SYSTEMS AND METHODS FOR BUILDING ENERGY USE BENCHMARKING

• US 20140142905A1
• Filed: 11/16/2012
• Published: 05/22/2014
• Est. Priority Date: 11/16/2012
• Status: Active Grant

• Current Assignee: Tyco Fire & Security GmbH (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Drees, Kirk H., Wenzel, Michael J.
• Granted Patent: US 9,135,667 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 17/10 Complex mathematical operations function generation by table look-up G06F1/03; evaluation of elementary functions by calculation G06F7/544
• CPC Class Codes: G06F 30/20 Design optimisation, verification or simulation optimisation, verification or simulation of circuit designs G06F30/30
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/06393 Score-carding, benchmarking or key performance indicator [KPI] analysis
• CPC Class Codes: G06Q 50/08 Construction
• CPC Class Codes: Y02P 90/82 Energy audits or management systems therefor

Abstract

Systems and methods for using an energy use model of a building for benchmarking is shown and described. An exemplary method includes receiving building data indicative of one or more characteristics of the building. The method further includes generating an energy use model for the building based on the building data and using the energy use model to generate statistics for the building. The method also includes identifying one or more other buildings having the same classification as the building. The method yet further includes comparing the generated statistics for the building to statistics for the identified one or more other buildings and providing an indication of the comparison.

Claims

1. A method for using an energy use model of a building comprising:

receiving, at a processing circuit, building data indicative of one or more characteristics of the building;

generating, by the processing circuit, an energy use model for the building based on the building data;

using the energy use model to generate statistics for the building;

identifying, by the processing circuit, one or more other buildings having the same classification as the building, each of the one or more other buildings having associated statistics generated from energy use models;

comparing, by the processing circuit, the generated statistics for the building to statistics for the identified one or more other buildings; and

providing, by the processing circuit, an indication of the comparison.

2. The method of claim 1, further comprising:

determining a classification for the building based on the one or more characteristics of the building.

3. The method of claim 1, wherein the energy use model comprises a regression model.

4. The method of claim 3, wherein the statistics for the building correspond to a normalized annual energy consumption intensity generated by driving the energy use model with typical meteorological year (TMY) data or to a parameter vector estimated using the energy use model.

5. The method of claim 1, wherein the generated statistics for the building are compared to a probability distribution function of the statistics for the identified one or more other buildings.

6. The method of claim 1, wherein the building data comprises a location of the building and the classification for the building is determined based in part on the location of the building.

7. The method of claim 1, wherein the building data comprises an indication of a usage type for the building and the classification for the building is determined based in part on the usage type.

8. The method of claim 1, further comprising:

determining a generated statistic to be an outlier using at least one of;

the generalized extreme studentized deviate (GESD) test, Wilks'"'"' method, or cluster analysis.

9. The method of claim 1, wherein two or more of the generated statistics for the building are compared to two or more of the statistics for the other buildings using multivariate statistical analysis.

10. The method of claim 1, further comprising:

determining the building to be an outlier based in part on a Mahalanobis distance between the statistics for the building and the statistics for the one or more other buildings; and

providing, by the processing circuit, an indication of the determined outlier.

18. The method of claim 1, further comprising:

determining a generated statistic to be an outlier using at least one of;

the generalized extreme studentized deviate (GESD) test, Wilks'"'"' method, or cluster analysis.

20. The system of claim 18, wherein the processing circuit is further operable to determine the building to be an outlier based in part on a Mahalanobis distance between the statistics for the building and the statistics for the one or more other buildings and to provide an indication of the determined outlier.

11. A system for using an energy use model of a building comprising a processing circuit operable to:

receive building data indicative of one or more characteristics of the building;

generate an energy use model for the building based on the building data;

use the energy use model to generate statistics for the building;

identify one or more other buildings having the same classification as the building, each of the one or more other buildings having associated statistics generated from energy use models;

compare the generated statistics for the building to statistics for the identified one or more other buildings; and

provide an indication of the comparison.

12. The system of claim 11, wherein the processing circuit is further configured to determine a classification for the building based on the one or more characteristics of the building;

13. The system of claim 11, wherein the energy use model comprises a regression model.

14. The system of claim 11, wherein the statistics for the building correspond to an annual energy consumption intensity generated by driving the energy use model using typical meteorological year (TMY) data or to a parameter vector estimated using the energy use model.

15. The system of claim 11, wherein the generated statistics for the building are compared to a probability distribution function of the statistics for the identified one or more other buildings.

16. The system of claim 11, wherein the building data comprises a location of the building and the classification for the building is determined based in part on the location of the building.

17. The system of claim 11, wherein the building data comprises an indication of a usage type for the building and the classification for the building is determined based in part on the usage type.

19. The system of claim 11, wherein two or more of the generated statistics for the building are compared to two or more of the statistics for the other buildings using multivariate statistical analysis.

21. A computer-readable storage medium having machine instructions stored therein, the instructions being executable by a processor to cause the processor to perform operations, the operations comprising:

receiving building data indicative of one or more characteristics of the building;

generating an energy use model for the building based on the building data;

using the energy use model to generate statistics for the building;

determining a classification for the building based on the one or more characteristics of the building;

identifying one or more other buildings having the same classification as the building, each of the one or more other buildings having associated statistics generated from energy use models;

comparing the generated statistics for the building to statistics for the identified one or more other buildings; and

providing an indication of the comparison.

22. The computer-readable storage medium of claim 21, wherein the statistics for the building correspond to an annual energy consumption intensity or a parameter vector estimated using the energy use model.

23. The computer-readable storage medium of claim 21, wherein the building data comprises at least one of a location of the building or an indication of a usage type for the building, wherein the classification for the building is determined based in part on the location of the building or the usage type for the building.

24. The computer-readable storage medium of claim 21, wherein the operations further comprise:

determining the building to be an outlier based in part on a Mahalanobis distance between the statistics for the building and the statistics for the one or more other buildings; and

providing an indication of the determined outlier.