Adrian Albert. Disaggregation: a Survey

Transcription

1 Disaggregation: Brief Survey Adrian Albert

2 What Should We Focus On >75W loads: 99% of consumed US energy Appliances by energy consumed (US) and difficulty to disambiguate from noisy signal. Source: Hart (1992) Can we do better than this Some appliances (heating, refrigerators, lighting) take up most of energy consumed (US) Some appliances harder to identify as others: complex physical operation characteristics Data 20 years old; Thing have changed in the meantime (e.g., more computers)

3 At a Glance At a Glance: Disaggregation Algorithms Steady state power changes (early work) Detect jumps in active & reactive power specific to individual appliances Resistive, simple appliances G. Hart (1992), F. Sultanem (1991), M. L. Marceau, R. Zmeureanu (2000), C. Laughman et al. (2003), Harmonic analysis Use current harmonics and power thereof as additional features Better identifies non linear appliances Y. Nakano & K. Yoshimoto (2004), L. Farinaccio & R. Zmeureanu (1995) Transient state High frequency noise induced by change in appliance state (need high sampling rate) Build database of transient signature L. Norford, S. Leeb et al. (1993), S. Shaw (1998), S. Patel et al., (2007) Source: Najmeddine et al., 2008 Recent work Emphasize transient, state change s R. Ford/J. McCoullough (Dphil Thesis Oxford, 2009): Bayesian framework for appliance classification Water system disaggregation (S. Patel et al, 2009): 98% accuracy

4 Performance and Limitations Performance Chart Algorithm type Steady-state edge detection (base case) Steady state harmonics features SVM classification Transients analysis: SVM classification Study examples Data & setup specifications Accuracy and performance Hart, Leeb (MIT); Sultanem (EDF) yr old data Nakano & Hidaka (CRIEPI & TUAT); Patel (UW); Schwab, Leeb (MIT) 1 Hz average power & voltage custom-built reading instrument off-line data analysis (few homes) Manual/automatic training 1/60 Hz (1 minute) Several test households (Patel) 1MHz current and voltage Custom-built monitoring system 6 homes, 4 weeks, ~3000 events Train: events; Test: events 85% (simple ON/OFF appliances >150W) 65%-80%, larger for smaller appliance subset ~40 different appliances of various complexities 80%-90% Are there other algorithmical approaches that improve accuracy How to define accuracy (% of appliances identified, % of energy )

5 Performance and Limitations Limitations of Current Techniques

6 Performance and Limitations Accuracy vs Sampling Rate: Accuracy vs Sampling Rate % of Energy Disaggregated What current research suggests Optimistic Pesimistic Sampling Rate [Hz, log scale] What (commercially-achievable) data sampling rate do we need for good disaggregation Upper limit on achievable accuracy (cutoff sampling rate)

7 Performance and Limitations Data and Algorithms What kind of data do we need to achieve different disaggregation accuracies Sampling rate, monitored electricity parameters Test data: types of homes, types of appliances Build database of signatures : how large is training set How much of a problem Sampling rate / Accuracy 70%-80% 80%-90% 90%-95% > 95% Hourly 15 minutes 1 minute Harmonics detection for some non-linear appliances 1 s (1 Hz) Edge detection for simple on/off appliances 0.01 s (100 Hz) <0.001 s (>1kHz) Transient analysis for relatively complex appliances

8 Next Steps Questions, Suggestions Universities, utilities, smart meter companies put together reference dataset: different data resolutions, parameters monitored etc. Agree on key questions to be answered based on dataset Design a Disaggregation Challenge similar to e.g., the InfoVis/IEEE Visualization Challenge Academic teams compete in an open challenge Award prizes E.g., conference participation fees Training the appliance model & building signature database User aided training: design system that asks for user input for labeling appliances Use AI techniques to study behavioral and lifestyle patterns of individual homes Customize general purpose appliance information ( signatures )

9 Algorithms Survey Algorithms Details

10 At a Glance At a Glance: Hardware Source: ZigBee SE specification manual Smart meters 1 10W power resolution, 95% 99% power/voltage accuracy Sampling rate 1mHz 1Hz: meters can generally sample at fast rates Home Area Networks: wireless enabled smart meters routers/gateways in home display devices Z Wave, ZigBee (IEEE ): kbps transfer rate, ~10 meter range star or peer to peer: network coordinators, routers, end devices Interfaces: Specifications for smart meter to communication card, HAN data transfer (ZigBee SE): message strings, price information, time, etc. (ANSI C12.19 data tables format)

11 Early Approaches Events Identification: Steady State Detecting jumps in power (active/reactive) Match appliance operational characteristics (DP, DQ) to signatures database ( edges ) Source: Hart (1992) Work at MIT by Hart, Leeb, Shaw (10-20 years old) Best for residential: steady, finite state machines (light bulb, toaster) Initial calibration phase for individual home needed Use ~1s data, achieve ~85% accuracy

