Big Data in the smart building

Dr. Vincent Cheng, Director of the ARUP Building Sustainability

HEATING/

COOLING/

POWER

SMART

SYSTEMS

INTEGRATI

ON CENTRE

RECYCLING

FACILITY

GREY

WATER

RECYCLING

Functional spaces

Equipment

•Equipment Operation

•Chilled/Hot water temperature

•Pump Efficiency

•Chiller COP

•Fan Efficiency

•Recycling water pump operation

•Air temperature

•Relative humidity

•Precipitation

•Wind speed

•Air quality & pollution

•Solar irradiance

•Daylight level

•Air movement etc.

WEATHER STATION

INDOOR ENVIRONMENT

CONDITION

SENSORS

LIGHTING

OPERATION

PLUG LOAD

HOTWATER

•Load balance

•Chilled water pressure

•Fan duct pressure

•Etc.

•Occupants density

•Plug load/lighting operation hours

•Energy saving effects

•Daylight quality

DAYLIGHT QUALITY

Data

Identify/Visualize Performance

Feedback

Design Operation

Building Performance Diagnosis

Internal layout

Facade

Site Environment

Equipment

System

Site Measureme

nt

Data Acquisition

Data & Design

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Simulation TK2a

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

• Real energy data feed back to design to understand cooling profile

• Energy model calibration according to real building energy data to better understand the peak

load and design

Night time operation of essential power

currently unknown – operational of data

centers and server rooms are an important

consideration

High summer morning peak requires earlier

start up; morning cool down period shows

good scope for optimal start up control

strategy.

Exact occupation and equipment usage

unknown, estimates made from experience.

Energy consumption – Operational data feedback to design

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COP,ch COP,chp COP,as

COP,hvac COP,hrs COP,chw

High summer morning peak requires earlier

start up; morning cool down period shows

good scope for optimal start up control

strategy.

Building partially operate at night, chiller

sequencing can respond as such. Greater

potential for free cooling. Night time

operation of essential power currently

unknown – operational of data centers and

server rooms are an important consideration

Varying cooling loads caused by

occupancy/climate must be within variable

delivery range, else consider sequencing

Winter loads 30% of summer, negative façade

load in winter. Typical VAV fans have 40%

turn down (30Hz) – can sequencing help?

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Simulation TK2a

Jan Feb Mar Apr May Jun

Jul Aug Sep Oct Nov Dec

Heat rejection efficiency based on outdoor

temperatures. Water side economization can

provide small benefit

Energy consumption – Operational data feedback to design

• Constant occupancy values

• No variation

• No modeling of individual behavior

• Large step changes

• Unrealistic peaks and troughs

Deterministic

(current practice)

Occupancy modeling – Operational data feedback to design

• Constantly changing - each step simulates

new arrivals and departures

• 365 unique days

• Individual occupants simulated (agent-

based modeling)

• More realistic

• Requires data for accurate assumptions

Stochastic

(new practice)

Occupancy modeling – Operational data feedback to design

Deterministic Stochastic

Occupancy modeling – Operational data feedback to design

Data Collection

• Goal: Accurate understanding of building occupants and their energy usage behavior

Direct Indirect

• Employee card reader

• WLAN triangulation

• Bluetooth positioning

• CO2 sensors

• Acoustic sensors

• Motion detectors

• Plugloads power data

• Passive Infrared sensing

• Pressure mats

• Door contact sensors

• Computer Image Processing

Data & Operation

Measuring Principles of

Instrumentations

Instrumentation Set-up

Calibration

Data Recording

BMS

In-situ

Port Reservation

Site Measurement

Data Recording

Building usage: exhibition

Building type: low rise

Year of completion: 2012

32.4% of total building energy consumption is consumed

during 19:00-8:00 (13 hours) over the non operation period

mainly due to:

Not switching ventilation fans off during non operating

hours

Keeping unnecessary lighting on for closed hours

Leaving exhibition equipment on for non operation

hours

Energy consumption – Operational data feedback to operation

Morning

31%

Afternoon

37%

3%

6%

4%

3%

3%

3%Others

11%

Morning

Afternoon

Landscape Lighting

G/F Essential Lighting and Power

P&D Room

Chiller Plant Room lighting,

ventilation and socket pwer

M/F Light and Power

Basement Floor Light and Power

Others

13:00-19:00

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Expected annual saving:7.8kWh/GFA

March 2013 - July2014, Data provided by Siemens

• Preliminary study from the latest data reveals improvement in Building Energy Consumption

