Conference Agenda

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Session Overview
Poster introductions 06: Urban environment, outdoor environment
Thursday, 26/Aug/2021:
11:35am - 12:00pm

Session Chair: Prof. Eva B Møller, Technical University of Denmark
Location: Room 4 - Room 015, Building: 116

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11:35am - 11:38am

Generating Residential Layout Based on AI in the View of Wind Environment

Xiaoyu Ying1,2, Xiaoying Qin1,2, Jiahui Chen1,2, Jing Gao1,2

1Zhejiang University; 2Zhejiang University City College

In pursuit of the goal of high-quality development of living environment, architects have been balancing the contradiction between high-density residential development form and the comfortable outdoor physical environment. The existing studies on wind environment of high-rise residential areas only provide guidance for the simple general layout, which cannot cope with the fact that the monomers are mixed arrangement of various sizes of point and slab blocks in most high-rise residential areas. This paper presents a new tool - "Automatic generation and comparison software of high-rise residence layout based on wind environment prediction", the software integrates three modules: the automatic generation of high-rise residential layout, the simulation of wind environment and the comparison for optimum. After several generations of calculation, the optimal solution of high-rise residential layout under specific plot ratio and plot conditions is obtained, which provides help for obtaining the performance guidance of human living environment in the rapid pace of architectural design.

11:38am - 11:41am

Development of a predictive urban heat island model using vertical aspect of cities

Victor George Equere1, Parham A. Mirzaei1, Saffa Riffat1, Yilin Wang2

1University of Nottingham, United Kingdom; 2University College London, United Kingdom

Urban heat island (UHI) impact on various aspects of urban climates. Previous remote sensing-based studies have demonstrated a significant correlation between the urban morphology indices and the UHI formation, however, they have barely taken the impact of vertical parameters into the account.

This study aims to improve the remote sensing-based surface UHI prediction approach by integrating the impact of vertical features and topographical aspects into an artificial neural network (ANN). Along with conventional morphological indices, including (NDVI and NDBI), to train and develop a predictive model that is sensitive to the spatial distribution of the UHI indicated by land surface temperature (LST), new vertical parameters are defined and employed in this study. All parameters used for the training process were derived from a case study in Illinois, USA where high-quality remote-sensing and climatic factor data are available. The developed model was then applied to predict the LST for a section of the city, which was not initially included in the training process. The results prove a significant improvement in the surface UHI prediction by integration of the proposed factors.

11:41am - 11:44am

Mitigating and adapting to climate change with a taxonomy of smart urban surfaces

Zekun Li, Vivian Loftness

Carnegie Mellon University, United States of America

Rapid urbanization is replacing natural land with dark, impervious surfaces. This has led to dire urban consequences including rising temperatures and stormwater deluge, resulting in significantly higher energy costs, greater stormwater damage, and associated health and comfort impacts. These issues can be mitigated using smart surfaces, those with high reflectivity and permeability, as well as increased landscape and photovoltaics, which can achieve sustainable and regenerative cities. The current literature on the benefits of urban surfaces is very segmented, focusing on either one specific surface type or one property of surfaces. A smart surface taxonomy with correlated heat, water and carbon metrics has been developed to fill this gap. A range of city surfaces in three broad categories - roofs, streets and sidewalks, and parking lots - have been identified with various levels of reflectivity, permeability, and carbon impacts. Through literature review that correlates reflectivity and urban heat, the taxonomy reveals surface temperatures that range from 29.7oC for a green roof to 74.3oC for a black roof. Through literature review that correlates percolation and porosity, the taxonomy reveals Rainfall retention potential ranging from 1.27 mm for impervious pavement to 86.4 mm for bioswales, with subsurface storage offering even higher potential. The development of a smart surface taxonomy with quantified benefits for mitigating or adapting to climate change will be critical for decision-makers to make informed decisions on city surface choices.

11:47am - 11:50am

Application of the TEAC software for analysis of Energy Flexible Building Clusters – a case study

Marcin Zygmunt, Dariusz Gawin

Lodz University of Lodz, Poland

Nowadays, natural environment protection and sustainable development became common and necessary issues for all the economic sectors. It is extremely important to focus on all the efforts resulting in the most efficient and sustainable power sources and electric power grid. Typically, the residential districts are connected by electric grids, which with an application of the appropriate technologies might be considered as so-called smart-grids. In the smart-grid neighbourhoods, houses are the consumers, energy supply is performed by the local or/and national power plants, while energy distribution is performed using some monitoring and management systems. Such a residential area can be considered as a Building Cluster, the novel paradigm in the energy and environmental analysis of the built environments. In this article, the exemplary single-family houses neighbourhood is examined, following the Building Cluster paradigm. The analysed area is located in Lodz (Poland), consisting of 202 buildings. The study is performed by means of the home-developed software named TEAC (Tool for Energy Efficiency Analyses of an Energy Cluster). The analysis is focused on the energy, economic and environmental issues of the considered Building Cluster.

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