The appl icat ion of The Screen ing Tool for Estate Environment Evaluat ion (STEVE) SketchUp Plug in as an urban microcl imate tool for urban planners

Erna TanDr. Steve Kardinal Jusuf

Dr. Marcel IgnatiusProf. Wong Nyuk Hien

erna7tan@nus.edu.sgDepar tment of Bui ld ingSchool of Design and Environment4 Architec ture Dr ive SDE 2Singapore 117566

Background• STEVE model has been developed empirically to predict air temperature in estate level of Singapore based on climate

condition as well as urban morphology characteristics. The main intention is to develop a simple or user-friendly tool for urban planners in order to understand the impact of urban microclimate to their urban planning and vice versa.

• Throughout the years, this model has been enhanced and developed as plugin of 3D modelling software. The latest development is as a plugin in SketchUp.

• The STEVE Tool plugin in SketchUp provides the users with temperature maps and profi les of the area, which include maximum, average, minimum, average daytime and average nighttime temperature

• These maps consider various parameters such as solar radiation, ambient air temperature, wind speed, pavement, building surface, building density, greenery, and albedo.

• The plugin has also incorporated database of greenery so that carbon sequestration of greenery planned in the area can calculated.

• The prediction models have been validated with fi eld measurement in Singapore at various locations .

Case Study• Jurong Lake District area, located in South West Singapore, has been chosen to showcase the implemenation of the

tool.

• Thermal comfort of the area will also be discussed based on the empirical outdoor comfort model developed for Singapore climate.

• Greenery calculation is based on Green Plot Ratio concept, a primary metric used to measure greenery in an area using leaf area index (LAI) variable.

• The STEVE tool has been embedded with extensive plants database (Singapore context) which cover various type with diff erent LAIs.

Air temperature of a point at a certain height level is the function of the local climate characteristics, which deviates according to the surrounding urban morphology characteristics (building, pavement and greenery) at a certain radius.

Prediction Models

Integrated microclimate analysis tool

SketchUp Plugin

Climate predictors

Ref Tmin = Daily minimum temperature at reference pointRef Tavg = Daily average temperature at reference pointRef Tavg (daytime) = Daily average temperature at daytime (7am – 6pm)Ref Tavg (nighttime) = Daily average temperature at night (7pm – 6am) Ref Tmax = Daily maximum temperature at reference pointSOLARtotal = Total of daily solar radiationSOLARmax = Maximum of daily solar radiationWindmax = Wind speed at the time of occurrence of Ref Tmax

Urban morphology predictors

PAVE = Percentage of pavement area over R50m surface areaAVG HEIGHT = Average buildings heightHBDG = Average buildings height to building area ratioWALL = total wall surface areaGnPR = Green plot ratioSVF = Sky view factorALB = Average surface albedo

INPUTINPUT3D Model3D Model

BuildingsRoads/Pavement

Greenery

STEVE ToolSTEVE ToolPluginPlugin

INPUTINPUTBackground Background

ClimateClimate

CarbonSequestration

Analysis Features

Grid SizingCanvassing + ZoningTemperature Profile

Dynamic Temperature Probing

Export heat maps imageExport data into Excel

Heat maps

Tmax, Tmin, TavgTavg daytime

Tavg nighttimeWINDAlbedo

Sky View Factor calculation

TEMPERATURE MAP

TEMPERATURE MAP

OUTDOOR THERMAL COMFORT

• The STEVE tool is also capable on generating temperature profi le based on the section line created by the user.

• User is able to analyze temperature behav-iour due to the sur-roundings.

• The information pro-vided comprises the minimum up to the maximum outdoor temperature.

• The initial concept is to devel-op an integrated analysis tool which consider various micro-climatic aspects in the urban area.

• The platform uses the concept of urban climatic map (UCM).

• The initial process have always been looking at real time ur-ban climate monitoring and urban parameters.

• The fi rst develop component is the outdoor temperature pre-diction model.

(in d

eg C

)

(grid cell number)

Real-time urban climate monitoring

Urban Parameters

DATA – Land Use

DATA – Inhabitant

Façade Solar Insolation

Solar Exposure

Urban Ventilation

Urban Temperature

Simulation model

Input

Urban Thermal Comfort

Energy Consumption

Urban Pollution

Urban Glare

Analysis

… …

UCM E

UCM D

UCM C

UCM B

UCM A

Others

Output

Urban Temperature