Abstract: A method for dispatching buildings in a demand response program event including 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.

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 ener

Abstract: An apparatus including devices, a network operations center (NOC), and control nodes. Each of the devices consumes a portion of a resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC is disposed external to a facility, and determines an energy lag for the facility based upon fine-grained energy consumption baseline data. The NOC employs the energy lag to generate a plurality of run time schedules that coordinates run times for the each of the devices to control the peak demand of the resource. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices. The plurality of control nodes transmits sensor data and device status to the NOC via the demand coordination network for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices on and off.

Abstract: An apparatus for prioritizing a demand response program event based on occupancy for one or more buildings of one or more building types participating in a demand response program is provided. The apparatus has an occupancy estimator and a dispatch controller. The occupancy estimator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the one or more buildings within the portion to process the streams, and is configured to generate occupancy levels corresponding to the one or more buildings within the portion, where the occupancy components and the occupancy levels are generated by exclusively processing the streams, and is configured to assign the occupancy levels to remaining ones of the one or more buildings not in the first portion.

Abstract: An occupancy based focused marketing system for augmenting market control techniques corresponding to one or more buildings of one or more building types within a geographic subsector is provided. The occupancy based traffic control system has a subsector occupancy aggregator, an other facility occupancy estimator, and a subsector market control processor. The subsector occupancy aggregator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the portion of the one or more buildings to process the streams, and is configured to generate average occupancy components corresponding to each building type within the portion of the one or more buildings and a first aggregated occupancy component for the first portion of the one or more buildings, where the occupancy components are generated by exclusively processing the streams.

Abstract: An occupancy based traffic control system for augmenting traffic control mechanisms corresponding to one or more buildings of one or more building types within a geographic subsector is provided. The occupancy based traffic control system includes, a subsector occupancy aggregator, an other facility occupancy estimator, and a subsector traffic control processor. The subsector occupancy aggregator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the portion of the one or more buildings to process the streams, and is configured to generate average occupancy components corresponding to each building type within the portion of the one or more buildings and a first aggregated occupancy component for the first portion of the one or more buildings, where the occupancy components are generated by exclusively processing the streams.

Abstract: An occupancy based targeted marketing system for augmenting market control techniques corresponding to one or more buildings of one or more building types within a geographic subsector is provided. The occupancy based traffic control system includes a subsector occupancy aggregator, an other facility occupancy estimator, and a subsector market control processor. The subsector occupancy aggregator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the portion of the one or more buildings to process the streams, and is configured to generate average occupancy components corresponding to each building type within the portion of the one or more buildings and a first aggregated occupancy component for the first portion of the one or more buildings, where the occupancy components are generated by exclusively processing the streams.

Abstract: A method for determining occupancy of a facility is provided.

Abstract: An apparatus for managing energy consumption of one or more buildings of one or more building types based on occupancy is provided. The apparatus includes an occupancy estimator and a multiple facility controller. The occupancy estimator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the one or more buildings within the portion to process the streams, and is configured to generate occupancy levels corresponding to the one or more buildings within the portion, where the occupancy components and the occupancy levels are generated by exclusively processing the streams, and is configured to assign the occupancy levels to remaining ones of the one or more buildings not in the first portion according to building type.

Abstract: An apparatus for controlling consumption of a resource by a facility is provided. The apparatus includes a plurality of devices, a network operations center (NOC), and a plurality of control nodes. The plurality of devices is disposed within the facility, each consuming a portion of the resource when turned on, and the each capable of performing a corresponding function within an acceptable operational margin by cycling on and off.

Abstract: An apparatus for controlling consumption of an energy resource by a facility is provided. The apparatus includes a plurality of devices, a network operations center (NOC), and a plurality of control nodes. The plurality of devices is disposed within the facility, each consuming a portion of the energy resource when turned on, and the each capable of performing a corresponding function to maintain a desired level of performance by cycling on and off.

Abstract: An apparatus for determining occupancy of a facility is provided. The apparatus includes a facility model processor and a global model module. The facility model processor is configured to generate occupancy components for the facility by processing a first data set comprising energy consumption and outside temperature data for the facility, the energy consumption and outside temperature data taken at a prescribed time increment over a first plurality of days, and is configured to generate a normalized first data set by employing the occupancy components to remove effects of occupancy of the facility from the first data set. The occupancy components include: a lower bound of energy consumption as a function of outside temperature; a normalized occupancy profile component as a function of the prescribed time increment; a marginal energy consumption component as a function of outside temperature; and a daily occupancy level component for each of the first plurality of days.

Abstract: An apparatus for security control of a facility is provided, including devices, a network operations center (NOC), and control nodes. The devices are within the facility, each consuming a portion of a resource, and each performing a corresponding function when actuated. The NOC is external to the facility, and generates run time schedules, where the run time schedules coordinate actuation times for the each of the devices to maintain security for local security environments, and adjusts the run time schedules based upon occupancy levels generated by the NOC, where the occupancy levels are exclusively generated based on consumption of the resource and outside temperature. The control nodes are within the facility, each coupled to a corresponding one of the devices, where the control nodes are coupled together via a building security network that is operatively coupled to the NOC, control nodes execute selected run time schedules to actuate the devices.

Abstract: A method for dispatching buildings in a demand response program event including 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.

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 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.

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 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 ener

Abstract: A method for characterizing buildings, including 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 according to similar energy lags.

Abstract: An apparatus including devices, a network operations center (NOC), and control nodes. Each of the devices consumes a portion of a resource when turned on, and performs a corresponding function within an acceptable operational margin by cycling on and off. The NOC is disposed external to a facility, and determines an energy lag for the facility based upon fine-grained energy consumption baseline data. The NOC employs the energy lag to generate a plurality of run time schedules that coordinates run times for the each of the devices to control the peak demand of the resource. Each of the plurality of control nodes is coupled to a corresponding one of the plurality of devices. The plurality of control nodes transmits sensor data and device status to the NOC via the demand coordination network for generation of the plurality of run time schedules, and executes selected ones of the run time schedules to cycle the plurality of devices on and off.