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NON INTRUSIVE LOAD MONITORING
1. Non-Intrusive Load Monitoring
Center for Energy and Environment, MNIT
Submitted by
Sai Goutham Golive
2014pcv5192
Submitted to
Prof. Jyotirmay Mathur
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2. Contents
Background
Introduction
General Frame Work Of NILM
Data Acquisition
Feature Extraction
Load Identification
System Training
Challenges
Conclusions
References
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3. Background
Energy conservation is a challenging issue
Global energy demands double by end of 2030 with negative impacts on the
environment
Energy crisis, climate change and the overall economy of a country affected
by the growth in energy consumption
Reduction in energy wastage can be achieved through monitoring of energy
consumption and relaying of this information back to the consumers
Goal of ALM (Appliance Load Monitoring) is to perform detailed energy
sensing and to provide information on the energy spent
ALM leads to identification of high energy consuming appliances
- peak to off-peak
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4. Why does it matter?
Improve relationships with customers
.
Understand customer behavior to
improve capacity planning
Identify appliances that could
participate in Demand Response
Understand your bill
Plan your monthly budget
Be able to make a financial
decision for when to use an
appliance
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Utilities Customers
5. Introduction
Two major approaches to ALM
- Intrusive Load Monitoring (ILM)
- Non-Intrusive Load Monitoring (NILM)
ILM require one or more than one sensor per appliance to perform ALM
NILM just requires only a single meter per house
The ILM method is more accurate compared with NILM
The ILM method has practical disadvantages
- High costs, multiple sensor configuration, installation complexity
Non-Intrusive Load Monitoring (NILM) is process of estimating the energy
consumed by individual appliances
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6. Appliance Classification
Feature Extraction
Data Acquisition
General Framework of NILM
The data is acquired from the main electrical panel outside the building, hence
considered to be non-intrusive
The goal is to partition the whole-house building data into its major constituents
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7. Segregation of total loads into individual appliance load and can be
formulated as:
P(t) = 𝑖=1
𝑛
𝑃𝑖(𝑡)
P(t) total power
Pi(t) power consumption of individual appliances
n is the total no. of active appliances.
Fig1: An aggregated load data obtained using single point of measurement [1]
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8. Consumer appliances can be categorized based on their operational states as follows:
Type 1 Type 2 Type 3 Type 4
Only ON/OFF Switching pattern
of these
appliances is
repeatable.
Continuously
Variable Devices
(CVD)
permanent
consumer devices
Eg: Eg: Eg: Eg:
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10. Data Acquisition Module
The role is to acquire aggregated load measurement
Variety of power meters designed to measure the aggregated load
(1) Low-Frequency Energy Meters:
- harmonics and traditional power metrics such as real
power, reactive power, Root Mean Square (RMS)
voltage and current values. In kHz
(2) High-Frequency Energy Meters:
- Transient events. 10 – 100 MHz
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12. NILM Methods Based on Steady-State
Analysis
Real power (P) and Reactive power (Q) for tracking On/Off operation of
appliances
Challenging for appliances which exhibits overlapping in the P-Q plane
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14. Constant power and constant impedance loads are characterized by their
steady state current harmonics
Non linear loads ------ > non sinusoidal current
linear loads ------ > sinusoidal current
Fig4: Current draw of linear vs non-linear loads [9]
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15. Steady-State
Methods
Features Advantages Shortcomings
Power
Change
Steady State Variation
of Real and Reactive
Power.
High-Power
Residential
Loads can easily be
identified
Low power
appliances
overlap in P-Q
plane.
Time and
Frequency
Characteristi
cs
of VI
Waveforms
Higher order Steady-
State Harmonics, Irms,
Iavg,Ipeak, Vrms,
Power factor
Device classes can
easily be categorized
into resistive,
inductive and
electronic loads
High sampling
rate
requirement
V-I
Trajectory
asymmetry,
Area.
Detail classification
of
electrical appliances
Sensitive to multi-
load
operation scenario.
Steady-State
Voltage
Noise
EMI signatures Motor-based
appliances
are easily
distinguishable.
Sensitive to wiring
Architecture.
