application of agent based modelling for thermal comfort

Kapil SinhaIndian Institute of Technology Roorkee India

Application of agent based modelling for thermal comfort and energy efficiency studies in airport terminal buildings.

Dr. E. RajasekarIndian Institute of Technology Roorkee, India

Speaker : Guide :

INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

1

Application of agent based modelling for thermal comfort and energy efficiency studies in transport terminal buildings.Kapil Sinha 2

INTRODUCTION

Transient Occupancy

Wider range of metabolic rates

Large Volume

Heat Gains

Terminal Buildings are usually characterized by following (What makes the Airport different from other buildings ?)


OBJECTIVES

• Evaluate the dynamics of passenger occupancy at different zones in terminal building during arrival and departure sequences

• Estimate the HVAC energy demand using the dynamic occupancy profiles obtain through ABM


METHODOLOGY

Pedestrian Flow Simulation Module

Pedestrian• Walking

Speed• Sequence

Building Layout

Arrival Rates

Arrival Departure Schedule

Energy Simulation Module

Building Physical Model

Lighting Types

Pedestrian Occupancy Schedule

Energy Demand

Prediction

Route Choice Policy

Airlines• No. of Gates• Gate Opening Time

Pedestrian Occupancy Schedule

Construction Types

Thermal Types

Weather Files


DETAILS OF THE STUDY

Visakhapatnam, India | 17.68° N, 83.21° E | Medium Sized Airport


PASSENGER SEQUENCEDEPARTURE PROCESS

ARRIVALPROCESS

Entry check

Visitor concourse

Check-in counter

Security check

Hold-room assignment

Hold-room

Boarding gate

Airside Corridor

Level change

Carousel assignment

Baggage carousel

Custom check

Exit gate

Flight Agent

Flight Agent


PASSENGER SEQUENCEDEPARTURE PROCESS


PASSENGER SEQUENCEARRIVAL PROCESS


SERVICE POINTSSTOCHASTIC MODELLING

Service Points No. of Counter

Probability Distribution

Parameter(in minutes)

Departure Entry Gates

3 Uniform a=0.25, b=0.52

Check-in Counters 20 Normal µ=3.8, σ=0.36Domestic Security Checks

4 Normal µ=2.8, σ =1.4

Departure Immigration

6 Normal µ=2.1, σ=0.25

International Security Checks

3 Normal µ=3, σ=1.5

Arrival Immigration 5 Normal µ=3, σ=1.1Customs Counter 4 Normal µ=1.5, σ=1.2Baggage carousel 3 Normal µ=8.4, σ=2.3

𝑇 𝜏 𝜏

𝜏 = time at which passengerreaches the service counter

𝜏 = time at which passenger leavethe counter


DECISION POINTSCHOICE MODELLING

Choice Event Abbr. Options

Curb-side 𝐶 wait/serve

Visitor Concourse 𝐶 Visitor/passenger

Passenger concourse 𝐶 Restaurant/washroom/wait

Check-in type assignment 𝐶 Online/counter/baggage

Check-in counter assignment 𝐶 Airline counters

Travel Type 𝐶 Domestic/International

Boarding floor assignment 𝐶 Ground floor / First floor

𝐶

𝑤𝑎𝑖𝑡 𝑠𝑒𝑟𝑣𝑒

𝐶

𝑣𝑖𝑠𝑡𝑜𝑟 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟

P 𝑤𝑎𝑖𝑡 P 𝑠𝑒𝑟𝑣𝑒

P 𝑤𝑎𝑙𝑘𝑠𝑡𝑜𝑝

𝐶


AGENT MODELLING

Walking Speed (metre/seconds)µ=0.7, σ=0.37

Decision variablesGenderFlight numberClass

Storage variablesWaiting TimeWalking Time

Check-in typeCheck-in counterHold-roomBaggage belt


MODEL SUMMARY

Dwell Time

282420161284

10

8

6

4

2

0

Check-In Hall

Dwell Time

Security Hall Domestic

32282420161284

7

6

5

4

3

2

1

0

Dwell Time

Security Hall International

32282420161284

8

7

6

5

4

3

2

1

0

DO

MES

TIC

INTE

RN

ATIO

NAL

Service Time

Check-In Counter

Service Time

Domestic Security Counter

International Security Counter

Service Time

Passenger Arrival Rate

Walking Time

Concourse

21181512963

600

500

400

300

200

100

0


SIMULATIONS

151413121110987654321

600

500

400

300

200

100

0

Simulation run

Occ

upan

t Cou

nt

Check-in Zone


PEDESTRIAN DENSITY MAP

1.8 p/m2

0 p/m2


DWELLING TIME

Immigration Arrival

International Baggage

Domestic Baggage

International Bus Lounge

Domestic Bus Lounge

International Holdroom

Domestic Holdroom

Immigration departure

Security International

Security Domestic

Check-in Hall

706050403020100Dweling Time (in minutes)

Zone µdwell time

Check-in Hall 12.4Security Domestic 13.8Security International 10.0Immigration departure 6.0Domestic Hold-room 28.5International Hold-room 35.1Domestic Bus Lounge 26.4International Bus Lounge 28.4Domestic Baggage 7.8International Baggage 11.5Immigration Arrival 2.8


OCCUPANCY PROFILE


INTERNAL HEAT GAIN FROM OCCUPANTS

Walking295 W/person

Standing145 W/person

Sitting115 W/person

Occupant Heat Gain | Typical summer weekday

Visakhapatnam Airport Floor PlanACTIVITY ZONING

First Floor

Ground Floor


OCCUPANCY PROFILE

0

50

100

150

200

250

300

350

0:00 2:00 4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Sensible Coo

ling De

mand (kW)

Flight Schedule Pedestrian Simulation


ANNUAL COOLING LOAD

100

200

300

400

500

600

700

800

900

1,000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Cooling Load

(MWh)

Airport Operation Flight Schedule Simulated Passenger Schedule

Annual Cooling Demand (MWh)

24 Hrs. Operation 8,924

Operation control with Flights Schedule

7,872

Operation control with Passenger Schedule

5,687 (- 27%)


PASSENGER DENSITY vs. COOLING DEMAND

Zone R2

Security domestic 0.83International bus lounge 0.74

International hold-room 0.70

Domestic bus lounge 0.69

Check-in hall 0.59Domestic hold-room 0.46Security international 0.42

Immigration departure 0.32

Domestic baggage 0.29International baggage 0.25

Immigration arrival 0.18

y = 19.522x + 2.7082R² = 0.8368

0

2

4

6

8

10

12

14

0 0.1 0.2 0.3 0.4 0.5 0.6Cooling De

mand (kW)

Passenger Density (person/m2)


CONCLUSION

• The study shows occupancy schedule has potential to reduce the peak demand in the terminal buildings.

• The results obtained through ABM simulation are useful for developing set-point values for the HVAC and lighting controls for the different levels of occupancies among the zones

27% 22% 40%SUMMERANNUAL WINTER

COOLING DEMAND REDUCTION

Kapil SinhaIndian Institute of Technology Roorkee India

Application of agent based modelling for thermal comfort and energy efficiency studies in transport terminal buildings.

[email protected]+91 9806181263

Speaker : Contact :

INDIAN INSTITUTE OF TECHNOLOGY ROORKEE

22

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application of agent based modelling for thermal comfort

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