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Expected annual saving:13kWh/sqm

Non-Operating Hours Operating Hours in one equipment room (excluding equipment energy usage) (19:00-8:00)

Energy consumption – Operational data feedback to operation

Exterior Lighting (reduced operation hours)

Data driven energy saving actions

Data driven energy saving actions

• Building Energy Performance Analysis

• Retrieved the operation data from BMS system

• Correlated the building energy consumption data with the outdoor air dry-bulb temperature

• Analyzed the energy trends and breakdown

• Identified the Significant Energy Use to carry out further analysis to improve the building energy performance

• Variable frequency drive could not run below 30Hz imply that the fan speed variation is limited to 60%

• Over 80% of time the AHU are running in part load operation

• During part load condition (flow required <60%), VAV system is running as CAV by changing the supply air temperature

cooling coil

valve

opening

AHU fan

speed

return air

temperature

supply air

temperature8/4/2010 9:00 77.965775 49.25 22.577579 16.98361

8/4/2010 9:15 81.557053 49.25 22.47962 15.90556

8/4/2010 9:30 77.243431 49.25 22.406151 15.662999

8/4/2010 9:45 69.312622 49.200001 22.332674 15.501289

8/4/2010 10:00 61.864681 49.150002 22.332674 16.956657

8/4/2010 10:15 81.098984 49.262501 22.504112 16.228977

8/4/2010 10:30 79.828835 49.150002 22.406151 15.824709

8/4/2010 10:45 76.153412 49.162502 22.308189 15.770802

8/4/2010 11:00 70.990646 49.25 22.259205 15.662999

8/4/2010 11:15 64.27227 49.150002 22.21022 15.58214

8/4/2010 11:30 74.755142 49.25 22.381659 17.172272

8/4/2010 11:45 80.986435 49.150002 22.308189 15.90556

8/4/2010 12:00 78.351334 49.25 22.259205 15.824709

8/4/2010 12:15 74.347466 49.162502 22.185738 15.716896

8/4/2010 12:30 68.828056 49.25 22.161245 15.662999

8/4/2010 12:45 62.713444 49.25 22.161245 15.636045

8/4/2010 13:00 77.995255 49.25 22.381659 16.98361

8/4/2010 13:15 80.295502 49.200001 22.234715 15.797755

8/4/2010 13:30 75.184685 49.25 22.161245 15.636045

8/4/2010 13:45 67.456955 49.25 22.136753 15.555196

8/4/2010 14:00 67.162445 49.25 22.259205 17.522635

8/4/2010 14:15 81.296265 49.200001 22.283697 16.040316

8/4/2010 14:30 79.817642 49.25 22.161245 15.824709

8/4/2010 14:45 76.300941 49.25 22.112261 15.770802

8/4/2010 15:00 71.921432 49.25 22.087769 15.74385

8/4/2010 15:15 67.049049 49.25 22.063284 15.74385

8/4/2010 15:30 63.327194 49.225002 22.087769 15.824709

8/4/2010 15:45 61.598927 49.25 22.087769 16.040316

8/4/2010 16:00 62.922585 49.262501 22.063284 16.094221

8/4/2010 16:15 65.467865 49.174999 22.112261 16.202024

8/4/2010 16:30 68.797623 49.225002 22.136753 16.175072

8/4/2010 16:45 72.079834 49.25 22.136753 16.175072

8/4/2010 17:00 75.048363 49.212502 22.112261 16.148119

8/4/2010 17:15 77.634743 49.174999 22.136753 16.148119

8/4/2010 17:30 79.505569 49.25 22.112261 16.067268

8/4/2010 17:45 79.695908 49.150002 22.112261 15.986409

8/4/2010 18:00 78.652924 49.237499 22.087769 15.90556

cooling coil

valve

opening

AHU fan

speed

return air

temperatur

e

supply air

temperatur

e1/19/2011 9:00 37.889057 29.0875 21.716301 19.813492

1/19/2011 9:15 33.176193 29.5 21.814262 18.708488

1/19/2011 9:30 38.337078 29.512501 21.936714 17.711298

1/19/2011 9:45 34.134567 29.7125 21.961206 19.570932

1/19/2011 10:00 38.973686 29.825001 22.010183 17.684345

1/19/2011 10:15 32.93779 30.174999 22.108152 17.468737

1/19/2011 10:30 41.437687 30.387501 22.132637 19.840446

1/19/2011 10:45 32.479179 30.5 22.132637 19.54398

1/19/2011 11:00 37.662056 30.6 22.181622 17.333981

1/19/2011 11:15 36.18705 30.512501 22.230604 18.708488

1/19/2011 11:30 36.158897 30.8125 22.230604 18.978003

1/19/2011 11:45 39.543282 30.9125 22.279581 17.846054

1/19/2011 12:00 32.943344 31.3375 22.279581 19.274467

