non intrusive load monitoring
TRANSCRIPT
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Non-Intrusive Load Monitoring
Center for Energy and Environment, MNIT
Submitted by Sai Goutham Golive2014pcv5192
Submitted toProf. Jyotirmay Mathur
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Contents Background
Introduction
General Frame Work Of NILM
Data Acquisition
Feature Extraction
Load Identification
System Training
Challenges
Conclusions
References
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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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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
Utilities Customers
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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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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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Segregation of total loads into individual appliance load and can be formulated as:
P(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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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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Fig2: Different load types based on their energy consumption pattern [1]
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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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Feature Extraction
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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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Fig3: Load distribution in P-Q Plane [10]
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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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Steady-State Methods
Features Advantages Shortcomings
Power Change
Steady State Variationof Real and ReactivePower.
High-Power ResidentialLoads can easily beidentified
Low power appliancesoverlap in P-Q plane.
Time and Frequency Characteristicsof VI Waveforms
Higher order Steady-State Harmonics, Irms,Iavg,Ipeak, Vrms,Power factor
Device classes can easily be categorized into resistive, inductive andelectronic loads
High sampling raterequirement
V-I Trajectory
asymmetry,Area.
Detail classification ofelectrical appliances
Sensitive to multi-loadoperation scenario.
Steady-State Voltage Noise
EMI signatures Motor-based appliancesare easily distinguishable.
Sensitive to wiringArchitecture.
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NILM Methods Based on Transient- State Analysis
Transients methods
Features Advantages Shortcomings
Transient power Repeatable transientpower profile
Same power drawcharacteristics can be easily differentiated
Continuousmonitoring, highsampling rateRequirement
Start up current transients
Current spikes, size,duration, shape ofswitching transients,transient responseTime
distincttransient behavior in multiple load operation Scenario
Poor detection ofsimultaneous activationdeactivation ofSequences
High frequency sampling of voltage noise
Noise Multi-state devices Expensive.
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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
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Non-Traditional Appliance Features
Fig5: Schematic diagram of two unit graph [11]
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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
Load
dis
aggr
egat
ion
Optimization
Pattern recognition
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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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Fig 6:Example graphical user interface (GUI) for training the classifier [5]
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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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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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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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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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