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Master’s Thesis Presentation
Milan Jain
Advisor:
Dr. Amarjeet Singh
Committee Members:
Dr. Amarjeet Singh (Chair)Dr. Pushpendra Singh (Internal)
Dr. Zainul M Charbiwala (External)
Predicting AC Consumption Minimizing Aggregate eNergy cost
3
29%[1]
Challenging Electricity Demands
[1] https://www.iea.org/publications/freepublications/publication/Indicators_2008.pdf
21%[1]
5
6
(30-50)%[1]
AC Electricity Consumption
7
Awareness – How much AC Consumes??
8
There are ways to measure that…
[1] AC Rated Power: 2000W Duration: 2
Hours9
Indirect Sensing (NILM)
Direct Sensing
Energy consumed/Day
(AC): 4 KwH[1]
10
Can I optimize
this?
Now What?
Optimize AC Performance
11
Detect Occupancy and Actuate
Switched on while it has to be Off
Switched Off while it has to be On
HVAC Actuation
12
Unanswered Questions…
13
What is the current temperature of my
room?
Temperature set is achieved? – In how
much time?
Can I save if I use lower set
temperature?
How to reduce energy bill from
AC?
Do I need to use AC?
14
Forecast Energy
Consumption of AC
Room Temperature in Real Time
Outside Weather
Conditions in Real Time
Energy Consumed by
AC
Analyze, Learn and Enhance
15
User Intervention
Static Learning
No Predictions
Predict
Real Time Feedback
Analyze
16
PACMAN
17
What does PACMAN
exactly do?
26
28
30
Tem
pe
ratu
re (°C
)
20
25
30
35
Tem
pe
ratu
re (°C
)
20
25
30
35
Te
mp
era
ture
(°C
)
18
PACMAN Overview
19
Before that, how does AC Works?
How will PACMAN predict my AC consumption??
How can PACMAN optimize my AC consumption??
Air Conditioning Unit
21
Comprising of multiple compressor cycles
AC Usage
22
Cooling Rate Leakage Rate
Compressor On Temperature
Compressor Off Temperature
Factors Affecting AC Usage
Thermal Noise
Doors
Human Activity
Occupancy
Window
Sensor Position
24
Compressor On & Off Temperature
Variation based on AC Usage
Variation based on AC Set Temperature
25
Learn Thermal Model
26
Predict Compressor Status
27
Who did these things in
PACMAN?? – How PACMAN is organized??
28
PACMAN Architecture and its Components
Why should I trust
PACMAN??
9Rooms
7Homes
2200 Hours3 Months
6Within IIITD Campus
1Outside IIITD Campus[1] For detailed experiment summary,
please refer to Thesis document
Validate PACMAN[1]
[1]
http://occupations.phillipmartin.info/occupatio
ns_weather_forecaster2.htm
[2] http://www.wunderground.com/
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Experimental Setup
Room Temperature
Weather Information[2]
Power Consumption – Smart Meter
32
Data Validation
Validation of NILM approach.
Extract AC compressor cycles from
meter data and validated with power
measurements from jPlug
Validation of weather data with
actual temperature in the campus.
Validation of weather forecast with
historical weather data from same
weather service.
33
Prediction Accuracy
34
Prediction Accuracy
35
Where PACMAN went wrong?
36
Outliers – Irregular Thermal Behavior
37
Outliers – External Temperature Variation
38
Better User Control
39
PACMAN Realization
40
Z-Wave NodesHSK-200Z(IR Sensor)
HSK-48(Z-Wave Gateway)
Power Monitor
41
TCIL Project - UI
42
Future Directions
User Study to learn impact of PACMAN
on AC Energy Consumption
Enhance Thermal Model by including Multiple
Thermodynamic Parameters
PACMAN Demo
Questions??
THANK YOU
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