Deokwoo Jung and Andreas Savvides Embedded Networks & Applications Lab (ENALAB) Yale University

1. Estimating Building Consumption Breakdowns using ON/OFFState Sensing and Incremental Sub-Meter Deployment Deokwoo Jung and Andreas Savvides Embedded Networks & Applications Lab (ENALAB) Yale University http://enalab.eng.yale.edu

2. Sensing Loads on Electricity Network How to Estimate Electrical Loads of Appliances ? Breaker Box Electric Meter Electricity Network Electrical Outlet Bed Room Living Room Kitchen

3. Indirect Monitoring • Total Load Disaggregation + Load Signature Detection NALM (Hart.et.al):Nonintrusive Appliance Load Monitoring • Kill-A-Watt EZ • $45 • Data display only • Watts up? .Net • $ 230 • Internet enabled • Power switching • Watts up? • $100-$130 • Data Logging A ElectriSense (Sidhant et.all) :Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home Electricity Energy Monitoring Systems • Direct Monitoring : Expensive and brute-force method

4. Load Disaggregation Data Flow Voltage and current waveforms at Electrical outlets or Power entry point High frequency electromagnetic interference Edge detection Heat Vibration Light intensity Event Detection Partial Load Information e.g. Total Power consumption ON/OFF state Load Disaggregation How do we compute the load disaggregation?

5. The Diverse Nature of Loads Resistive vs. Inductive -> Short-term property Stationary vs. Non-stationary -> Long-term property Hard to estimate energy breakdown Washing Machine Non-Stationary heater Air Conditioner TV Long-term property Laptop Refrigerator Water Pump Dehumidifier Electric Kettle Hard to measure power consumption DVD Player Stationary Bulb Inductive Resistive Short-term property

6. Our Approach: Energy Breakdown per Unit Time Instead of instantaneous measurements, use average consumption over a time window Actual Average Power Consumption Actual Power Consumption Profile Estimated Average Power Consumption Estimation Error Estimation Period k Estimation Period k-1 Estimation Period k+1 Example appliance: LCD TV

7. Three Tier Tree Network Time Appliance Consumption fluctuation properties Problem Setup Goal:Estimate the average power consumption for a time window Select an appropriate time window to get the best estimate of energy consumption

8. Prototype System Implementation One Energy Meter and ON/OFF Sensors BehaviorScope Portal TED 5000 Monitor Consumption measurements Appliance ON/OFF Information Active RFID Dry Contact Sensor

9. 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

10. Solve Opt. Problem: Problem Formulation • Objective Function:

11. Designing Weights and Selecting Appropriate Time Window • Optimal Choice of Weight Matrix, W • 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)

12. Evaluation - Case StudyA 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. Daily energy consumption ground truth in one-bedroom apartment from an experiment from Thursday to Saturday Histogram of power consumption of appliances during their On state The hourly energy consumption ground truth in one-bedroom apartment from an experiment from Thursday to Saturday The number of meter samplesobserved given composite binary states

13. Evaluation - Case Study :A small electricity Network with single power meter • Estimated hourly energy consumption profile of each appliance • Average 10% of relative error

14. No Weight Algorithm performance Unit Sum Weight Lower bound Performance over Estimation Periods • With different weight matrix

15. Algorithm performance Current Data Selection Cumulative Data Selection Lower bound Performance over Estimation Periods • With different data selection schemes

16. Performance by Data Selection, Weight Matrix, and Estimation Period 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

17. ? Weight Coefficient: # of meters vs performance Estimation Quality Node Efficiency Increasing Accuracy on Larger Networks with Additional Meters • How many power meters we need and where should place them? • Tree Decomposition Problem • Depending on sensor duty cycles • Combinatorial Optimization Problem • Use Stochastic Search Algorithm : Simulated Annealing • Cost function of Simulated Annealing • Evaluated against the initial solution, • Z0=(1,1…,1) : Placing meters on all available electrical outlets.

18. 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 • 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

19. 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

20. 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