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 non-linear parabolic 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 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: The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

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: A system for charging an electric or hybrid-electric vehicle, the system comprising: a low power grid interface comprising an AC pole and a DC pole, the AC pole being electrically connected to an electrical power grid; a battery buffer electrically connected to the DC pole of the low power grid interface; and a DC-DC converter having an input DC pole and an output DC pole, the input DC pole being connected to the battery buffer and the output DC pole being connected to an electric vehicle, wherein the connection between the battery buffer and the DC-DC converter is a DC electrical connection.

Abstract: An apparatus is provided for configuring validation, estimation, and editing (VEE) rules for performing VEE on a plurality of interval based energy consumption streams. The apparatus includes a post VEE readings data stores and a rules processor. The post VEE readings data stores provides a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the plurality of interval based energy consumption streams. Each of the plurality of tagged energy consumption data sets has first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited.

Abstract: A building control system is provided that performs validation, estimation, and editing (VEE) on a plurality of interval based energy consumption streams. The system includes a post VEE readings data stores, a rules processor, and a building controller. The post VEE readings data stores is configured to provide a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the plurality of interval based energy consumption streams, each of the plurality of tagged energy consumption data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited.

Abstract: The present disclosure is directed to machine learning of electrical power system behavior, and related systems, apparatuses, and methods. A controller of an electrical power system includes a data storage device configured to store model data indicating a model load power consumed by loads of the electrical power system. The controller also includes a processor configured to determine current data including current load power consumed by the loads, modify the model data by aggregating the model data with the current data, and determine a set of control values for a set of control variables to effectuate a change to operation of the electrical power system based, at least in part, on the model data.

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: The present disclosure is directed to systems and methods for economically optimal control of an electrical system. Some embodiments employ generalized multivariable constrained continuous optimization techniques to determine an optimal control sequence over a future time domain in the presence of any number of costs, savings opportunities (value streams), and constraints. Some embodiments also include control methods that enable infrequent recalculation of the optimal setpoints. Some embodiments may include a battery degradation model that, working in conjunction with the economic optimizer, enables the most economical use of any type of battery. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

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 apparatus is provided for configuring validation, estimation, and editing (VEE) rules for performing VEE on a plurality of interval based energy consumption streams. The apparatus includes a network operations center, configured to receive the plurality of interval based energy consumption streams and is configured to perform VEE on the plurality of interval based energy consumption streams within a specified time period. The network operations center has a post VEE readings data stores and a rules processor. The post VEE readings data stores is configured to provide a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the plurality of interval based energy consumption streams, each of the plurality of tagged energy consumption data sets including first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited.

Abstract: An apparatus is provided for performing validation, estimation, and editing (VEE) on a plurality of interval based energy consumption streams. The apparatus has a network operations center, configured to receive the plurality of interval based energy consumption streams, and configured to perform VEE on the plurality of interval based energy consumption streams within a specified time period. The network operations center includes a VEE processor and a VEE configuration engine.

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 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 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 and automotive vehicles.

Abstract: An apparatus for controlling consumption of a resource includes devices, a network operations center (NOC), and control nodes. The devices are within the facility. The NOC is external to the facility, and generates run time schedules that coordinate run times for each of the devices to control consumption while maintaining local environments, and adjusts the schedules based upon occupancy levels generated by the NOC, where the levels are exclusively generated based on the consumption of the resource and outside temperature. The control are coupled together via an energy management network that is operatively coupled to the NOC, and transmit sensor data and device status to the NOC for generation of the plurality of run time schedules. The nodes execute selected ones of the run time schedules to cycle the devices on and off.

Abstract: A brown out prediction system is provided that performs validation, estimation, and editing (VEE) on a plurality of interval based energy consumption streams. The system includes a post VEE readings data stores, a rules processor, a peak prediction element, and a peak controller. The post VEE readings data stores is configured to provide a plurality of tagged energy consumption data sets. The rules processor is configured to read the post VEE readings data stores and is configured to create a plurality of anomalies having a plurality of differnt durations. The peak prediction element is coupled to the post VEE readings data stores and to weather stores, and is configured to receive post VEE readings. The peak controller is coupled to the peak prediction element, and is configured to receive the brown out time, and is configured to trigger exceptional measures to manage the cumulative energy consumption in order to preclude a brown out.

Abstract: An apparatus for controlling consumption of an energy resource includes devices, a network operations center (NOC), and control nodes. The devices are within the facility. The NOC is external to the facility, and generates run time schedules that coordinate run times for the each of the devices to control consumption while maintaining local environments and desired level of performance, and adjusts the run time schedules based upon resource utilization levels generated by the NOC, where the resource utilization levels are exclusively generated based on the consumption of the energy resource and outside temperature. The control nodes are coupled together via an energy management network that is operatively coupled to the NOC, and transmit sensor data and device status to the NOC for generation of the 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 demand response dispatch prediction system is provided that performs validation, estimation, and editing (VEE) on a plurality of interval based energy consumption streams. The system includes a post VEE readings data stores, a rules processor, and a dispatch prediction element. The post VEE readings data stores is configured to provide a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the plurality of interval based energy consumption streams, each of the plurality of tagged energy consumption data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited.

Abstract: An energy baselining system is provided that performs validation, estimation, and editing (VEE). The system includes a facility model processor, a post VEE readings data stores, a VEE configuration engine, and a global model module. The facility model processor is configured to employ one or more interval based energy consumption streams corresponding to a facility to develop and maintain weather-normalized baseline energy consumption data for the facility, where the weather-normalized baseline energy consumption data is derived from training data for the facility. The post VEE readings data stores is configured to provide a plurality of tagged energy consumption data sets that are each associated with a corresponding one of the one or more interval based energy consumption streams, each of the plurality of tagged energy consumption data sets comprising first groups of contiguous interval values tagged as having been validated and second groups of contiguous interval values tagged as having been edited.