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Metadata Verification and SBS

Jorge Ortiz. Metadata Verification and SBS. Outline. Metadata verification Scalable anomaly detection. Chiller. Pump. Zone. Chiller. Vent. Vent. Pump. SF. EF. AHU. System. Chiller. Space. Pump. Zone. Chiller. Vent. Vent. Pump. SF. EF. AHU. Types of relationships.

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Metadata Verification and SBS

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  1. Jorge Ortiz Metadata Verification and SBS

  2. Outline • Metadata verification • Scalable anomaly detection

  3. Chiller Pump Zone Chiller Vent Vent Pump SF EF AHU

  4. System Chiller Space Pump Zone Chiller Vent Vent Pump SF EF AHU

  5. Types of relationships • Geometric • Placement, associations • Functional • Temperature,pressure, flow, etc. • Semantic • Electrical device taxonomy • Ownership

  6. Metadata management pipeline Our work Current

  7. Geometric Verification • Are the geometric (spatial) associations correct? • Are all the sensors with the same spatial grouping in the same location? • Sensors can be moved or replaced • Contractor mislabels point in BMS • How can the sensor data guide this process? SODA4R520__ART

  8. Similar Trend in Data Streams • Sensor streams driven by same phenomena • Common trend ineffective at uncovering relationships

  9. No Discernible Correlation Pattern in Raw Traces • Each row/column is a location in the building • Each location has one or more sensors • Cell (i,j) is the average device pairwise correlation between sensors at locations i and j

  10. Empirical mode decomposition • Approach used for finding underlying data trends • Algorithm for decomposing signals in the time domain of non-stationary, non-linear signals • Similar to FFT, PCA but yields characteristic time and frequency scales • Output “Intrinsic mode functions” • Combination of underlying signal in the same time scale

  11. Devices in the same room

  12. Devices is different rooms

  13. Broader validation Compare the EHP to 674 other sensors: **Suggests Geometric Verification is possible** EMD helps us to discriminate un/related sensors

  14. Functional Verification • Mislabeled “type” information of a data stream • Fault detection • Strip, bind, and search process

  15. Buildings Generate Lots of Data • Difficult for building managers to know where to start to look for problems • Which devices? Locations? Patterns? Time interval? • Key Observation • Devices are used simultaneous in the same way • Typically usage times/patterns are tightly un/coupled • Example: • Lights and HVAC during the day • Basic assumption • Normal usage is efficient. • Pairwise correlation analysis of sensor traces • Uncover usage relationships between devices

  16. Strip and Bind

  17. Searching for Outliers • Construct reference matrix for each time-reference interval • For new data points, compute l • Identifying outliers • Median Absolute Deviation p=4 , b=1.4826

  18. Results • High power usage • Alarms corresponding to electricity waste • Lower power usage • Alarms representing abnormal low electricity consumption • Punctual • Short increase/decrease in electricity consumption • Missing data • Possible sensor failure • Other • unknown

  19. Alarms in Eng. Bldg 2 @Todai AC On All Night AC Not On During Office Hours Lights On All Night

  20. Alarms in Cory Hall Change in power usage pattern Possible Chiller dysfunction Normal 18 days, 2500 kWh Simultaneous heating and cooling

  21. Impact • 2 research papers in collaboration with U. of Tokyo • Internet of Thing Workshop @IPSN 2012 • IPSN 2013 (April) • Web tool that finds anomalies from data uploads • Upcoming release

  22. Future Work • Value verification • Model-based verification, model validation • Standard representation with embedded confidence parameters for MPC

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