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Estimating Building Consumption Breakdowns using ON/OFF State Sensing and Incremental Sub-Meter Deployment Deokwoo Jung and Andreas Savvides Embedded Networks & Applications Lab (ENALAB) Yale University http://enalab.eng.yale.edu Nov 4, 2010 Deokwoo Jung Sensing Loads on Electricity Network Breaker Box How to Estimate Electrical Loads of Appliances ? Electric Meter Electricity Network Electrical Outlet Bed Room Nov 4, 2010 Living Room Kitchen Deokwoo Jung Electricity Energy Monitoring Systems • Indirect Monitoring • Direct Monitoring : Expensive and brute-force – Total Load Disaggregation + Load Signature Detection method NALM (Hart.et.al): Nonintrusive Appliance Load Monitoring • Kill-A-Watt EZ – – $45 Data display only • Watts up? – – $100-$130 Data Logging • Watts up? .Net – – – $ 230 Internet enabled Power switching A ElectriSense (Sidhant et.all) :Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home Nov 4, 2010 Deokwoo Jung Load Disaggregation Data Flow High frequency electromagnetic interference Event Detection Edge detection Partial Load Information Heat at Electrical outlets or Power entry point Vibration Light intensity Voltage and current waveforms e.g. Total Power consumption ON/OFF state Load Disaggregation How do we compute the load disaggregation? Nov 4, 2010 Deokwoo Jung The Diverse Nature of Loads Resistive vs. Inductive -> Short-term property Stationary vs. Non-stationary -> Long-term property NonStationary Hard to estimate energy breakdown Washing Machine heater Air Conditioner Long-term property TV Laptop Refrigerator Electri c Kettle Stationary Bulb Resistive Nov 4, 2010 DVD Player Short-term property Dehumid ifier Water Pump Hard to measure power consumption Inductive Deokwoo Jung Our Approach: Energy Breakdown per Unit Time Instead of instantaneous measurements, use average consumption over a time window Actual Average Power Consumption Estimated Average Power Consumption Actual Power Consumption Profile Estimation Error Estimation Period k-1 Estimation Period k Estimation Period k+1 Example appliance: LCD TV Nov 4, 2010 Deokwoo Jung Problem Setup Three Tier Tree Network Time Appliance Consumption fluctuation properties 0 1 1 0 0 1 1 1 0 1 1 1 1 1 0 0 0. 0 1 0 1 1 0 1 0 1 0 1 0 1 Goal: Estimate the average power consumption for a time window Select an appropriate time window to get the best estimate of energy consumption Nov 4, 2010 Deokwoo Jung Prototype System Implementation One Energy Meter and ON/OFF Sensors TED 5000 Monitor BehaviorScope Portal Consumption measurements Appliance ON/OFF Information Active RFID Dry Contact Sensor Nov 4, 2010 Deokwoo Jung Main Idea ON/OFF sequence of appliances occurs between the worst (Perfectly Synch) and the best case (Perfectly Desynch) appliance A appliance B Worst Case Observed Binary Data Best Case Approach – Variant of Weighted Linear Regression – Accounting for Diversity • Design Optimal Weight Matrix, W – Metric Driven Data Selection • Regression data set is adaptively chosen according to active power consumption property, stationary vs. non-stationary • Using Prediction Metric for Estimation Error Nov 4, 2010 Deokwoo Jung Problem Formulation Objective Function: Min W TotalPower- P1 X1 P2 X 2 Pn X n Sample Index On/Off state of TV On/Off state of Microwave On/Off state of Lamp The Average of Power meter measurement (Watt) 1 0 0 1 59.3 3 2 0 1 1 369.3 3 3 1 0 0 120 1 4 1 0 1 Y 160 1 5 1 1 1 469 1 X Solve Opt. Problem: Nov 4, 2010 # of