semi-automatic rooftop extraction to assess solar ...€¦ · the people living in the rural or...

INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

Semi-automatic rooftop extraction to assess

solar potential for smart cities

Mudit Kapoor1 , Rahul Dev Garg2

1,2Geomatics Engineering Group, CED, Indian Institute of Technology Roorkee, Roorkee-247667, Uttarakhand, INDIA

Email: [email protected]; [email protected]

Paper ID: 26

mailto:[email protected]

mailto:[email protected]

2 03-11-2018 2

PREVIEW

• RESEARCH OBJECTIVES

• STUDY AREA

• DATA USED

• METHODOLOGY

• RESULTS

• CONCLUSIONS

• ACKNOWLEDGEMENTS

GeoPreVi-2018 29-30 October, 2018 Bucharest, Romania

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RESEARCH OBJECTIVES

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• The objective of this research is to extract the building

rooftops of smart cities from the satellite images using a k-

means clustering algorithm to identify the usable area for

solar potential assessment.

• The scenes of WorldView-3 [Google Earth] are segmented

into nine parts and the algorithm implemented in Matlab is

applied to the individual parts for better utilization of

computing resources.

• Solar Potential assessment has been carried out using tilted

Global Horizontal Irradiance in Big data cloud environment.


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STUDY AREA

Figure 1 Study area selected for this research work GeoPreVi-2018 29-30 October, 2018 Bucharest, Romania

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DATA USED

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• High Resolution Image (HRI) from Google Earth (Google

Earth 2018)

• Socioeconomic data by India census 2011

• land use land cover (LULC) maps

• Pyranometer data &

• Global Horizontal Irradiance (GHI) from National Renewable

Energy Laboratory (NREL), United States


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METHODOLOGY

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Figure 2 Methodology adopted for the study


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ALGORITHM 1: Semi-Automatic Feature Extraction

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• k-means clustering (MathsWorks 2018)

– Input the coloured image (jpg format)

– Convert the image to RGB to L*a*b* color space

– Apply k-means algorithm to classify the colours in a*b* space

– Tag the pixels using k-means results

– Images have been produced using segments of H&E colours

– The result is a segmented image


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ALGORITHM 2: Tilted GHI

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Global solar irradiance on horizontal plane H

Solar Constant HSC

Astronomical and Geographical data, 𝛿, n s, 𝛚

Monthly average value of clearness index KT

Extra-terrestrial solar irradiance H0

HT

Ground reflectivity 𝝆𝒈 Diffuse solar irradiance on

horizontal plane Hd

Beam solar irradiance on horizontal plane Hb

HrRr HdRd HbRb


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STEP 1: Determine the value of declination (𝛿)

Declination (𝛿) = 23.45[sin360

365 (284+n)] ….(1)

STEP 2: Determine the value of sunset hour Angle (hs). hs= cos-1 [-tan (φ) tan (𝛿)] ….(2) STEP 3: Determine the value of average daily extra-terrestrial solar radiation (𝐇°)

H°= HSC[1+0.033(360n

365)][cos(φ) cos(𝛿) sin(hs)+ (

2πhs

360)sin (φ)sin(𝛿)]….(3)

HEX = HSC[1 + 0.033(360n

365)] …. (4)

where, HSC = 1353 w/m2 and n= day of year STEP 4: Determine the average monthly value of clearness index 𝐊𝐓

Clearness Index (KT)=H

Ho ….(5)

Where, Ho = extra terrestrial solar radiation. If KT= 1, then H=Ho, i.e. Hd= 0 (there is no diffused radiations)

SOLAR IRRADIANCE AT TILTED PLANE


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STEP 5: Find out the value of diffused radiation on horizontal surface (𝐇𝐝) Hd= [0.775+ 0.00653(hs-90) - {0.0505+ 0.0045(hs-90)} cos(115KT– 103]…. (6) STEP 6: Calculate the beam radiation Hb = 𝐻 − Hd…. (7) STEP 7: Calculate tilt factor for beam radiation (𝐑𝐛)

