Abstract: Systems and methods may use a low speed controller in addition to an economic optimizer to achieve long-term objectives without significantly disrupting or destabilizing an electrical system. Specific long-term objectives include maximizing a capacity factor incentive and regulating battery degradation, but the methods and systems herein can be extended to more long-term objectives. A low speed controller can adjust one or more parameters of a cost function based on the relation between the projected state of the electrical system and the one or more parameters to effectuate a change to the electrical system to attempt to comply with the long-term objective.

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: 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: The present disclosure is directed to systems and methods for economically optimal control of an electrical system. A two-stage controller includes an optimizer and a high speed controller to effectuate a change to one or more components of the electrical system. The high speed controller receives a set of control parameters for an upcoming extended time period. The control parameters include a plurality of bounds for an adjusted net power of the electrical system. The high speed controller sets an energy storage system command control variable (ESS command) based on a state of adjusted net power of the electrical system and the set of control parameters.

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: 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: The present disclosure is directed to systems and methods for controlling an electrical system using setpoints. Some embodiments include control methods that monitor an adjusted net power associated with the electrical system and adjust the setpoint based on a comparison of the adjusted demand to the setpoint. If the adjusted demand has not exceeded the demand setpoint, the setpoint is reduced. If the adjusted demand has exceeded the demand setpoint, the setpoint is increased.

Abstract: A method for performing validation, estimation, and editing (VEE), including: displaying real-time electrical usage for a building on a controllable video display; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising groups of contiguous interval values tagged as having been validated and corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing the controllab

Abstract: An apparatus for configuring validation, estimation, and editing (VEE) rules for performing VEE on interval-based energy consumption streams includes a data stores, a rules processor, and a process control element. The stores provides tagged data sets each associated with a corresponding one of the streams, each of the sets comprising groups of contiguous interval values tagged as having been validated. The rules processor reads the stores upon initiation of an event and, for the each of the sets, creates anomalies having different durations using only the groups of values, the groups of values corresponding to correct data, and generates estimates for the anomalies by employing estimation techniques for each of the durations and, for the each of the durations, selects a corresponding one of the techniques for subsequent employment when performing VEE of subsequent energy consumption data associated with the each of the durations for the corresponding one of the streams.

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: 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: A building control system that includes a post VEE readings data stores, a rules processor, weather stores, and a building controller. The stores provide tagged data sets that are each associated with a corresponding one of energy consumption streams, each of the sets having groups of contiguous interval values tagged as having been validated. The rules processor reads the stores and creates anomalies having different durations using only the groups of contiguous interval values and generates estimates for the anomalies by employing estimation techniques for each of the different durations and selects one of the estimation techniques for subsequent employment.

Abstract: An apparatus for configuring validation, estimation, and editing (VEE) rules for performing VEE on interval-based energy consumption streams includes a data stores, a rules processor, and a process control element. The stores provides tagged data sets each associated with a corresponding one of the streams, each of the sets comprising groups of contiguous interval values tagged as having been validated. The rules processor reads the stores upon initiation of an event and, for the each of the sets, creates anomalies having different durations using only the groups of values, the groups of values corresponding to correct data, and generates estimates for the anomalies by employing estimation techniques for each of the durations and, for the each of the durations, selects a corresponding one of the techniques for subsequent employment when performing VEE of subsequent energy consumption data associated with the each of the durations for the corresponding one of the streams.

Abstract: The present disclosure is directed to systems and methods for economically optimal control of an electrical system. A two-stage controller includes an optimizer and a high speed controller to effectuate a change to one or more components of the electrical system. The high speed controller receives a set of control parameters for an upcoming extended time period. The control parameters include a plurality of bounds for an adjusted net power of the electrical system. The high speed controller sets an energy storage system command control variable (ESS command) based on a state of adjusted net power of the electrical system and the set of control parameters.

Abstract: A method for performing validation, estimation, and editing (VEE), including: employing HVAC subsystems to adjust comfort in a building; performing VEE on energy consumption streams; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the data sets comprising groups of contiguous interval values tagged corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy consumption data for the corresponding one of the streams; and executing functions on the streams translated by the generating and directing one or more of the

Abstract: A building control system that includes a post VEE readings data stores, a rules processor, and a building controller. The stores provide tagged data sets that are each associated with a corresponding one of energy consumption streams, each of the sets having groups of contiguous interval values tagged as having been validated. The rules processor reads the stores and creates anomalies having different durations using only the groups of contiguous interval values and generates estimates for the anomalies by employing estimation techniques for each of the different durations and selects one of the estimation techniques for subsequent employment. The building controller receives post VEE readings and outside temperatures corresponding to the interval-based streams and determines and controls cumulative energy consumption corresponding to the interval-based streams and manages the cumulative energy consumption by scheduling run times for building elements that are coupled to the building controller.

Abstract: A method for performing validation, estimation, and editing (VEE), including: employing HVAC subsystems to adjust comfort in a building; performing VEE on energy consumption streams; executing VEE rules on each of the streams to generate and store a corresponding post VEE readings, the post VEE readings comprising tagged energy consumption data sets each associated with a corresponding one of the streams, each of the 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, the first groups of contiguous interval values corresponding to correct data; for the each of the data sets, creating anomalies having different durations using only the first groups of contiguous interval values; generating estimates for the anomalies by employing estimation techniques; for each of the durations, selecting one of the estimation techniques for subsequent employment when performing VEE of subsequent energy con