SYSTEMS AND METHODS FOR ANALYZING ENERGY CONSUMPTION MODEL DATA

• US 20140278165A1
• Filed: 03/13/2014
• Published: 09/18/2014
• Est. Priority Date: 03/14/2013
• Status: Abandoned Application

• Current Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Drees, Kirk H., Nesler, Clay G., Wenzel, Michael J.
• Application Number:
  US14/209,234
• Publication Number:
  US 20140278165A1
• Time in Patent Office: Days
• US Class Current: 702/61
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/0639 Performance analysis of employees; Performance analysis of enterprise or organisation operations
• CPC Class Codes: G06Q 50/08 Construction

Abstract

A building'"'"'s energy consumption may be modeled using weather data, utility billing data, or other data regarding the building. The resulting model data may be analyzed to detect a shift in the model data, which may indicate the presence of a fault condition. Changes to the model'"'"'s coefficients that would result from an upgrade, energy conservation measure, or other action may also be used to predict the resulting Energy Star score for the building.

Claims

1. A method for evaluating a fault condition in a building comprising:

generating, by a processing circuit, an energy consumption model for the building;

using the energy consumption model and input data from different time windows to generate model data;

analyzing the model data to detect a non-routine change in the model data across the different time windows; and

providing an indication of a potential fault condition based on the non-routine change in the model data being detected.

2. The method of claim 1, wherein the input data comprises billing data from a utility that supplies energy to the building, and wherein the input data comprises weather data for the geographic area in which the building is located.

3. The method of claim 2, further comprising:

normalizing the model data by driving the energy consumption model using typical meteorological year (TMY) data to account for energy consumption changes attributable to routine weather changes.

4. The method of claim 1, further comprising:

using the generated model data to train a control chart having control limits based on the model data, wherein the non-routine change in the model data is detected by comparing model data associated with a new time window to the control limits of the control chart.

5. The method of claim 4, wherein the control chart is an exponentially weighted moving average (EWMA) control chart.

6. The method of claim 4, wherein the control chart comprises at least one of:

a moving average control chart, an Xbar control chart, a Shewhart control chart, or a cumulative sum control chart.

7. The method of claim 1, further comprising:

receiving a test observation corresponding to model data from a new time window; and

generating a confidence interval for a point estimate based on the model data, wherein the non-routine change in the model data is detected by comparing model data associated with a new time window to the control limits of the control chart.

8. The method of claim 1, further comprising:

using a null-hypothesis test to detect the non-routine change in the model data.

9. The method of claim 1, further comprising:

calculating one or more recursive residual values using the model data; and

analyzing the one or more recursive residual values to detect the non-routine change in the model data.

10. The method of claim 9, wherein the one or more recursive residual values are analyzed using a statistical process control chart.

11. The method of claim 10, wherein the control chart is an exponentially weighted moving average (EWMA) control chart.

12. The method of claim 9, wherein the one or more recursive residual values are analyzed using a cumulative sum test or a cumulative sum of squares test.

13. A system for evaluating a fault condition in a building comprising a processing circuit configured to generate an energy consumption model for the building, wherein the processing circuit is configured to use the energy consumption model and input data from different time windows to generate model data, wherein the processing circuit is configured to analyze the model data to detect a non-routine change in the model data across the different time windows, and wherein the processing circuit is configured to provide an indication of a potential fault condition based on the non-routine change in the model data being detected.

14. The system of claim 13, wherein the input data comprises billing data from a utility that supplies energy to the building, and wherein the input data comprises weather data for the geographic area in which the building is located.

15. The system of claim 14, wherein the processing circuit is configured to normalize the model data by driving the energy consumption model using typical meteorological year (TMY) data to account for energy consumption changes attributable to routine weather changes.

16. The system of claim 13, wherein the processing circuit is configured to use the generated model data to train a control chart having control limits based on the model data, wherein the non-routine change in the model data is detected by comparing model data associated with a new time window to the control limits of the control chart.

17. The system of claim 16, wherein the control chart is an exponentially weighted moving average (EWMA) control chart.

18. The system of claim 16, wherein the control chart comprises at least one of:

a moving average control chart, an Xbar control chart, a Shewhart control chart, or a cumulative sum control chart.

19. The system of claim 13, wherein the processing circuit is configured to generate a confidence interval for a point estimate based on the model data, wherein the non-routine change in the model data is detected by comparing model data associated with a new time window to the control limits of the control chart.

20. The system of claim 13, wherein the processing circuit is configured to use a null-hypothesis test to detect the non-routine change in the model data.

21. The system of claim 13, wherein the processing circuit is configured to calculate one or more recursive residual values using the model data, wherein the processing circuit is configured to analyze the one or more recursive residual values to detect the non-routine change in the model data.

22. The system of claim 21, wherein the one or more recursive residual values are analyzed using a statistical process control chart.

23. The system of claim 22, wherein the control chart is an exponentially weighted moving average (EWMA) control chart.

24. The system of claim 21, wherein the one or more recursive residual values are analyzed using a cumulative sum test or a cumulative sum of squares test.

25. A method for determining a change to an energy score of a building comprising:

generating, by a processing circuit, an energy consumption model for the building;

using the energy consumption model and input data regarding the building to calculate baseline model data, the baseline model data being associated with a baseline energy score;

receiving an identifier representing a proposed change to the operation of the building, the received identifier being associated with a change to the model data; and

calculating an energy score associated with the proposed change using the baseline model data, the change to the model data associated with the proposed change, and the baseline energy score.

26. The method of claim 25, wherein the energy score comprises an Energy Star score associated with the proposed change.

27. The method of claim 26, further comprising:

normalizing the baseline model data using typical meteorological year (TMY) data to determine a baseline normalized annual consumption intensity value;

using the change to the model data associated with the received identifier and the TMY data to determine a normalized annual consumption intensity value associated with the proposed change;

calculating an energy use intensity ratio relating the baseline normalized annual consumption energy intensity value to the normalized annual consumption intensity value associated with the proposed change; and

using the energy use intensity ratio to calculate the Energy Star score associated with the proposed change.

28. The method of claim 27, further comprising:

calculating a baseline energy efficiency ratio for the building;

calculating an energy efficiency ratio associated with the proposed change using the baseline energy efficiency ratio and the energy use intensity ratio; and

using the energy efficiency ratio associated with the proposed change to calculate the Energy Star score associated with the proposed change.

29. The method of claim 28, further comprising:

using an inverse gamma function to calculate the baseline energy efficiency ratio.

30. The method of claim 29, further comprising:

using the energy efficiency ratio associated with the proposed change with a gamma function to calculate the Energy Star score associated with the proposed change.

31. The method of claim 25, wherein the proposed change to the operation of the building comprises at least one of:

implementing an energy conservation measure or altering equipment in the building.