Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method 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, the method comprising: receiving energy consumption and outside temperature streams Ei(h,T) corresponding to a portion of the one or more buildings, and receiving and employing occupancy components for each of the one or more buildings within the portion to process the streams, and generating occupancy levels corresponding to the one or more buildings within the portion, and assigning the occupancy levels to remaining ones of the one or more buildings not in the portion; and optimizing execution of the demand response program event by employing the occupancy components to prioritize dispatch messages to the one or more buildings to achieve objectives of the demand response program event.

Abstract: A method 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, the method comprising: receiving energy consumption and outside temperature streams Ei(h,T) corresponding to a portion of the one or more buildings, the energy consumption provided by streaming consumption sources, and receiving and employing occupancy components for each of the one or more buildings within the portion to process the streams, and generating occupancy levels corresponding to the one or more buildings within the portion, and assigning the occupancy levels to remaining ones of the one or more buildings not in the portion; and optimizing execution of the demand response program event by employing the occupancy components to prioritize dispatch messages to the one or more buildings to achieve objectives of the demand response program event.

Abstract: A method for predicting when a grid will exceed production capacity including generating time shifted versions of consumption data for each of the buildings, each version comprising consumption values along with time and temperature values, where the consumption values are shifted by lag values relative to the time and temperature values, and where each lag value is different; performing a regression analysis on each version 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; using outside temperatures, regression model parameters, and lags for all of the buildings to estimate a cumulative consumption for the buildings, and to predict the time when the grid will exceed production capacity; and receiving the time when the grid will exceed production capacity, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when consumption on a grid will exceed normal capacity for buildings including generating time shifted versions of consumption data for the buildings, each version comprising consumption values along with time and outside temperature values, where the energy consumption values within each version are shifted by one of a plurality of lag values relative to the time and outside temperature values; performing regression analysis on each version to yield regression model parameters and a residual; determining a least valued residual indicating an energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags to estimate a cumulative consumption for the buildings, and to predict when consumption on the grid will exceed normal capacity; and receiving the time when consumption on the grid will exceed normal capacity, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when consumption on a grid will exceed production capacity for buildings within the grid including time shifted version for each of the buildings, each version comprising consumption values along with time and temperature values, where the consumption values within each set are shifted by one of a plurality of lag values relative to the time and temperature values; 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; using temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative consumption, and to predict the time when consumption on the grid will exceed production capacity; and receiving the time, and preparing and commencing exceptional measures required to manage the consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: An occupancy-based traffic control system for augmenting traffic control mechanisms corresponding to buildings of one or more building types within a geographic subsector is provided. The system includes, a subsector aggregator, an other facility occupancy estimator, and a subsector processor. The subsector aggregator receives energy consumption and outside temperature streams corresponding to a portion of the buildings, and employs occupancy components to generate average occupancy components corresponding to each building type. The other facility occupancy estimator matches the average occupancy components to remaining ones of the buildings not in the first portion according to building type. The subsector processor uses the first and second aggregated occupancy components modify defaults of traffic control devices based on the occupancy estimate to optimize flow of pedestrians.

Abstract: An apparatus for controlling consumption of a resource by a facility includes devices, an occupancy determination system, control nodes, and monitor nodes. The devices each consume a portion of the resource and perform a function. The occupancy determination system is disposed in a network operations center that is external to the facility and generates schedules which coordinate run times for the devices to control the consumption of the resource and adjusts the schedules based upon occupancy components and occupancy levels that are generated based on the consumption of the resource and outside temperature only. The control nodes are coupled to a corresponding one of the devices, and are coupled together via an energy management network that is operatively coupled to the NOC, The control nodes transmit sensor data and device status to the NOC for generation of the plurality of run time schedules, and where the control nodes execute selected ones of the run time schedules to cycle the devices on and off.

Abstract: An apparatus for controlling consumption of a resource by a facility includes devices, an occupancy determination system, and control nodes. The devices each consume a portion of the resource and perform a function. The occupancy determination system is disposed in a network operations center that is external to the facility and generates schedules which coordinate run times for the devices to control the consumption of the resource and adjusts the schedules based upon occupancy components and occupancy levels that are generated based on the consumption of the resource and outside temperature only. The control nodes are coupled to a corresponding one of the devices, and are coupled together via an energy management network that is operatively coupled to the NOC, The control nodes transmit sensor data and device status to the NOC for generation of the plurality of run time schedules, and where the control nodes execute selected ones of the run time schedules to cycle the devices on and off.

Abstract: An apparatus for controlling facility resource consumption includes devices, a network operations center (NOC), and control nodes. The devices each consume a portion of the resource and perform a function. The NOC generates schedules which coordinate run times for the devices to control the consumption of the resource and adjusts the schedules based upon occupancy components and occupancy levels that are generated based on the consumption of the resource and outside temperature only. The control nodes are coupled to a corresponding one of the devices, and are coupled together via an energy management network that is operatively coupled to the NOC, The control nodes transmit sensor data and device status to the NOC for generation of the plurality of run time schedules, and where the control nodes execute selected ones of the run time schedules to cycle the devices on and off.

Abstract: An occupancy-based traffic control system for augmenting traffic control mechanisms corresponding to buildings of one or more building types within a geographic subsector is provided. The system includes, a subsector aggregator, an other facility occupancy estimator, and a subsector processor. The subsector aggregator receives energy consumption and outside temperature streams corresponding to a portion of the buildings, and employs occupancy components to generate average occupancy components corresponding to each building type. The other facility occupancy estimator matches the average occupancy components to remaining ones of the buildings not in the first portion according to building type. The subsector processor uses the first and second aggregated occupancy components modify defaults of traffic control devices based on the occupancy estimate to optimize flow of automotive vehicles.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model 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; 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 will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

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 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.