SYSTEMS AND METHODS FOR GENERATING AN ENERGY USAGE MODEL FOR A BUILDING

• US 20160154389A1
• Filed: 02/04/2016
• Published: 06/02/2016
• Est. Priority Date: 06/22/2009
• Status: Active Grant

• Current Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Drees, Kirk H., Kummer, James P., Wenzel, Michael J.
• Granted Patent: US 9,575,475 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G05B 13/048 using a predictor
• CPC Class Codes: G05F 1/66 Regulating electric power
• 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 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00006 characterised by information or instructions transport means between the monitoring, controlling or managing units and monitored, controlled or operated power network element or electrical equipment
• CPC Class Codes: H02J 2203/20 Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y02E 60/00 Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 40/12 characterised by data transport means between the monitoring, controlling or managing units and monitored, controlled or operated electrical equipment
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A building management system (BMS) includes a baseline model generator configured to receive an initial set of predictor variables for potential use in an energy usage model for a building, generate a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables, remove one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables, generate a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables, calculate a test statistic for the removed variable using a difference between the first set of coefficients and the second set of coefficients, and automatically select the removed predictor variable for use in the baseline energy usage model in response the test statistic exceeding a critical value.

Claims

1. A building management system comprising:

one or more sensors configured to measure one or more predictor variables for potential use in a baseline energy usage model for a building; and

a baseline model generator comprising a processing circuit configured to;

receive an initial set of predictor variables including the one or more measured predictor variables;

generate a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables;

remove one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables;

generate a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables;

calculate a test statistic for the removed variable using a difference between the first set of coefficients and the second set of coefficients; and

automatically select the removed predictor variable for use in the baseline energy usage model in response to the test statistic exceeding a critical value.

2. The system of claim 1, wherein the processing circuit is configured to determine that the removed predictor variable is statistically significant to energy usage of the building in response to the test statistic exceeding the critical value.

3. The system of claim 1, wherein the processing circuit is configured to:

generate the first set of coefficients by applying a regression analysis to the initial set of predictor variables; and

generate the second set of coefficients by applying a regression analysis to the subset of the initial set of predictor variables.

4. The system of claim 1, wherein the initial set of predictor variables comprises a calculated predictor variable based on one or more of the measured predictor variables.

5. The system of claim 1, wherein the processing circuit is configured to:

receive a user selection of one or more predictor variables via a user interface device; and

add one or more predictor variables to the initial set of variables based on the user selection.

6. The system of claim 1, wherein the processing circuit is configured to:

receive a user selection of a data source via a user interface device;

identify one or more predictor variables provided by the data source; and

add the one or more identified predictor variables to the initial set of predictor variables.

7. The system of claim 1, wherein the processing circuit is configured to:

collect data points for each of the predictor variables in the initial set of predictor variables, each data point having a data value and a time value;

identify a time period during which all of the predictor variables in the initial set of predictor variables have a threshold number of data points based on the time values for each of the data points; and

using the identified time period to generate the first set of coefficients for the baseline energy usage model.

8. The system of claim 1, wherein the processing circuit is configured to:

use a first set of building data collected during a first time period to generate a third set of coefficients for the baseline energy usage model;

use a second set of building data collected during a second time period to generate a fourth set of coefficients for the baseline energy usage model; and

detect a fault in the building in response to a difference between the third set of coefficients and the fourth set of coefficients exceeding a threshold value.

9. The system of claim 1, wherein the processing circuit is configured to:

use a first set of building data collected prior to implementing energy conservation measures to generate a third set of coefficients for the baseline energy usage model;

use a second set of building data collected after implementing the energy conservation measures to generate a fourth set of coefficients for the baseline energy usage model; and

estimate an amount of energy saving resulting from the energy conservation measures based on a difference between the third set of coefficients and the fourth set of coefficients.

10. The system of claim 9, wherein the processing circuit is configured to:

apply the second set of building data to the baseline energy model with the third set of coefficients to estimate an energy usage of the building had the energy conservation measures not been implemented; and

compare the estimated energy usage to an actual energy usage of the building after implementing the energy conservation measures to estimate an amount of energy savings resulting from the energy conservation measures.

11. A method for generating a baseline energy usage model for a building, the method comprising:

measuring one or more predictor variables for potential use in the baseline energy usage model using one or more sensors of the building;

receiving, at a processing circuit, an initial set of predictor variables including the one or more measured predictor variables;

generating, by the processing circuit, a first set of coefficients for the baseline energy usage model based on the initial set of predictor variables;

removing, by the processing circuit, one of the predictor variables from the initial set of predictor variables to create a subset of the initial set of predictor variables;

generating, by the processing circuit, a second set of coefficients for the baseline energy usage model based on the subset of the initial set of predictor variables;

calculating, by the processing circuit, a test statistic for the removed predictor variable using a difference between the first set of coefficients and the second set of coefficients; and

automatically selecting, by the processing circuit, the removed predictor variable for use in the baseline energy usage model in response to the test statistic exceeding a critical value.

12. The method of claim 11, further comprising determining that the removed predictor variable is statistically significant to energy usage of the building in response to the test statistic exceeding the critical value.

13. The method of claim 11, wherein:

generating the first set of coefficients comprises applying a regression analysis to the initial set of predictor variables; and

generating the second set of coefficients comprises applying a regression analysis to the subset of the initial set of predictor variables.

14. The method of claim 11, wherein the initial set of predictor variables comprises a calculated predictor variable based on one or more of the measured predictor variables.

15. The method of claim 11, further comprising:

receiving a user selection of one or more predictor variables via a user interface device; and

adding one or more predictor variables to the initial set of predictor variables based on the user selection.

16. The method of claim 11, further comprising:

receiving a user selection of a data source via a user interface device;

identifying one or more predictor variables provided by the data source; and

adding the one or more identified predictor variables to the initial set of variables.

17. The method of claim 11, further comprising:

collecting data points for each of the predictor variables in the initial set of predictor variables, each data point having a data value and a time value;

identifying a time period during which all of the predictor variables in the initial set of predictor variables have a threshold number of data points based on the time values for each of the data points; and

using the identified time period to generate the first set of coefficients for the baseline energy usage model.

18. The method of claim 11, further comprising:

using a first set of building data collected during a first time period to generate a third set of coefficients for the baseline energy usage model;

using a second set of building data collected during a second time period to generate a fourth set of coefficients for the baseline energy usage model; and

detecting a fault in the building in response to a difference between the third set of coefficients and the fourth set of coefficients exceeding a threshold value.

19. The method of claim 11, further comprising:

using a first set of building data collected prior to implementing energy conservation measures to generate a third set of coefficients for the baseline energy usage model;

using a second set of building data collected after implementing the energy conservation measures to generate a fourth set of coefficients for the baseline energy usage model; and

estimating an amount of energy saving resulting from the energy conservation measures based on a difference between the third set of coefficients and the fourth set of coefficients.

20. The method of claim 19, further comprising:

applying the second set of building data to the baseline energy model with the third set of coefficients to estimate an energy usage of the building had the energy conservation measures not been implemented; and

comparing the estimated energy usage to an actual energy usage of the building after implementing the energy conservation measures to estimate an amount of energy savings resulting from the energy conservation measures.