12 Harmonics Harmonics and Transient Regimes For multiple relatively similar loads, (DP, DQ) space gets cluttered Many appliances for non residential use take a long time to reach stationary state Fourier analysis of current waveform: compute "spectral envelopes" that summarize time varying harmonic content Source: Najmeddine et al., 2008 C. Laughman et al (2003) Detect transient regimes: Computer (capacitors) profile is different than that of lamp (resistor) Collect signatures: initial calibration Use as feature power in higher harmonics (3, 5, 7) of current waveform

13 Transient Analysis Transients: Higher Dimensional Feature Space Classification in higher dimensional space S. Patel et al., At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (2007) Use transient or continuous noise produced by abruptly switching on electrical loads Industrial loads: J. G. Roos et al (1994) AI flavored approaches: neural network as supervised classifier J. G. Roos et al, Using Neural Networks for Non intrusive Monitoring of Industrial Electrical Loads (1994) Classify appliances after their functionality, physical operation principle etc. Train neural net using labeled prototypes from each appliance category Signature space (feature vector): Fourier harmonics Standard machine learning classifier: Support Vector Machines (SVM) N-dimensional separation hyperplane. Signatures collected in initial calibration Wall switch: 100 Hz 5 KHz noise Inductive loads: 5 khz 1 MHz continuous noise Sample rate 1MHz, accuracy 85%-90% for different types of homes

14 All Is Not Electricity That Sparks Froelich, Patel: HydroSense: Infrastructure Mediated Single Point Sensing of Whole Home Water Activity (2009) Measure water flow pressure at main source Build pressure jumps and harmonics signature database Train events classifier using multiple features Accuracy ~98%

15 Sample Literature References Household level monitoring and disaggregation based on Hart's method (events identification): F. Sultanem, USING APPLIANCE SIGNATURES FOR MONITORING RESIDENTIAL LOADS AT METER PANEL LEVEL. IEEE Transaction on Power Delivery, Vol. 6, No. 4, 1991 G. Hart, Nonintrusive Appliance Load Monitoring. Proceedings of the IEEE, vol. 80, no. 12, 1992 M.L. Marceau, R. Zmeureanu, Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses for residential buildings. Energy Conversion & Management 41 (2000) 1389±1403 C. Laughman et al, Advanced Nonintrusive Monitoring of Electric Loads. IEEE Power and Energy, March 2003 Harmonic analysis based methods Y. Nakano, Non Intrusive Electric Appliances Load Monitoring System Using Harmonic Pattern Recognition. Technical Report, S. Leeb, S. Shaw, J. Kirtley, Transient Event Detection in Spectral Envelope Estimates for Nonintrusive Load Monitoring. IEEE Transactions on Power Delivery, Vol. 10. No. 3, July 1995 Transients analysis J. G. Roos et al, Using Neural Networks for Non intrusive Monitoring of Industrial Electrical Loads. IMTC '9 S. Patel et al., At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line. UbiComp 2007, LNCS 4717, pp , 2007 R. Cox, S. Leeb, S. Shaw, L. Norford, Transient Event Detection for Nonintrusive Load Monitoring and Demand Side Management Using Voltage Distortion. IEEE, Hardware or device specific papers K. D. Lee, Estimation of Variable Speed Drive Power Consumption From Harmonic Content. IEEE TRANSACTIONS ON ENERGY CONVERSION, VOL. 20, NO. 3, SEPTEMBER 2005 S. R. Shaw, C. B. Abler, R. F. Lepard, D. Luo, S. B. Leeb, L. K. Norford, Instrumentation for High Performance Nonintrusive Electrical Load Monitoring. Transactions of the ASIVIE, 224 / Vol. 120, AUGUST 1998 Overview and exploratory papers W. K. Lee, Exploration on Load Signatures. International Conference on Electrical Engineering (ICEE) 2004, Japan. Hala NAJMEDDINE, State of art on load monitoring methods. 2nd IEEE International Conference on Power and Energy (PECon 08), December 1 3, 2008, Johor Baharu, Malaysia Other applications of disambiguation and similar techniques Robert W. Cox, Patrick L. Bennett, LCDR T. Duncan McKay, James Paris, Steven B. Leeb, Using the Non Intrusive Load Monitor for Shipboard Supervisory Control. IEEE, Jon Froehlich, Eric Larson, Tim Campbell, Conor Haggerty, James Fogarty, Shwetak N. Patel, HydroSense: Infrastructure Mediated Single Point Sensing of Whole Home Water Activity. UbiComp 2009, Sep 30 Oct 3, 2009, Orlando, Florida, USA.

16 Discussion Points Are there other fundamentally different algorithmical approaches What is best way of describing accuracy/algorithms performance (%energy identified, % successful event identifications ) How does the graph accuracy vs frequency look like (missing data points) What type of data would be needed for effective disaggregation Can we fill in the table on slide 7 Other parameters than accuracy & frequency relevant Does it even make sense