Rb =sin ɸ−s sin δ +cos ɸ−s cos δ cos(ω)

sin ɸ sin δ +cos ɸ cos δ cos(ω)…. (8)

STEP 8: Calculate tilt factor for diffuse radiation (𝐑𝐝)

Rd =1+cos(δ)

2…. (9)

STEP 9: Calculate tilt factor for reflected radiation(𝐑𝐫)

Rr =1−cos(δ)

2 …. (10)

STEP 10: Compute the value of total solar radiation on tilted surface HT = HbRb + HdRd + HrRr …. (11) Where, Hr = Hb + Hd ρg, ρg= reflectivity of ground

….SOLAR IRRADIANCE AT TILTED PLANE


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RESULTS

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Satellite Image

Extracted Rooftops


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…RESULTS

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Satellite Image

Extracted Rooftops


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…RESULTS

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Satellite Image

Extracted Rooftops


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…RESULTS

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Satellite Image

Extracted Rooftops


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ROOFTOP MAP

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Figure 3 Rooftop extracted from the satellite image using GIS analysis


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SOLAR POTENTIAL ASSESSMENT

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Year

Month/Year

GHI (kWh/m2/day)

Tilt Angle (degree)

Tilted GHI (kWh/m2/day)

Solar Potential (MWh)

GHI values published

in 2010

January 3.284 19.96 4.175 15.005

February 4.433 19.96 5.286 18.998

March 5.889 19.96 6.559 23.573

April 6.781 19.96 7.100 25.518

May 7.411 19.96 7.469 26.844

June 6.516 19.96 6.453 23.192

July 5.532 19.96 5.510 19.803

August 5.239 19.96 5.362 19.271

September 5.317 19.96 5.745 20.648

October 5.213 19.96 6.104 21.938

November 4.206 19.96 5.342 19.199

December 3.406 19.96 4.481 16.105

Annually 5.270 19.96 7.056 25.360

Table 1 Solar potential assessment over Har Ki Pauri rooftops


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…SOLAR POTENTIAL ASSESSMENT

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Date/Time

GHI

(Wh/m2) Tilt Angle (degree)

Tilted GHI

(Wh/m2) Solar Potential

(MW)

11-09-18 /14:18:53 724 19.96 738.74 2.655

Table 2 Solar Potential assessed using pyranometer data (instance)

# Houses Population Requirement (per capita, kWh) Total Energy consumption (MWh)

1 70 280 5.46 1.53

Table 3 Energy requirements of the selected study area using India Census 2011 data


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CONCLUSIONS

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• The main objective of this study was to estimate the usable

area and feasibility study of the solar plant at Har Ki Pauri,

India.

• The semi-automatic feature extraction approach helped in

extracting the rooftops.

• These extracted rooftops along with GIS analysis have been

utilized to calculate the rooftop’s area.

• The semi-automatic feature extraction approach helped in

extracting approximately 90% of the rooftops.

• The solar potential assessment and energy requirement

analysis of this location showed it is feasible to install SPV

panels for electric power generation.


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…CONCLUSIONS

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• GHI from pyranometer at the local level and GHI [NREL]

extracted using satellite images have been utilized in this

study.

• In this study, the semi-automatic feature extraction approach

has been combined with the feasibility study for solar

potential assessment using Big data cloud environment.

• These types of solar potential assessments help government

bodies in smart city policy making and providing subsidies to

the people living in the rural or hilly terrains.

• The results of this semi-automatic approach can be

improved by using higher resolution satellite image for

accurate prediction and assessment.


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…CONCLUSIONS

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• Vegetation, trees, shadows have not considered for this

study.

• These parameters can be taken into consideration in related

studies.


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ACKNOWLEDGEMENTS

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• I would like to thank International Federation of Surveyors

(FIG) for Young Surveyor Grant to attend and present this

paper.

• We are also thankful to the GeoPreVi-2018 International

Symposium ‘Geodesy for Smart Cities’ Organizing

Committee for the invitation and help to present our paper.


INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

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Thank you for your kind attention!


semi-automatic rooftop extraction to assess solar ...€¦ · the people living in the rural or...

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