Abstract: A method for evaluating a building signature operational model (BSOM) associated with a building signature intelligent electronic device (BSIED) includes processing building signature operational data (BSOD) on a cloud-connected central processing unit to identify the BSIED from which the BSOD was received, and to determine if a current BSOM associated with the BSIED, and used by the BSIED to generate the BSOD, needs to be updated. In response to determining that the current BSOM needs to be updated, a new BSOM is selected for the BSIED from a BSOM library based, at least in part, on one or more characteristics associated with the BSIED. The new BSOM is transmitted to the BSIED for installation on the BSIED.

Abstract: A method for evaluating a building signature operational model (BSOM) associated with a building signature intelligent electronic device (BSIED) includes processing building signature operational data (BSOD) on a cloud-connected central processing unit to identify the BSIED from which the BSOD was received, and to determine if a current BSOM associated with the BSIED, and used by the BSIED to generate the BSOD, needs to be updated. In response to determining that the current BSOM needs to be updated, a new BSOM is selected for the BSIED from a BSOM library based, at least in part, on one or more characteristics associated with the BSIED. The new BSOM is transmitted to the BSIED for installation on the BSIED.

Abstract: A method for evaluating a building signature operational model (BSOM) associated with a building signature intelligent electronic device (BSIED) includes processing building signature operational data (BSOD) on a cloud-connected central processing unit to identify the BSIED from which the BSOD was received, and to determine if a current BSOM associated with the BSIED, and used by the BSIED to generate the BSOD, needs to be updated. In response to determining that the current BSOM needs to be updated, a new BSOM is selected for the BSIED from a BSOM library based, at least in part, on one or more characteristics associated with the BSIED. The new BSOM is transmitted to the BSIED for installation on the BSIED.

Abstract: A method comprises detecting a power quality event, determining if one or more power outages occurs in a defined time period extending from a beginning of the power quality event to an end of the power quality event, and if one or more power outages occurs in the defined time period, then performing an analysis, where performing the analysis comprises determining if the one or more power outages is associated with the power quality event. The method may also comprise outputting the information regarding the analysis to a display device.

Abstract: A method for evaluating a building signature operational model (BSOM) associated with a building signature intelligent electronic device (BSIED) includes processing building signature operational data (BSOD) on a cloud-connected central processing unit to identify the BSIED from which the BSOD was received, and to determine if a current BSOM associated with the BSIED, and used by the BSIED to generate the BSOD, needs to be updated. In response to determining that the current BSOM needs to be updated, a new BSOM is selected for the BSIED from a BSOM library based, at least in part, on one or more characteristics associated with the BSIED. The new BSOM is transmitted to the BSIED for installation on the BSIED.

Abstract: A process for detecting statistically significant changes in energy consumption patterns by monitoring for changes in the parameters to a parametric energy model. Two parametric models of energy consumption are created: the first being a model providing an initial base line of energy consumption, the second being a test model to be compared to the initial base model. Statistically significant changes are detected by using a difference score that compares the parameters of two models along with the uncertainties of each parameter to determine whether the differences in the parameters of each model indicate a statistically significant deviation in the energy consumption pattern.

Abstract: A method of detecting leakage in a compressed air system having a compressor includes configuring an electrical monitor to measure one or more electrical parameters indicative of operation of the compressor. The method further includes identifying periods of time during which the compressor is not in use; analyzing running times and intervals of the compressor between compressor operations during one or more of identified periods of time to establish a baseline for air leakage. The method further includes continuing to analyze running times and intervals of the compressor between compressor operations when the compressor is not in use. The method further includes notifying one or more users when running times and intervals between compressor operations vary from the baseline.

Abstract: A method comprises detecting a power quality event, determining if one or more power outages occurs in a defined time period extending from a beginning of the power quality event to an end of the power quality event, and if one or more power outages occurs in the defined time period, then performing an analysis, where performing the analysis comprises determining if the one or more power outages is associated with the power quality event. The method may also comprise outputting the information regarding the analysis to a display device.