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16. NILM Methods Based on Transient-
State Analysis
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Transients
methods
Features Advantages Shortcomings
Transient power Repeatable transient
power profile
Same power draw
characteristics can
be easily
differentiated
Continuous
monitoring, high
sampling rate
Requirement
Start up current
transients
Current spikes, size,
duration, shape of
switching transients,
transient response
Time
distinct
transient behavior
in multiple load
operation Scenario
Poor detection of
simultaneous
activation
deactivation of
Sequences
High frequency
sampling of
voltage noise
Noise Multi-state devices Expensive.
17. 17
Transient behavior of major appliances is distinct and their features are less
overlapping in comparison with steady state signatures
The major limitation is the high sampling rate requirement in order to capture
the transients
20. Load Identification
Optimization approach matches the observed power measurements to
appliance power signals
one major drawback is that the presence of unknown loads
Pattern recognition approach has been a preferred method
Recently, researchers have shown an increased interest in unsupervised
methods for the load disaggregation
Loaddisaggregation
Optimization
Pattern recognition
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21. System Training
On-line training, used the time slice or window based methods
The off-line training approach acquires the aggregated load measurements
from the target environment
System training On-line training
Off-line training
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23. To ease the data annotation process, sub-metering approach utilized
Requires installation of one energy meter per appliance
It includes extra cost, complex installation of sensors on every device
Requires human interference and supervision
Currently there are no standard automated solutions
This is one of the limiting factor for delay the widespread success of NILM
solutions
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24. Challenges
Due to the lack of reference datasets
Low power consumer appliances exhibit similar power consumption
characteristics
Update of the appliance signature database
How to identify new devices that are not included in signature database
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25. Conclusion
High cost and intrusive nature of ILM, research is more focused towards non-
intrusive approaches
No set of appliance features as well as load disaggregation algorithms are
suitable
Combining transient and steady-state signatures to improve recognition
accuracy
Research in the future should focus on unsupervised learning methods
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26. References
1. Zoha, A.; Gluhak, A.; Imran, M.A.; Rajasegarar, S. , “ Non-Intrusive Load Monitoring
Approaches for Disaggregated Energy Sensing: A Survey.” Sensors 2012, vol.12, 16838-
16866.
2. G. Hart, “Nonintrusive appliance load monitoring,” Proceedings of IEEE,1992, vol. 80,
no. 12, pp. ,1870–1891.
3. Basu, K.; Debusschere, V.; Bacha, S.; Maulik, U.; Bondyopadhyay, S. , “Non Intrusive
Load Monitoring: A Temporal Multi-Label Classification Approach”. IEEE Trans. on
Industrial Informatics, 2015, vol.11, no.1 , pp.,262-270.
4. Laughman, C.; Lee, K.; Cox, R.; Shaw, S.; Leeb, S.; Norford, L.; Armstrong, P., “ Power
signature analysis.” IEEE Power Energ. Mag. 2003, vol.1, 56–63.
5. Berges, M.; Goldman, E.; Matthews, H.S.; Soibelman, L.; Anderson, K., “ User-centered
non-intrusive electricity load monitoring for residential buildings.” J. Comput. Civil Eng.
2011, 25, 471–480.
6. Norford, L.K.; Leeb, S.B., “ Non-intrusive electrical load monitoring in commercial
buildings based on steady-state and transient load-detection algorithms.” Energ. Build.
1996, 24, 51–64.
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27. 7. Jin, Y.; Tebekaemi, E.; Berges, M.; Soibelman, L., “ Robust Adaptive Event Detection
in Non-Intrusive Load Monitoring for Energy Aware Smart Facilities.” In Proceedings
of IEEE International Conference on Acoustics, Speech and Signal Processing, Prague,
Czech Republic, 22–27 May 2011; pp. 4340–4343.
8. Zeifman, M.; Roth, K., “ Nonintrusive appliance load monitoring: Review and
outlook.” IEEE Trans. Consum. Electron. 2011, 57, 76–84.
9. Liang, J.; Ng, S.K.K.; Kendall, G.; Cheng, J.W.M., “ Load signature study Part I: Basic
concept, structure, and methodology.” IEEE Trans. Power Del. 2010, 25, 551–560.
10. Hazas, M.; Friday, A.; Scott, J. “Look back before leaping forward: Four decades of
domestic energy inquiry.” IEEE Pervas. Comput. 2011, 10, 13–19.
11. Wang, Z.; Zheng, G., “ Residential appliances identification and monitoring by a
nonintrusive method.” IEEE Trans. Smart Grid 2012, 3, 80–92.
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