Jorge Ortiz

 Metadata verification  Scalable anomaly detection

Chiller Pump Chiller Pump AHU SFEF Vent Zone

Chiller Pump Chiller Pump AHU SFEF Vent Zone System Space

 Geometric  Placement, associations  Functional  Temperature, pressure, flow, etc.  Semantic  Electrical device taxonomy  Ownership

Current Our work

 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? SODA4 R520 __ART

 Sensor streams driven by same phenomena  Common trend ineffective at uncovering relationships

 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

 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

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

 Mislabeled “type” information of a data stream  Fault detection  Strip, bind, and search process

 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

 Construct reference matrix for each time-reference interval  For new data points, compute l  Identifying outliers  Median Absolute Deviation p=4, b=1.4826

 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

AC On All Night Lights On All Night AC Not On During Office Hours

Possible Chiller dysfunction Change in power usage pattern Simultaneous heating and cooling Normal 18 days, 2500 kWh

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

 Value verification  Model-based verification, model validation  Standard representation with embedded confidence parameters for MPC