Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 non-linear parabolic analysis on the each of the plurality of energy use data sets to yield corresponding non-linear parabolic model parameters and a corresponding residual; determining a least valued residual from all residuals, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to simila

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 non-linear parabolic analysis on the each of the plurality of energy use data sets to yield corresponding non-linear parabolic 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 categorizing the buildin

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 machine learning model analysis on the each of the plurality of energy use data sets to yield corresponding machine learning model parameters and a corresponding residual; determining a least valued residual from all residuals, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to similar

Abstract: A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, where the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a machine learning model analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

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 machine learning model analysis on each set to yield corresponding machine learning model model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, machine learning model 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; a

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 machine learning model analysis on each set to yield corresponding machine learning model model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, machine learning model 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; a

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 non-linear parabolic analysis on each set to yield corresponding non-linear parabolic model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, non-linear parabolic 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 rec

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 non-linear parabolic analysis on each set to yield corresponding non-linear parabolic model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, non-linear parabolic 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 rec

Abstract: A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, where the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a machine learning model analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

Abstract: A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, where the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a non-linear parabolic analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 machine learning model analysis on the each of the plurality of energy use data sets to yield corresponding machine learning model parameters and a corresponding residual; determining a least valued residual from all residuals, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to similar

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 non-linear parabolic analysis on the each of the plurality of energy use data sets to yield corresponding non-linear parabolic model parameters and a corresponding residual; determining a least valued residual from all residuals, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to simila

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 non-linear parabolic analysis on the each of the plurality of energy use data sets to yield corresponding non-linear parabolic 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 categorizing the buildin

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 e

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 machine learning model analysis on the each of the plurality of energy use data sets to yield corresponding machine learning model parameters and a corresponding residual; determining a least valued residual from all residuals, the least valued residual indicating a corresponding energy lag for the each of the buildings; and categorizing the buildings into types according to similar

Abstract: A method for dispatching buildings participating in a demand response program 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 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 lag value; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued 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 a dispatch order reception time for a demand response program event.

Abstract: A method for characterizing buildings, including retrieving a plurality of baseline energy use data sets for the buildings from a baseline data stores; generating energy use data sets for each of the buildings, each of the energy use data sets comprising energy consumption values along with corresponding time and outside temperature values, where the energy consumption values within each of the sets 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 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 categorizing the buildings into types accord

Abstract: A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, wherei the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a machine learning model analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

Abstract: A method for dispatching buildings, including: generating data sets, each having energy values along with corresponding time and outside temperature values, wherein the energy values are shifted by one of a plurality of lag values relative to the corresponding time and outside temperature values; performing a non-linear parabolic analysis on the each of the data sets; determining a least valued residual that indicates a corresponding energy lag for each of the buildings, the corresponding energy lag describes a transient energy consumption period preceding a change in outside temperature; using outside temperatures, model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict a dispatch order reception time for the demand response program event; and employing the dispatch order reception time to prepare actions required to control the each of the buildings to optimally shed energy specified in a dispatch order.

Abstract: A method for dispatching buildings participating in a demand response program including generating data sets for each of the buildings, each 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 lag value; performing a regression analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued 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 a dispatch order reception time for a demand response program event.