LABEL CORRECTION AND EVENT DETECTION FOR ELECTRICITY DISAGGREGATION Mark Valovage1 and Maria Gini2 1Ph.D. Candidate, University of Minnesota 2Professor of Computer Science, University of Minnesota

OVERVIEW Background Contributions Conclusions Electricity Disaggregation Contributions Training Sample Label Correction (Supervised Learning) Parameter-free Event Detection (Unsupervised Learning) Conclusions

ELECTRICITY DISAGGREGATION Annual electrical waste (United States) $70 billion/year. 1.1 gigatons pollution. Aggregate data “You used X KWh.” “Pay us $Y.” Disaggregated data Can reduce power consumption by 15%.

LABEL CORRECTION Background Contributions Conclusions Electricity Disaggregation Contributions Training Sample Label Correction (Supervised Learning) Parameter-free Event Detection (Unsupervised Learning) Conclusions

ELECTRICITY DISAGGREGATION LABELING ERRORS Limiting Assumptions Correctly labeled samples Isolated appliances Labels error sources Offsets Raw user errors Truncated shutdowns Contaminations/Not isolated

LABEL ERRORS IMPACT ON CLASSIFICATION Back Porch Lights Samples Mean Power Increase (W) Uncorrected Corrected Sample 1 229.7 331.8 Sample 2 145.04 333.15 Sample 3 144 330.74 Sample 4 143.11 N/A Mean 165.46 331.90 Std. Dev. 37.09 0.99 Simple Mean Classification (+/- 10%) True Positives 0/4 4/4 False Positives 3 8 False Positive Appliances (Power Phase 2) Dining Room Lights 344.84 Master Bathroom Lights 314.52 Bedroom 1 Lights 290.19 Bedroom 1 LCD TV 152.04 Simple Mean Classification (+/-2 std) True Positives 0/4 4/4 False Positives 8

BAYESIAN CHANGE DETECTION Detects abrupt changes in 1-dimensional data Probability based Tracks active sequence (highest probability) Identifies changepoint when active run sequence changes Advantages: Robust to noise Does not require parameter tuning BCD is a tool that detects abrupt changes in 1-dimensional data.

LABEL CORRECTION ALGORITHMS (1/2) Naïve Proximal (NaïveProx) Run Bayesian Change Detection on training sample Use closest changepoints to user on/off labels Does not account for domain-level properties

LABEL CORRECTION ALGORITHMS (2/2) Power-constrained Proximal (PCProx) Can be online or offline (OnlinePCProx/OfflinePCProx) Run Bayesian Change Detection on training sample Use closest changepoints to user on/off labels Ensure new on/off labels satisfy real-power constraints

LABEL CORRECTION DATASET Kaggle Belkin dataset 4 houses Real power sampled at 5 Hz 147 appliances 462 training samples Label errors have not been removed

LABEL CORRECTION RESULTS

EVENT DETECTION Background Contributions Conclusions Electricity Disaggregation Contributions Training Sample Label Correction (Supervised Learning) Parameter-free Event Detection (Unsupervised Learning) Conclusions

UNSUPERVISED LEARNING EVENT DETECTION No training samples Appliances not isolated Limitations of existing event detection methods Require pre-processing to reduce noise Parameters must be tuned

EVENT DETECTION COMPETING ALGORITHMS Modified Generalized Likelihood Ratio (mGLR) Change Detection Requires parameter tuning Dirichlet Process Gaussian Mixture Models (DPGMM) Identifies steady-state power levels through iterative clustering Marks events as changes between steady-states Reduces noise through median window filtering

EVENT DETECTION DATASETS BLUED (Building-Level fUlly-labeled dataset for Electricity Disaggregation) 1 house Power sampled at 60 Hz for 1 week REDD (Reference Electricity Disaggregation Dataset) 6 houses Power sampled at ~0.25 Hz

EVENT DETECTION PERFORMANCE METRICS Unweighted F-measure Power-weighted F-measure

EVENT DETECTION RESULTS (1/2) UNWEIGHTED POWER-WEIGHTED

EVENT DETECTION RESULTS (2/2) UNWEIGHTED POWER-WEIGHTED

CONCLUSIONS Introduced application of Bayesian change detection Label Correction (Supervised Learning) Event Detection (Unsupervised Learning) Advantages Improves accuracy of samples for supervised learning Eliminate the need for parameter tuning Perform competitively against existing methods Can be run in real time on inexpensive hardware

FUTURE WORK Label Correction Event Detection Improve correction accuracy Recover information from contaminated samples Event Detection Automatically compute baseline noise threshold, Improve appliance reconstruction algorithms

QUESTIONS LABEL CORRECTION AND EVENT DETECTION FOR ELECTRICITY DISAGGREGATION Additional materials available at www.cs.umn.edu/~valovage/AAMAS-2017 Mark Valovage Ph.D. Candidate, University of Minnesota Research Intern, SIFT Maria Gini Professor, Computer Science University of Minnesota That concludes my presentation. I will now take your questions.