Apparatus and method for employing weather induced facility energy consumption characterizations in a demand response dispatch system

Patent metadata

Field	Value
Publication number	US-2016294186-A1
Application number	US-201514984706-A
Country	US
Kind code	A1
Filing date	Dec 30, 2015
Priority date	Mar 31, 2015
Publication date	Oct 6, 2016
Grant date	—

Abstract

A method for dispatching buildings in a demand response program event 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 having 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 of the plurality of lag values is different from other ones of the plurality of lag values; performing a regression analysis on each set to yield regression model parameters and a residual; determining a least valued residual from all residuals yielded, the least valued residual indicating a corresponding energy lag for the each of the buildings; and using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags.

First claim

What is claimed is:

1 . A demand response dispatch system, comprising:
baseline data stores, configured to store a plurality of baseline energy use data sets for buildings participating in a demand response program; 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 a dispatch processor, coupled to said building lag optimizer, configured to receive a plurality of energy lags, each corresponding to one of said buildings, and configured to generate a dispatch schedule for a demand response program event according to a prioritization of said plurality of energy lags.

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 each of said plurality of energy lags.

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 corresponding time values comprise 15-minute values and said plurality of lag values spans a 24-hour period.

6 . The system as recited in claim 1 , wherein said dispatch schedule directs for dispatch of ones of said buildings having greater energy lags prior to others of said buildings having lesser energy lags.

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 dispatching buildings participating in a demand response program event, the system comprising:
baseline data stores, configured to store a plurality of baseline energy use data sets for the buildings; 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 the energy lag for said one of the buildings; and
a dispatch processor, coupled to said building lag optimizer, configured to receive a plurality of energy lags, each corresponding to one of the buildings, and configured to generate a dispatch schedule for the demand response program event according to a prioritization of said plurality of energy lags.

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 each of said plurality of energy lags.

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 said corresponding time values comprise 15-minute values and said plurality of lag values spans a 24-hour period.

13 . The system as recited in claim 8 , wherein said dispatch schedule directs for dispatch of ones of the buildings having greater energy lags prior to others of the buildings having lesser energy lags.

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 dispatching buildings participating in a demand response program event, 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 the regression engine, the least valued residual indicating a corresponding energy lag for the each of the buildings; and using energy lags for all of the buildings to generate a dispatch schedule for the demand response program event according to a prioritization of the energy lags.

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
• Paradis Richard R
• Bassa Angela S

Classifications

• H02J3/003

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

• G05B2219/2642

  Domotique, domestic, home control, automation, smart house · CPC title

• G06Q10/04

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• H02J3/00 Primary

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

• G05F1/66 Primary

  Regulating electric power · CPC title