Systems and methods for energy consumption and energy demand management

• US 9,569,804 B2
• Filed: 03/14/2013
• Issued: 02/14/2017
• Est. Priority Date: 08/27/2012
• Status: Active Grant

• Current Assignee: Gridium, Inc.
• Original Assignee: Gridium, Inc.
• Inventors: Stein, Adam R., Arnold, Thomas A., Anderson, Gregory D.
• Primary Examiner(s): Lo, Kenneth M
• Assistant Examiner(s): Huntley, Michael J
• Application Number:
  US13/830,919
• Publication Number:
  US 20140058572A1
• Time in Patent Office: 1,433 Days
• Field of Search: 700/291
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/06 Energy or water supply
• CPC Class Codes: H02J 13/00004 characterised by the power network being locally controlled
• CPC Class Codes: H02J 13/00028 involving the use of Internet protocols
• CPC Class Codes: H02J 2310/16 The load or loads being an Information and Communication Technology [ICT] facility
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand

Abstract

Various of the disclosed embodiments contemplate a computer-implemented method of energy consumption and energy demand management in a building. In accordance with some embodiments, interval energy data of a specific building may be collected with a fixed time interval and paired with local historical weather data and other forms of operational data, as well as financial data including historical utility bills, utility rate structures and billing cycle dates. Paired energy interval data and the local historical weather data may be analyzed according to one or more analytic algorithms.

Claims

1. A computer-implemented method comprising:

collecting energy interval data associated with a building at a time interval, the energy interval data indicative of an energy use associated with the building;

collecting local historical weather data associated with the building;

creating a statistical model for predicting energy use as a function of weather and operational inputs, the statistical model created based on the energy interval data and upon the local historical weather data;

analyzing paired energy interval data and local historical weather data according to one or more analytic algorithms, using the statistical model, the analyzing including decomposing the energy use into multiple categories, and analyzing each of the categories, wherein the analyzing includes;

determining a peak to date energy use value that is indicative of a peak energy use associated with the building until a current time in a specified period,

determining, for a forecasted period in the specified period, multiple demand values indicative of predicted energy demand, the demand values including a first set of predicted demand values whose peak probabilities are above their corresponding curtailment probabilities and a second set of predicted demand values whose peak probabilities are below their corresponding curtailment probabilities; and

providing, for display at a computer associated with a user, one or more options to reduce energy bills of the building based on the analysis, the providing including providing the one or more options to reduce the energy bills by reducing the energy use in different categories, wherein the one or more options includes a target value that is indicative of a value to which energy demand in the forecasted period is to be curtailed and a particular period in the forecasted period when the energy demand is to be curtailed, the target value determined as a function of the peak-to-date energy use value, the first set of predicted demand values and the second set of predicted demand values.

2. The computer-implemented method of claim 1, further comprising collecting additional operational data, the additional operational data including at least one of occupancy, building hours, manufacturing output, and economic activity, and wherein the statistical model is created at least in part based on the additional operational data.

3. The computer-implemented method of claim 1, further comprising collecting energy financial data, the energy financial data including at least one of a tariff, historical bills, and historical bill cycle information.

4. The computer-implemented method of claim 1, wherein providing one or more options to reduce energy bills of the building comprises:

determining that a portion of past or forecast energy use or demand exceeds a threshold or matches a predetermined pattern; and

sending an automatic alert based on the data exceeding the threshold or matching the pattern.

5. The computer-implemented method of claim 1, further comprising analyzing interactions between use charges, demand charges, special incentive programs, rate options and time-of-use effects.

6. The computer-implemented method of claim 1, further comprising:

generating one or more analysis reports based on at least the energy data and the analysis results.

7. The computer-implemented method of claim 6, wherein each of the one or more analysis reports includes a link to a web site with additional charts and analytic results.

8. The computer-implemented method of claim 1, further comprising:

collecting weather forecast data of the building; and

generating a forecasting report based on demand and use predictions of the building.

9. The computer-implemented method of claim 8, wherein the forecasting report includes an advanced warning when the period of highest energy demand of the building is likely to occur within current or future billing cycles.

