Abstract: Embodiments predict an occurrence of one or more hot sockets among a meter installation of a plurality of smart meters. Embodiments receive historical data over a predefined time period from the plurality of smart meters, the historical data including, for each smart meter, an amount of daily consumption of electricity for the meter, historical meter events for the meter, and all service orders associated with the meter. Embodiments pre-process the historical data to generate hot socket features. Embodiments train a machine algorithm using the hot socket features, and use the trained machine algorithm to predict the occurrence of one or more hot sockets.

Abstract: Techniques for anomaly detection are disclosed. An anomaly detection system transforms observation signals received from signal generators into multidimensional representations. The system extracts portions of the multidimensional representation indicative of anomalies. Based on the extracted portions of the multidimensional representation, the system determines metrics representing the observation signals and determines whether the metrics satisfy anomaly detection criteria.

Abstract: Systems, methods, and other embodiments are associated with detecting an electric vehicle charging event. The system receives unknown time series data of usage values of electricity consumption, wherein the unknown time series data is unknown to have electric vehicle (EV) charge events. For a given account, the unknown time series data is converted into time intervals with corresponding usage values. Each time interval is encoded with a symbol from a series of symbols representing a level of electricity consumption, wherein the encoding generates an encoded consumption pattern of symbols. The system detects whether the encoded consumption pattern includes a string of high usage symbols that are similar to a known EV charge motif that represents a known EV charging event. Based on the detecting, the given account is marked as having an electric vehicle charge event or as not having an electric vehicle charge event.

Abstract: Systems, methods, and other embodiments are associated with detecting an electric vehicle charging event. The system receives unknown time series data of usage values of electricity consumption, wherein the unknown time series data is unknown to have electric vehicle (EV) charge events. For a given account, the unknown time series data is converted into time intervals with corresponding usage values. Each time interval is encoded with a symbol from a series of symbols representing a level of electricity consumption, wherein the encoding generates an encoded consumption pattern of symbols. The system detects whether the encoded consumption pattern includes a string of high usage symbols that are similar to a known EV charge motif that represents a known EV charging event. Based on the detecting, the given account is marked as having an electric vehicle charge event or as not having an electric vehicle charge event.

Abstract: Systems, methods, and other embodiments associated with detecting an electric vehicle charging event. In one embodiment, from electricity consumption data from a known set of electric vehicle owners, the method encodes usage values from time intervals with a symbol from a series of symbols representing a level of electricity consumption during the time interval. The encoding generates an encoded consumption pattern of symbols for each electrical vehicle owner. An EV charge motif is identified that represents an EV charging event. One or more machine learning classifiers is trained to identify the EV charge motifs from the known set of electric vehicle owners and to distinguish from non-charge motifs to identify EV charges from unknown data sets.

Abstract: Embodiments predict an occurrence of one or more hot sockets among a meter installation of a plurality of smart meters. Embodiments receive historical data over a predefined time period from the plurality of smart meters, the historical data including, for each smart meter, an amount of daily consumption of electricity for the meter, historical meter events for the meter, and all service orders associated with the meter. Embodiments pre-process the historical data to generate hot socket features. Embodiments train a machine algorithm using the hot socket features, and use the trained machine algorithm to predict the occurrence of one or more hot sockets.

Abstract: Systems, methods, and other embodiments associated with detecting an electric vehicle charging event. In one embodiment, from electricity consumption data from a known set of electric vehicle owners, the method encodes usage values from time intervals with a symbol from a series of symbols representing a level of electricity consumption during the time interval. The encoding generates an encoded consumption pattern of symbols for each electrical vehicle owner. An EV charge motif is identified that represents an EV charging event. One or more machine learning classifiers is trained to identify the EV charge motifs from the known set of electric vehicle owners and to distinguish from non-charge motifs to identify EV charges from unknown data sets.