Abstract: A method of detecting leakage in a compressed air system having a compressor includes configuring an electrical monitor to measure one or more electrical parameters indicative of operation of the compressor. The method further includes identifying periods of time during which the compressor is not in use; analyzing running times and intervals of the compressor between compressor operations during one or more of identified periods of time to establish a baseline for air leakage. The method further includes continuing to analyze running times and intervals of the compressor between compressor operations when the compressor is not in use. The method further includes notifying one or more users when running times and intervals between compressor operations vary from the baseline.

Abstract: Systems and methods for monitoring energy management (EM) data from at least one energy data source; detecting a data anomaly event in the EM data; determining if a resolution for the data anomaly event requires human input; and submitting a request to at least one user for human input to resolve the data anomaly event.

Abstract: A process for detecting statistically significant changes in energy consumption patterns by monitoring for changes in the parameters to a parametric energy model. Two parametric models of energy consumption are created: the first being a model providing an initial base line of energy consumption, the second being a test model to be compared to the initial base model. Statistically significant changes are detected by using a difference score that compares the parameters of two models along with the uncertainties of each parameter to determine whether the differences in the parameters of each model indicate a statistically significant deviation in the energy consumption pattern.

Abstract: Systems, methods, and devices for monitoring and modeling energy consumption are presented herein. A computer-implemented method of monitoring and modeling an energy load in an electrical system is featured. This method includes: determining one or more monitoring parameters; determining an energy conservation measure (ECM) evaluation period; creating an evaluation model of energy load over the ECM evaluation period based on the monitoring parameter(s), the evaluation model including one or more driver variables and at least one additional driver variable that is representative of at least one energy conservation measure; determining a coefficient of the at least one additional driver variable within an equation representing the ECM evaluation model; and outputting to a user the coefficient of the at least one additional driver variable. The coefficient represents the average change in energy due to the energy conservation measure(s) associated with the additional driver variable(s).

Abstract: A system, method, and computer program product for predicting operation for physical systems with distinct operating modes uses observable qualities of the system to predict other qualities of the system. Independent variables including temperature or production volume are observed to determine the degree to which a dependent modeled variable, including energy load, is influenced. Partition variables representing operating conditions of the dependent variables are defined as discrete values. Reference datasets with coincident values of the dependent variable, independent variable, and partition variables are received, and models are created for each discrete value of the partition variables in the reference dataset. Each model is populated with the values of the dependent variable and the independent variable. The dependent variable is modeled as a function of the independent variable.

Abstract: A method of graphically representing values of at least one selected parameter at multiple monitoring points in a utility system, comprises receiving data measured at the multiple monitoring points; determining the values of the selected parameter at the multiple monitoring points, based on the received data, and using the values to generate a graphical representation of the values of the selected parameter at the multiple monitoring points, the graphical representation including shapes having (1) sizes representative of the magnitudes of the values and (2) locations representative of the hierarchy of the monitoring points. In one application, the selected parameter is at least one of electrical power and energy consumed in portions of an electrical power distribution system that correspond to the multiple monitoring points.

Abstract: A system, method, and computer program product for predicting operation for physical systems with distinct operating modes uses observable qualities of the system to predict other qualities of the system. Independent variables including temperature or production volume are observed to determine the degree to which a dependent modeled variable, including energy load, is influenced. Partition variables representing operating conditions of the dependent variables are defined as discrete values. Reference datasets with coincident values of the dependent variable, independent variable, and partition variables are received, and models are created for each discrete value of the partition variables in the reference dataset. Each model is populated with the values of the dependent variable and the independent variable. The dependent variable is modeled as a function of the independent variable.

Abstract: A method of graphically representing values of at least one selected parameter at multiple monitoring points in a utility system, comprises receiving data measured at the multiple monitoring points; determining the values of the selected parameter at the multiple monitoring points, based on the received data, and using the values to generate a graphical representation of the values of the selected parameter at the multiple monitoring points, the graphical representation including shapes having (1) sizes representative of the magnitudes of the values and (2) locations representative of the hierarchy of the monitoring points. In one application, the selected parameter is at least one of electrical power and energy consumed in portions of an electrical power distribution system that correspond to the multiple monitoring points.