Apparatus and method for prediction of an energy brown out

• US 10,153,637 B2
• Filed: 12/30/2015
• Issued: 12/11/2018
• Est. Priority Date: 03/31/2015
• Status: Active Grant

• Current Assignee: Enel X North America, Inc. (ENEL SPA)
• Original Assignee: Enernoc
• Inventors: Al-Mohssen, Husain, Paradis, Richard R., Bassa, Angela S.
• Primary Examiner(s): Suryawanshi, Suresh
• Application Number:
  US14/984,872
• Publication Number:
  US 20160292577A1
• Time in Patent Office: 1,077 Days
• Field of Search: 700291
• CPC Class Codes: G05B 13/026 using a predictor
• CPC Class Codes: H02J 3/00 Circuit arrangements for ac mains or ac distribution networks
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: H02J 3/144 Demand-response operation of the power transmission or distribution network

Abstract

A method for predicting when energy consumption on a grid will exceed normal production capacity for buildings within the grid including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating data sets for each of the buildings, each set comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each set are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and where each lag value is different; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; and using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption on the grid will exceed normal production capacity.

Claims

1. A system for predicting a time when energy consumption on a grid will exceed normal production capacity, the system comprising:

baseline data stores, configured to store a plurality of baseline energy use data sets for buildings corresponding to the grid;

a building lag optimizer, configured to receive identifiers for said buildings, and configured to retrieve said plurality of baseline energy use data sets from said baseline data stores for said buildings, and configured to generate energy use data sets for each of said buildings, each of said energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said energy use data sets are shifted by one of a plurality of lag values relative to said corresponding time and outside temperature values, and wherein each of said plurality of lag values is different from other ones of said plurality of lag values, and configured to perform a regression analysis on said each of said energy use data sets to yield corresponding regression model parameters and a corresponding residual, and configured to determine a least valued residual from all residuals yielded, said least valued residual indicating a corresponding energy lag for said each of said buildings, and regression model parameters that correspond to said least valued residual, and wherein said corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature;

a peak prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of said buildings, outside temperatures, said corresponding energy lag, and said corresponding regression model parameters, and configured to estimate a cumulative energy consumption for said buildings, and configured to predict the time when energy consumption on the grid will exceed normal production capacity; and

peak control, coupled to said peak prediction element, configured to receive the time when energy consumption on the grid will exceed normal production capacity, and configured to prepare and commence exceptional measures required to manage the energy consumption.

2. The system as recited in claim 1, wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values.

3. The system as recited in claim 1, wherein said corresponding time values are less than or equal to said corresponding energy lag for said each of said buildings.

4. The system as recited in claim 1, wherein said corresponding time values comprise hourly values and said plurality of lag values spans a 24-hour period.

5. The system as recited in claim 1, wherein said buildings comprise aggregated buildings that are powered by a substation on the grid.

6. The system as recited in claim 1, wherein said cumulative energy consumption for said buildings comprises a future energy consumption timeline as a function of said outside temperatures.

7. The system as recited in claim 1, wherein said each of said energy use data sets comprises a first portion of a corresponding each of said plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of said corresponding each of said plurality of baseline energy use data sets.

8. A system for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the system comprising:

baseline data stores, configured to store a plurality of baseline energy use data sets for the buildings within the grid;

a building lag optimizer, configured to determine an energy lag for one of the buildings, said building lag optimizer comprising;

a thermal response processor, configured to generate a plurality of energy use data sets for said one of the buildings, each of said plurality of energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein said energy consumption values within said each of said plurality of energy use data sets are shifted by one of a plurality of lag values relative to said corresponding time and outside temperature values, and wherein each of said plurality of lag values is different from other ones of said plurality of lag values; and

a regression engine, coupled to said thermal response processor, configured to receive said plurality of energy use data sets, and configured to perform a regression analysis on said each of said plurality of energy use data sets to yield corresponding regression model parameters and a corresponding residual;

wherein said thermal response processor determines a least valued residual from all residuals yielded by said regression engine, said least valued residual indicating said energy lag for said one of the buildings, and wherein said energy lag describes a transient energy consumption period preceding a change in outside temperature;

a peak prediction element, coupled to said building lag optimizer and to weather stores, configured to receive, for each of the buildings in the grid, outside temperatures, said corresponding energy lag, and said corresponding regression model parameters, and configured to estimate a cumulative energy consumption for the buildings, and configured to predict the time when energy consumption on the grid will exceed normal production capacity; and

peak control, coupled to said peak prediction element, configured to receive the time when energy consumption on the grid will exceed normal production capacity, ad configured to prepare and commence exceptional measures required to manage the energy consumption.

9. The system as recited in claim 8, wherein said plurality of lag values indicates shifts of said energy consumption values to different time and outside temperature values.

10. The system as recited in claim 8, wherein said corresponding time values are less than or equal to said corresponding energy lag for the each of the buildings.

11. The system as recited in claim 8, wherein said corresponding time values comprise hourly values and said plurality of lag values spans a 24-hour period.

12. The system as recited in claim 8, wherein the buildings comprise aggregated buildings that are powered by a substation on the grid.

13. The system as recited in claim 8, wherein said cumulative energy consumption for the buildings comprises a future energy consumption timeline as a function of said outside temperatures.

14. The system as recited in claim 8, wherein said each of said plurality of energy use data sets comprises a first portion of a corresponding each of said plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of said corresponding each of said plurality of baseline energy use data sets.

15. A method for predicting a time when energy consumption on a grid will exceed normal production capacity for buildings within the grid, the method comprising:

retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores;

generating a plurality of energy use data sets for each of the buildings, each of the plurality of energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, wherein the energy consumption values within the each of the plurality of energy use data sets are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values, and wherein each of the plurality of lag values is different from other ones of the plurality of lag values;

performing a regression analysis on the each of the plurality of energy use data sets to yield corresponding regression model parameters and a corresponding residual;

determining a least valued residual from all residuals yielded by said performing a regression analysis, the least valued residual indicating a corresponding energy lag for the each of the buildings, wherein the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature;

using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption on the grid will exceed normal production capacity; and

receiving the time when energy consumption on the grid will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

16. The method as recited in claim 15, wherein the plurality of lag values indicates shifts of the energy consumption values to different time and outside temperature values.

17. The method as recited in claim 15, wherein the corresponding time values are less than or equal to the energy lags.

18. The method as recited in claim 15, wherein the corresponding time values comprise hourly values and the plurality of lag values spans a 24-hour period.

19. The method as recited in claim 15, wherein the buildings comprise aggregated buildings that are powered by a substation on the grid.

20. The method as recited in claim 15, wherein the cumulative energy consumption for the buildings comprises a future energy consumption timeline as a function of the outside temperatures.

21. The method as recited in claim 15, wherein the each of the plurality of energy use data sets comprises a first portion of a corresponding each of the plurality of baseline energy use data sets, and wherein required energy consumption values resulting from shifts are taken from a second portion of the corresponding each of the plurality of baseline energy use data sets.