samples observed ˆ arg min (XWX)P XWY P P0 Deokwoo Jung Designing Weights and Selecting Appropriate Time Window • Optimal Choice of Weight Matrix, W No Weight W # of Samples Unit Sum Matrix # of Samples On State All Appliances Estimated Variance Sum Matrix Exact Variance Sum Matrix # of Samples On State Varianceof ActivePower All Appliances • Account for (Non-) Stationary Property – Stationary Load : larger window of measurements is better – Non-Stationary Load: small window of measurements is better – Automatically select to use either of the entire estimation periods (Cumulative Data) or only the current period (Current Data) Nov 4, 2010 Deokwoo Jung Evaluation - Case Study A small electricity Network with single power meter • • Collecting data from 12 appliances in one-bedroom Apt from Thu-Sat A large variation of energy load – the heater accounts for more than 60% of the total energy consumption – the laptop consumed the least, less than 1% of the total load. Dehumedifier Ceramic Heater Laptop Dehumedifier Ceramic Top Freezer Refrigerator Halogen40 Desk LampHeater Microwave Oven 10 10 LaptopFluorescent Desk Lamp LCD TV(32") Top Freezer Refrigerator Halogen Desk Lamp Desktop+Monitor Standard Bulb Table lamp Compact Refrigerator Two-Way Floor Lamp Microwave Oven 5 20Fluorescent Desk Lamp 5 Desktop+Monitor 2.5 Standard Bulb Table lamp Compact Refrigerator Two-Way Floor Lamp 0 0 0 4 Friday Saturday 440 Thursday 460 480 500 1000 1500 20 30 40 25 LCD TV(32") Top Freezer Refrigerator Halogen Desk Lamp 20 20 20 2 Compact Refrigerator only Energy Consumption, (kWh) 320 Energy Consumption, (kWh) The number of samples, nk 5 Daily The energy consumption ground truth in one-bedroom apartment from an hourly energy consumption ground truth in one-bedroom apartment Histogram of power consumption appliances during their Onbinary state states experiment from Thursday to Saturday 4 The number of meter samples observed given composite from an experiment fromof Thursday to Saturday x 10Dehumedifier Ceramic Heater Laptop LCD TV(32") 10 0 60 1.5 2 15 1 10 0.5 5 Nov 4, 2010 6 0 38 40 42 Desktop+Monitor 20 + Ceramic Heater Top Freezer Refigerator 10 10 10 0 0 500 1000 Standard Bulb Table lamp 40 0 32 33 34 Compact Refrigerator 40 0 100 120 140 Two-W ay Floor Lamp 20 20 20 10 0 30 0 0 0 0 100 120 140 Fluorescent Desk Lamp 20 80 100 Microwave Oven 20 1 0 All appiances are off 10 Compact Refigerator +10Ceramic Heater 5035 8 10 12 14 16 18 20 22 0 100 40 2 4 6 0 65 70 150 0 200 100 75 8 10 12 14 16 18 20 22 0 2 4 6 200 250 300 350 300 8 10 12 14 16 18 20 22 0 2 4 6 Decimal of(hour) composite binary states, xk Thursday representation Friday Saturday Time, Deokwoo Jung Evaluation - Case Study : A small electricity Network with single power meter • Estimated hourly energy consumption profile of each appliance – Average 10% of relative error Nov 4, 2010 Deokwoo Jung Performance over Estimation Periods Average Active Power Relatve Error, (%) • With different weight matrix 100 Opt. Data.Sel + Est.Var.Sum.Wgt (Algorithm Performace) Opt. Data.Sel + Unit.Sum.Wgt No Weight Opt. Data.Sel + No.Wgt. Oracle. Data.Sel + Exact.Var.Sum.Wgt (Lower Bound) 80 60 40 Algorithm performance Unit Sum Weight 20 0 0 0.5 Lower bound Nov 4, 2010 1 1.5 2 Estimation Periods, T est ( hour) 2.5 3 Deokwoo Jung Performance over Estimation Periods Average Active Power Relatve Error, (%) • With different data selection schemes 200 Est.Var.Sum.Wgt + Opt. Data.Sel (Algorithm Performace) Est.Var.Sum.Wgt + Cur. Data.Sel