Systems and methods for measuring and verifying energy savings in buildings

• US 8,532,808 B2
• Filed: 02/08/2011
• Issued: 09/10/2013
• 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., Wenzel, Michael J.
• Primary Examiner(s): Padmanabhan, Kavita
• Assistant Examiner(s): STEVENS, THOMAS H
• Application Number:
  US13/023,392
• Publication Number:
  US 20110130886A1
• Time in Patent Office: 945 Days
• Field of Search: 700/109, 700/174, 700/275, 700/276, 700/291
• US Class Current: 700/109
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: G06F 1/3203 Power management, i.e. event-based initiation of a power-saving mode
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04
• CPC Class Codes: H02J 13/00016 using a wired telecommunication network or a data transmission bus
• 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 70/30 Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
• 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 40/70 Smart grids as climate change mitigation technology in the energy generation sector
• 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 20/20 End-user application control systems
• CPC Class Codes: Y04S 40/124 using wired telecommunication networks or data transmission busses
• CPC Class Codes: Y04S 40/20 Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Abstract

A computer system for use with a building management system in a building includes a processing circuit configured to use historical data received from the building management system to automatically select a set of variables estimated to be significant to energy usage in the building. The processing circuit is further configured to apply a regression analysis to the selected set of variables to generate a baseline model for predicting energy usage in the building.

Claims

1. A computer system for use with a building management system in a building, comprising:

a processing circuit configured to use historical data received from the building management system to automatically select a set of variables for inclusion in a baseline model for predicting energy usage in the building by performing statistical hypothesis testing on potential variables;

wherein the processing circuit is further configured to apply a regression analysis to the selected set of variables to generate the baseline model for predicting energy usage in the building

wherein the processing circuit determines which of energy days and degree days to use in the regression analysis by calculating an enthalpy balance point estimated to minimize energy usage in the building, calculating a temperature balance point estimated to minimize energy usage in the building, calculating a historical energy day value based on the calculated enthalpy balance point, calculating a historical degree day value based on the calculated temperature balance point, and comparing a variance associated with the historical energy day value to a variance associated with the historical degree day value.

2. The computer system of claim 1, wherein the regression analysis is a partial least squares regression.

3. The computer system of claim 1, wherein the processing circuit is configured to re-execute the regression analysis with a different regression parameter in response to a determination that the baseline model does not accurately estimate energy usage for test data for a historical time period.

4. The computer system of claim 3, wherein the different regression parameter comprises at least one of a regression model order and a number of variables in the set of variables.

5. The computer system of claim 1, wherein the processing circuit is further configured to store the baseline model in memory for use in at least one of:

a calculation of energy savings, a validation of an expected performance, and a detection for whether baseline contract terms have changed.

6. The computer system of claim 1, wherein the processing circuit is configured to calculate an enthalpy value and to use the enthalpy value in the regression analysis and as part of the baseline model for predicting energy usage.

7. The computer system of claim 6, wherein the processing circuit uses the calculated enthalpy in the regression analysis and as part of the baseline model rather than using separate variables of temperature and humidity in the selected set of variables.

8. The computer system of claim 1, wherein the processing circuit is configured to calculate enthalpy and to use the calculated enthalpy to calculate heating and cooling energy days;

and

wherein the processing circuit uses the calculated heating and cooling energy days in the regression analysis rather than degree days.

9. The computer system of claim 1, wherein the processing circuit is configured to automatically identify non-representative data of the historical data and to remove or replace the non-representative data to improve integrity of the automatic selection and the regression analysis;

wherein the processing circuit excludes a variable from the selected set of variables in response to a determination that there is insufficient data for the variable;

wherein the processing circuit identifies the non-representative data by conducting at least one of an outlier analysis, a data cluster analysis, stuck data analysis, and a missing data analysis.

10. The computer system of claim 1, wherein the processing circuit is configured to calculate the enthalpy balance point estimated to minimize energy usage in the building by:

performing an iteratively reweighed least squares process on the enthalpy balance point.

11. A method for use with a building management system in a building, comprising:

receiving historical data from the building management system;

using the historical data to automatically select a set of variables for inclusion in a baseline model for predicting energy usage in the building by performing statistical hypothesis testing on potential variables;

applying a regression analysis to the selected set of variables to generate the baseline model for predicting energy usage in the building,

determining which of energy days and degree days to use in the regression analysis by calculating an enthalpy balance point estimated to minimize energy usage in the building, calculating a temperature balance point estimated to minimize energy usage in the building, calculating a historical energy day value based on the calculated enthalpy balance point, calculating a historical degree day value based on the calculated temperature balance point, and comparing a variance associated with the historical energy day value to a variance associated with the historical degree day value.

12. The method of claim 11, wherein the regression analysis is a partial least squares regression.

13. The method of claim 11, further comprising:

re-executing the regression analysis with a different regression parameter in response to a determination that the baseline model does not accurately estimate energy usage for test data for a historical time period.

14. The method of claim 13, wherein the different regression parameter comprises at least one of a regression model order and a number of variables in the set of variables.

15. The method of claim 11, further comprising:

storing the baseline model in memory for use in at least one of;

a calculation of energy savings, a validation of an expected performance, and a detection for whether baseline contract terms have changed.

16. The method of claim 11, further comprising:

calculating an enthalpy value; and

using the calculated enthalpy value in the regression analysis and as part of the baseline model for predicting energy usage.

17. The method of claim 16, further comprising:

using calculated enthalpy in the regression analysis and as part of the baseline model rather than using separate variables of temperature and humidity in the selected set of variables.

18. The method of claim 11, further comprising:

calculating enthalpy;

using the calculated enthalpy to calculate heating and cooling energy days, wherein the calculated heating and cooling energy days are used in the regression analysis rather than degree days.

19. The method of claim 11, further comprising:

automatically identifying non-representative data from the historical data;

removing or replacing the non-representative data to improve integrity of the automatic selection and the regression analysis;

excluding a variable from the selected set of variables in response to a determination that there is insufficient good data for the variable;

wherein identifying the non-representative data comprises conducting at least one of an outlier analysis, a data cluster analysis, stuck data analysis, and a missing data analysis.

20. The method of claim 11, wherein calculating the enthalpy balance point estimated to minimize energy usage in the building comprises:

performing an iteratively reweighed least squares process on the enthalpy balance point.

21. Computer-readable non-transitory media with computer-executable instructions embodied thereon that when executed by a computer system perform a method for use with a building management system in a building, wherein the computer-executable instructions comprise:

instructions for using historical data from the building management system to select a set of variables for inclusion in a baseline model for predicting energy usage in the building by performing statistical hypothesis testing on potential variables;

instructions for applying a regression analysis to the selected set of variables to generate the baseline model for predicting energy usage in the building, and

instructions for determining which of energy days and degree days to use in the regression analysis by calculating an enthalpy balance point estimated to minimize energy usage in the building, calculating a temperature balance point estimated to minimize energy usage in the building, calculating a historical energy day value based on the calculated enthalpy balance point, calculating a historical degree day value based on the calculated temperature balance point, and comparing a variance associated with the historical energy day value to a variance associated with the historical degree day value.