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.

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 system for enabling spot cleaning includes a mobile computing device and a mobile cleaning robot. The mobile computing device includes at least one camera configured to capture images of an environment, and at least one data processor configured to (a) establish, based at least in part on first information provided by the at least one image sensor, a coordinate system in the environment, (b) determine, based at least in part on second information provided by the at least one camera, a first set of coordinates of a region at a first location, (c) determine, based at least in part on third information provided by the at least one camera, a second set of coordinates of a mobile cleaning robot at a second location, (d) send the first set of coordinates and second set of coordinates, or coordinates of the first location relative to the second location, to the mobile cleaning robot, and (e) send an instruction to the mobile cleaning robot to request the mobile cleaning robot to travel to the first location.

Abstract: An apparatus for controlling consumption of a resource by a facility 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: 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 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 machine learning model analysis on each set to yield machine learning 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 system for enabling spot cleaning includes a mobile computing device and a mobile cleaning robot. The mobile computing device includes at least one camera configured to capture images of an environment, and at least one data processor configured to (a) establish, based at least in part on first information provided by the at least one image sensor, a coordinate system in the environment, (b) determine, based at least in part on second information provided by the at least one camera, a first set of coordinates of a region at a first location, (c) determine, based at least in part on third information provided by the at least one camera, a second set of coordinates of a mobile cleaning robot at a second location, (d) send the first set of coordinates and second set of coordinates, or coordinates of the first location relative to the second location, to the mobile cleaning robot, and (e) send an instruction to the mobile cleaning robot to request the mobile cleaning robot to travel to the first location.

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

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

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 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 one or more 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 one or more buildings not in the portion according to building type. The subsector processor uses the first and second aggregated occupancy components to modify defaults of market control devices based on the occupancy estimate to optimize targeted advertising to a flow of pedestrians and automotive vehicles in the geographic subsector.

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 system has 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 one or more 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 one or more buildings not in the portion according to building type.

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 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 non-linear parabolic analysis on each set to yield machine learning 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 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 machine learning model analysis on each set to yield machine learning 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.