Energy brown out prediction system

Patent metadata

Field	Value
Publication number	US-2016294185-A1
Application number	US-201514674041-A
Country	US
Kind code	A1
Filing date	Mar 31, 2015
Priority date	Mar 31, 2015
Publication date	Oct 6, 2016
Grant date	—

Abstract

A method for predicting when energy consumption on a grid will exceed normal production capacity for buildings within the grid including 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.

First claim

What is claimed is:

1 . A system for predicting a time when energy consumption on a grid will exceed normal production capacity, the system comprising:
a building lag optimizer, configured to receive identifiers for buildings corresponding to the grid, 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; 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.

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 a 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 a 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:
a building lag optimizer, configured to determine an energy lag for a building, said building lag optimizer comprising:
a thermal response processor, configured to generate a plurality of energy use data sets for said building, 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 the energy lag for said building; and
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.

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 a 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 a 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:
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 the regression engine, the least valued residual indicating a corresponding energy lag for the 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.

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

Assignees

• Enernoc Inc

Inventors

• Al-Mohssen Husain
• Bassa Angela S
• Paradis Richard R

Classifications

• H02J3/003 Primary

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• H02J3/17

  Demand-responsive operation of AC power transmission or distribution networks · CPC title

• H02J3/00 Primary

  Circuit arrangements for AC mains or AC distribution networks · CPC title

• G05B13/026

  using a predictor · CPC title

• H02J2003/003

  Electricity · mapped topic