A communication apparatus comprises a first communication unit configured to receive, from an external server, an output suppression message instructing output suppression of a dispersed power source; and a second communication unit configured to perform communication of a predetermined message having a predetermined format with a power management apparatus that manages power of an equipment installed in a consumer's facility. The output suppression of the dispersed power source is performed in accordance with the output suppression message by a conversion apparatus that converts DC power output from the dispersed power source to AC power. The predetermined format includes an information element capable of storing at least one of first information and second information, the first information being related to a communication status with the conversion apparatus, the second information being related to an acquisition status of the output suppression message. The second communication unit is configured to transmit, to the power management apparatus, the predetermined message including at least one of the first information and the second information as an information element.

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.

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.

An intelligent user-side power management device (PMD) that has an optional energy storage unit and can interface with a utility grid or microgrid to eliminate power theft and efficiently provide clean energy to the users of the grid while helping the grid to do smart demand response management, particularly for renewable energy based grids that need to efficiently manage the slack due to the large variability in power generation through these energy sources.

A microgrid controller includes a database in communication with a processor. The processor is operable to receive at least one microgrid input, to determine a first plurality of optimal power characteristic levels at a corresponding one of a plurality of first time intervals for a first time period, and to determine a second plurality of optimal power characteristic levels of a device determined at a corresponding one of a plurality of second time intervals for a second time period. The first time intervals are found at a first frequency different than a second frequency of the second time intervals. One of the second plurality of optimal power characteristic level corresponds to one of the first plurality of optimal power characteristic levels at each first time interval. The processor is configured to control a device optimal power characteristic level in response to the second plurality of optimal power characteristic levels.

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.

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, 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.

A distributed control system receives analysis data. The analysis data includes quality attributes of fluid samples and an indication of a plant location corresponding to the fluid samples. The system identifies one or more anomalies of fluid based upon the quality attributes of the plurality of fluid samples and attributes the one or more anomalies to one or more particular areas. The system triggers an alert, trigger control, or both, based at least in part upon the identified one or more anomalies and the attributed one or more particular areas.

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 energy lags.

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 buildings into types according to similar energy lags.