation strategies for office buildings and found that a rural optimised office would not need any artificial cooling and would be able to maintain temperatures below 24 °C.
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Urban heat island intensity in London: An investigation of the impact of physical characteristics on changes in outdoor air temperature during summer

TL;DR: In this paper, the potential of physical characteristics in mitigating the urban heat island intensity (UHI) in London during summer was investigated using six on-site variables namely aspect ratio, surface albedo, plan density ratio, green density ratio and thermal mass.
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London's urban heat island: Impact on current and future energy consumption in office buildings

TL;DR: In this article, the authors presented the results of a computational study on the energy consumption and related CO2 emissions for heating and cooling of an office building within the Urban Heat Island of London, currently and in the future.
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Cooling-energy reduction in air-conditioned offices by using night ventilation

TL;DR: In this paper, the applicability of night ventilation in air-conditioned office buildings was investigated and the potential for energy savings and/or improved internal comfort conditions by applying night ventilation cooling was examined.
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The London Heat Island and building cooling design

TL;DR: In this article, the authors used an Artificial Neural Network (ANN) model to predict location specific hourly temperatures for London, taking into account radial distance from central London, hourly air temperature measured at the meteorological station and associated synoptic weather data.