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SBS: Strip, Bind, and Search

SBS: Strip, Bind, and Search. D etecting E nergy-Savings O pportunities in Buildings Jorge Ortiz Romain Fontugne , Hiroshi Esaki, Davi d Culler. Software-Defined Building January 11, 2013. SDBs Generate Lots of Data.

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SBS: Strip, Bind, and Search

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  1. SBS: Strip, Bind, and Search Detecting Energy-Savings Opportunities in Buildings Jorge Ortiz RomainFontugne, Hiroshi Esaki, David Culler Software-Defined Building January 11, 2013

  2. SDBs 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

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

  4. Strip and Bind

  5. 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

  6. SBS Result • 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 • Building 1 = Todai • Building 2 = Cory Hall

  7. Alarms in Eng. Bldg 2 @Todai

  8. Alarms in Cory Hall Chiller stopped working Uncorrelated power usage Simultaneous heating and cooling

  9. Takeaways • Points to interesting segments in the data • No prior building or device model necessary • Successfully finds energy-saving opportunities • Changes in usage pattern can uncover in/efficient usage patterns

  10. Possible Future Directions • Continuous metadata label checking • Compare clusters with human-inputted labels • Automatic classification of faults across many buildings • Integration with automatic control strategies

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