1/19/2011 12:15 42.142628 31.637501 22.279581 17.765194

1/19/2011 12:30 32.653709 31.737499 22.304073 19.355326

1/19/2011 12:45 37.566998 31.9 22.255089 17.846054

1/19/2011 13:00 40.141308 32.037498 22.304073 18.412031

1/19/2011 13:15 33.820251 32.112499 22.304073 19.031908

1/19/2011 13:30 41.763931 32.525002 22.353058 17.846054

1/19/2011 13:45 39.216148 32.75 22.377542 19.328373

1/19/2011 14:00 35.564407 32.112499 22.426527 17.926905

1/19/2011 14:15 37.113998 32.637501 22.451019 19.058861

1/19/2011 14:30 40.393848 32.637501 22.451019 18.061661

1/19/2011 14:45 33.256248 32.200001 22.402035 18.924105

1/19/2011 15:00 40.222439 32.212502 22.426527 18.250322

1/19/2011 15:15 35.789734 32.012501 22.402035 18.088614

1/19/2011 15:30 40.766705 32.012501 22.426527 19.247515

1/19/2011 15:45 34.190948 31.799999 22.402035 18.169464

1/19/2011 16:00 35.897926 31.9125 22.377542 18.546787

1/19/2011 16:15 40.962017 31.799999 22.402035 18.600685

1/19/2011 16:30 33.536819 31.799999 22.402035 18.600685

1/19/2011 16:45 42.230934 31.799999 22.499996 18.600685

1/19/2011 17:00 32.716415 31.799999 22.499996 18.654593

1/19/2011 17:15 39.922962 31.700001 22.451019 18.196417

1/19/2011 17:30 35.909409 31.9 22.377542 18.681545

1/19/2011 17:45 37.202065 32.112499 22.426527 18.250322

1/19/2011 18:00 38.989365 32.125 22.377542 18.115566

Summer Winter

Energy consumption – Big data help choose the best suited equipment

Chiller Plant Operation Analysis• Retrieved data from BMS system

• Correlated the entering condensing water temp. with chiller COP

• Come up with preferred operation sequence of the chiller systems to minimize the energy consumption

Chiller Plant Operation Analysis• Retrieved data from BMS system

• Analyzed the impact of operation hour on plant energy efficiency

• Identified that the balance of individual chiller operation is important to maintain high energy efficiency

Chilled Water Temperature Distribution Analysis

• Measured the chilled water temperature at different

mechanical floors

• Analyzed the chilled water pipework insulation conditions

• Identified the more significant insulation issue

6

7

8

9

10

11

-3 7 17 27 37T

emp

erat

ure

Floor Level

Chilled Water Temperature Distribution

• Artificial Lighting Analysis

• Measured the indoor lux level distribution

• Verified with Simulation Model

• Applied energy efficient measures to improve the energy efficiency and maintain/improve indoor visual comfort

• To reduce the energy consumption of lighting system

Verified

BEFORE AFTER

• PV area 1050 m2

• PV rated power ~ 152 kW

• PV performance tally with solar radiation and cloud cover data

• PV output ~ 17.1kWh/m2 during October-December

050

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ut

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h)

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Daily  Global solar Radiation (MJ/m2) Mean Amout of Cloud (%)

October-December

Weather Data (Recorded)

PV Output (Recorded)

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Output vs. Daily Global Solar Radiation

PV panel Analysis

• Cooling Tower Analysis• Realized short-circuiting problem on site

• Carried out CFD analysis to analyze the problem that would reduce the energy efficiency of the chiller system

• Proposed retrofit recommendations to improve the problem.

WAY FORWARD – INTEGRATION?

Designer

Operator

What meters are installed?

What does it measure?

How to use the data?

What data to feed back?

Poor process – Industry norm in East Asia

Supplier

WAY FORWARD – INTEGRATION?

Operator

(operation consult?)

Supplier

Designer

Better Integration

Equipment data

feedback to

Analysis& Design

Data

Equipment data

feedback to

operatorOperator FEEDBACK

to supplier, to drive

innovation in metering

technology

?