Cross indicator analysis between wind energy potential and urban morphology

Wang, B.; Cot, L. D.; Adolphe, Luc; Geoffroy, Sandrine; Sun, Shuai

doi:10.1016/j.renene.2017.06.057

Cited by 47 publications

(26 citation statements)

References 31 publications

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“…It could be inferred that the directions of buildings and streets and types of spatial allocation of buildings make a difference because they influence the wind environment significantly. This matched the findings in [28]. In addition to the impacts of wind environment, emissions on roads could also influence air pollutants concentrations, interacting with local wind environment [29].…”

Section: Discussionsupporting

confidence: 83%

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

Ku¹

2020

Sustainability

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The deterioration of air quality in urban areas is often closely related to urbanization, as this has led to a significant increase in energy consumption and the massive emission of air pollutants, thereby exacerbating the current state of air pollution. However, the relationship between urban development and air quality is complex, thus making it difficult to be analyzed using traditional methods. In this paper, a framework integrating spatial analysis and statistical methods (based on 170 regression models) is developed to explore the spatial and temporal relationship between urban land use patterns and air quality, aiming to provide solid information for mitigation planning. The thresholds for the influence of urban patterns are examined using different buffer zones. In addition, the differences in the effects of various types of land use pattern on air quality were also explored. The results show that there were significant differences between 1999 and 2013 with regards to the correlations between land use patterns and air pollutant concentrations. Among all land uses, forest, water and built-up areas were proved to influence concentrations the most. It is suggested that the developed framework should be applied further in the real-world mitigation planning decision-making process Sustainability 2020, 12, x FOR PEER REVIEW 3 of 16 (AWMPFD), Shannon's diversity index (SHDI), and Shannon's evenness index (SHEI) of residential 95 areas were highly correlated with acoustic noise level and PM10 concentration. Furthermore, higher 96 ED and PD of built-up areas led to higher acoustic noise levels and PM10 concentration. Detailed 97 definition of the landscape metrics mentioned above could be found in [5,16,19]. 98In order to gain a deeper understanding of the effectiveness of strategies for improving future 99 urban air quality, integrated research methods, including the quantification of urban spatial patterns 100 and structures and the correlation analysis between air quality and urban form, should be carried 101 out. In view of this, a spatiotemporal analytical framework is constructed in this study, which 102 involves first a correlation analysis between land use type indicators and air quality indicators at 103 different spatial distances, followed by multiple sets of regression analysis based on data from 104 different years. Our aim is to clearly quantify the correlation between different land use patterns and 105 air quality in cities, which can serve as a reference for the future formulation of mitigation and 106 adaptation planning strategies. This study will calculate the land use patterns within the different 107 buffer radii of air quality monitoring stations and will explore the association between urban land 108 use patterns and air quality using statistical analysis in order to serve as an important reference for 109 the formulation of adaptation strategies related to air quality, which can benefit future land use 110 planning. This proposed framework aims to provide useful information for public sec...

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Section: Discussionsupporting

confidence: 83%

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

Ku¹

2020

Sustainability

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“…[6]. Progress in the use of renewable energy (RE) is an imperative part of realizing the sustainable and smart cities of the future [7]. Since 1997, renewable energy sources have been gaining more attention with the adoption of the Kyoto protocol [8], and the focus is now on harnessing renewable sources of energy such as solar, wind, geothermal, hydropower, and tidal [1,9].…”

Section: Introductionmentioning

confidence: 99%

“…Among the different sources of energy, wind energy is gaining popularity because of technological developments, easy installation of wind turbines, efficiency, long life, and smaller space requirements relative to solar power harnessing [11,12]. Many studies have been conducted on installing wind energy in different terrains such as in urban environments [7,13], wind farms [14], and offshore [1,15]. However, there are several environmental issues also associated with energy such as ecological disturbance, wildlife safety, visual pollution, noise, electromagnetic interference, and local climate change [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…MCDM approaches have been widely used in different fields such as site selection for electrical grids for hybrid renewable energy systems [31], location selection of shopping malls [32], and for ranking sustainable sugarcane farms [33]. Wang et al [7] presented a comprehensive literature survey about the application of multi-criteria decision analysis in the decision making process of sustainable energy development. Ioannou et al [34] used the soil and water assessment tool (SWAT) and hydrologic simulation model for predicting the streamflow data.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Criteria Decision-Making Approach for Selecting Wind Energy Power Plant Locations

et al. 2019

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In pursuit of green technology innovations, the energy industry is showing an interest in sustainable sources such as wind energy generation. The Saudi Arabian energy industry has a 2030 target to generate and transmit electricity to major customers nationwide and other neighboring Gulf countries. However, the selection of wind energy power plant locations is a concern because the decision process involves social, technological, economical, and environmental factors. The originality of this study lies in (1) proposing an integrated quantitative and qualitative multi-criteria decision making framework for selecting wind-energy power plant locations; (2) applying the proposed framework in the context of the energy industry in a gulf region country and investigating expert-based and entropy-based criterion weight assignments; (3) choosing five possible alternative wind energy power plant locations with 17 response criteria for each alternative to help decision makers identify the best possible alternative; and (4) establishing the superiority of one alternative over another (if it exists). The presented approach extends considerable support to the comparing and ranking of alternatives along with its validation and sensitivity analysis. Based on the proposed multi-criteria decision-making approach, an appropriate wind energy power plant location has been successfully selected among the five alternatives.

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“…Except for a few studies [44], research on wind flow at the urban scale is mostly limited to urban comfort studies [45]; these have evaluated a limited range of constant average values [46] or urban wind at the pedestrian level around buildings regardless of energy demand simulations [47]. There are several studies which have used coupled CFD-BES models to evaluate microclimate conditions considering wind and temperature variations [48].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

Javanroodi

Nik

2019

Buildings

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Urbanization trends have changed the morphology of cities in the past decades. Complex urban areas with wide variations in built density, layout typology, and architectural form have resulted in more complicated microclimate conditions. Microclimate conditions affect the energy performance of buildings and bioclimatic design strategies as well as a high number of engineering applications. However, commercial energy simulation engines that utilize widely-available mesoscale weather data tend to underestimate these impacts. These weather files, which represent typical weather conditions at a location, are mostly based on long-term metrological observations and fail to consider extreme conditions in their calculation. This paper aims to evaluate the impacts of hourly microclimate data in typical and extreme climate conditions on the energy performance of an office building in two different urban areas. Results showed that the urban morphology can reduce the wind speed by 27% and amplify air temperature by more than 14%. Using microclimate data, the calculated outside surface temperature, operating temperature and total energy demand of buildings were notably different to those obtained using typical regional climate model (RCM)–climate data or available weather files (Typical Meteorological Year or TMY), i.e., by 61%, 7%, and 21%, respectively. The difference in the hourly peak demand during extreme weather conditions was around 13%. The impact of urban density and the final height of buildings on the results are discussed at the end of the paper.

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Cross indicator analysis between wind energy potential and urban morphology

Cited by 47 publications

References 31 publications

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

Exploring the Spatial and Temporal Relationship between Air Quality and Urban Land-Use Patterns Based on an Integrated Method

Multi-Criteria Decision-Making Approach for Selecting Wind Energy Power Plant Locations

Impacts of Microclimate Conditions on the Energy Performance of Buildings in Urban Areas

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