Est.Var.Sum.Wgt + Cma. Data.Sel Exact.Var.Sum.Wgt + Oracle. Data.Sel (Lower Bound) 150 100 Algorithm performance 50 0 0 0.5 Lower bound Nov 4, 2010 Current Data Selection Cumulative Data Selection 1 1.5 2 Estimation Periods, T est ( hour) 2.5 3 Deokwoo Jung Performance by Data Selection, Weight Matrix, and Estimation Period Average Active Power Relatve Error, (%) The maximum, minimum, and average value of relative error of active power consumption for all estimation periods with various combination of weighted matrix and data selection schemes 250 Est.Var.Wgt + Cur.Data.Sel 200 No.Wgt + Opt.Data.Sel 150 Est.Var.Wgt + Opt.Data.Sel (Algorithm Performace) Unit.Sum.Wgt + Opt.Data.Sel Exact.Var.Wgt + Opt.Data.Sel 100 Est.Var.Wgt + Cma.Data.Sel 50 Est.Var.Wgt + Oracle.Data.Sel Exact.Var.Wgt + Oracle.Data.Sel (Lower Bound) 0 Nov 4, 2010 Deokwoo Jung Increasing Accuracy on Larger Networks with Additional Meters Topology 1 • How many power meters we need and where should place them? ? – Tree Decomposition Problem • Depending on sensor duty cycles y1 y0 y1 y0 y1 x1 x2 x3 x4 x5 0 y2 – Combinatorial Optimization Problem • Use Stochastic Search Algorithm : Simulated Annealing • Cost function of Simulated Annealing – Topology 2 x1 x2 x3 x4 x5 y0 y2 y2 Unsynchronized Synchronized Evaluated against the initial solution, x1 x2 x5 x3 • Z0=(1,1…,1) : Placing meters on all available electrical outlets. Z MSE( P | Z0 ) c ( Z) (1 ) MSE ( P | Z ) Z 0 Weight Coefficient: # of meters vs performance Estimation Quality Nov 4, 2010 Node Efficiency Deokwoo Jung x4 Evaluation - Case Study 2: A large scale electricity network with meter deployment • Performance evaluation by increasing the number of Apt units from 1 to 12 With a single power meter for a large electricity network Relative Error, (%) • 150 100 50 0 0 20 40 60 80 100 120 The number of appliances Meter Deployment by Algorithm • – – – – Compared by random deployment For λ= 0.5, x10 in performance Or reduce x 2~3 in # of meters λ = 0 Single power meter λ = 1 Full deployment 10 Random Deployment B-SEND Algorihtm 6 Max =0 RSS(P) • 10 Mean 5 Min =0.3 10 =1 4 =0.5 =0.7 10 Nov 4, 2010 3 0 2 =0.9 4 6 8 10 The Number of Electricity Meters 12 14 Deokwoo Jung Conclusions and Future Work Developed an energy breakdown estimation algorithm for a single power meter and the knowledge of ON/OFF states • 10% of relative error for 12 home appliances and a single power meter Developed an algorithm for optimally placing additional power meters to improve estimation accuracy in large networks • Deployment algorithm can reduce 3-4 times of the number of power meter for the simulation of 12 households Future work: - Experimental deployment on a Yale building in January 2011 - Handle incomplete binary state sensing - Leverage history information and user inputs Nov 4, 2010 Deokwoo Jung Discussion & Comparison with Related Work • • • The question on high frequency systems makes some sense. Assuming that you can detect signatures, if the frequency of measurement is high enough you may have enough information to computer itemized consumption. The key argument to make is that this approach could work today with existing low-frequency meters. The central meter in a home only has to same using 1Hz. Also, in the home, we may be able to do this without any additional hardware by just completing forms on a GUI. While we work out details for a journal version it is important to identify and propose the next problem to solve on load disaggregation Nov 4, 2010 Deokwoo Jung