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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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References1. 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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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.