10. A system for energy consumption and energy demand management, the system comprising:

one or more processors in a cloud-based platform; and

a computer readable device storing instructions which, when executed using said one or more processors, cause the cloud-based platform to perform operations comprising;

collecting energy interval data of a building with a fixed time interval, the energy interval data indicative of an energy use associated with the building;

collecting local historical weather data associated with the building;

creating a statistical model of building energy use based on local weather;

analyzing paired energy interval data and local historical weather data according to one or more analytic algorithms, the analyzing including decomposing the energy use into multiple categories by applying the statistical model, and analyzing each of the categories, wherein the analyzing includes;

determining a peak-to-date energy use value that is indicative of a peak energy use associated with the building until a current time in a specified period,

determining, for a forecasted period in the specified period, multiple demand values indicative of predicted energy demand, the demand values including a first set of predicted demand values whose peak probabilities are above their corresponding curtailment probabilities and a second set of predicted demand values whose curtailment probabilities are below their corresponding curtailment probabilities; and

providing, for display at a computer associated with a user, one or more options to reduce energy bills of the building based on the analysis, the providing including providing the one or more options to reduce the energy bills by reducing the energy use in different categories, wherein the one or more options includes a target value that is indicative of a value to which energy demand in the forecasted period is to be curtailed and a particular period in the forecasted period when the energy demand is to be curtailed, the target value determined as a function of the peak-to-date energy use value, the first set of predicted demand values and the second set of predicted demand values.

11. A system as recited in claim 10, wherein the operations further comprise:

collecting financial data of the building, the financial data including historical utility bills, utility rate structures and billing cycle dates.

12. A system as recited in claim 10, wherein the operations further comprise:

analyzing interactions between use charges, demand charges, special incentive programs, rate options and time-of-use effects.

13. A system as recited in claim 10, wherein the operations further comprise:

generating one or more analysis reports based on at least the energy data and the analysis results.

14. A system as recited in claim 13, wherein each of the one or more analysis reports includes a link to a web site with additional charts and analytic results.

15. A system as recited in claim 10, wherein the operations further comprise:

collecting weather forecast data of the building; and

generating a forecasting report on demand and use predictions of the building.

16. A system as recited in claim 15, wherein the forecasting report includes an advanced warning when the period of highest energy demand of the building is likely to occur in the current or future billing cycles.

17. A system as recited in claim 10, wherein the local historical weather data is collected with the fixed time interval.

18. A system as recited in claim 10, wherein the fixed time interval is a 15-minute interval.

19. A system for energy consumption and energy demand management comprising:

a rate engine configured to simulate utility tariff logic;

one or more processors in a cloud-based platform; and

a computer readable device storing instructions which, when executed using said one or more processors, cause the system to perform operations comprising;

collecting energy interval data of a building with a fixed time interval, the energy interval data indicative of an energy use associated with the building;

collecting local historical weather data associated with the building;

creating a statistical model of building energy use based on local weather;

analyzing paired energy interval data and local historical weather data according to one or more analytic algorithms, the analyzing including decomposing the energy use into multiple categories by applying the statistical model, and analyzing each of the categories, wherein the analyzing includes;

determining a peak-to-date energy use value that is indicative of a peak energy use associated with the building until a current time in a specified period,

determining, for a forecasted period in the specified period, multiple demand values indicative of predicted energy demand, the demand values including a first set of predicted demand values whose peak probabilities are above their corresponding curtailment probabilities and a second set of predicted demand values whose peak probabilities are below their corresponding curtailment probabilities; and

providing, for display at a computer associated with a user, one or more options to reduce energy bills of the building based on the analysis and based upon utility tariff logic generated by the rate engine, the providing including providing the one or more options to reduce the energy bills by reducing the energy use in different categories, wherein the one or more options includes a target value that is indicative of a value to which energy demand in the forecasted period is to be curtailed and a particular period in the forecasted period when the energy demand is to be curtailed, the target value determined as a function of the peak-to-date energy use value, the first set of predicted demand values and the second set of predicted demand values.

20. A system as recited in claim 19, wherein analyzing paired energy interval data and local historical weather data includes quantifying energy use patterns into a dollar amount in energy costs.