RELIABLE PROFILING FOR MONITORING SYSTEMS

• US 20140012821A1
• Filed: 03/01/2012
• Published: 01/09/2014
• Est. Priority Date: 03/17/2011
• Status: Abandoned Application

• Current Assignee: Philips Lighting BV (Koninklijke Philips N.V.)
• Original Assignee: Koninklijke Philips N.V.
• Inventors: Fuhrmann, Peter, Hu, Hao, Flinsenberg, Ingrid Christina Maria
• Application Number:
  US14/005,328
• Publication Number:
  US 20140012821A1
• Time in Patent Office: Days
• US Class Current: 707/691
• CPC Class Codes: G06F 16/2365 Ensuring data consistency and integrity
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data

Abstract

The present invention relates to an apparatus and method for analyzing building auditing information to identify irregular usage patterns and incorrect audit information. The auditing information, such as energy consumption, is analyzed using presence information at room or zone level. Based on pre-selected expectations, clustering is applied to data sets. By using different criteria, the clustering results are examined within every cluster and among clusters to find irregular information. Furthermore, through cross-checking with other background information, irregular usage pattern can be found and incorrect audit information can be identified, so that succeeding energy prediction and decision-support algorithms can work on a reliable set of profiles.

Claims

1. An apparatus for analyzing a building energy/lighting auditing information retrieved by a monitoring system, said apparatus comprising:

a dedicated or disturbed processor including;

a. a clustering unit for clustering data sets of said building energy/lighting auditing information based on at least one predetermined clustering criteria relating to a building'"'"'s characteristics and/or energy/lighting usage pattern(s) to obtain clusters with a commonality;

b. a comparison unit for comparing clusters obtained by said clustering stage based on at least one predetermined comparison criteria to determine potential candidates for irregular or incorrect clusters; and

c. a checking unit for cross-checking candidates determined by said comparison stage with background information relating to a specific condition of the building'"'"'s energy/lighting use including at least one of building room types(s), calendar information, time or user schedules to determine irregular energy/lighting usage patterns or incorrect auditing information.

2. The apparatus according to claim 1, further comprising an audit stage for processing auditing information from which incorrect data sets have been removed and for translating said auditing information into representative profiles.

3. The apparatus according to claim 1, wherein said checking stage is adapted to determine at least one root cause of irregularity and to correct a determined irregular data set into a regular data set based on said at least one root cause.

4. The apparatus according to claim 3, wherein said checking stage is adapted to correct said determined irregular data set based on at least one of an interpolation of valid measurement samples, a regression model, a rule model and a decision tree model.

5. The apparatus according to claim 1, wherein said auditing information comprises presence information and measurement values of energy consumption at a predetermined acquisition rate.

6. The apparatus according to claim 5, wherein said apparatus is adapted to apply an iterative approach, where said clustering stage first clusters data sets of a single area of a building or floor to determine irregular or incorrect patterns for said single areas and then clusters the areas in their entirety.

7. The apparatus according to claim 5, wherein said clustering stage is adapted to use a distance between presence profiles as said predetermined clustering criteria.

8. The apparatus according to claim 7, wherein said clustering stage is adapted to apply a clustering algorithm to cluster the profile using said distance.

9. A method of analyzing auditing information retrieved by a monitoring system, said method comprising:

a. clustering data sets of said auditing information based on at least one predetermined clustering criteria to obtain clusters with a sufficient commonality;

b. comparing obtained clusters based on at least one predetermined comparison criteria to determine potential candidates for irregular or incorrect clusters; and

c. cross-checking determined candidates with background information to determine irregular usage patterns or incorrect auditing information.

10. The method according to claim 9, further comprising processing auditing information from which incorrect data sets have been removed and translating said auditing information into representative profiles.

11. The method according to claim 10, further comprising determining at least one root cause of irregularity and correcting a determined irregular data set into a regular data set based on said at least one root cause.

12. The method according to claim 11, further comprising correcting said determined irregular data set based on at least one of an interpolation of valid measurement samples, a regression model, a rule model and a decision tree model.

13. The method according to claim 9, wherein said auditing information comprises presence information and measurement values of energy consumption at a predetermined acquisition rate.

14. The method according to claim 13, further comprising using a distance between presence profiles as said predetermined clustering criteria.

15. The method according to claim 14, further comprising applying a clustering algorithm to cluster the profile using said distance.

16. A computer program product comprising code means for producting the steps of claim 9 when run on a computing device.