Abstract: In one example implementation, a log analysis system can comprise an activity engine to monitor user activity of a computer system, a baseline engine to generate an expected baseline of a log, and an abnormality engine to compare the log to the expected baseline to identify an abnormality, compare the abnormality to a user activity volume based on a correlation between the user activity volume and the log activity, and classify the log.

Abstract: The present invention provides a method for determining probable presence, in a surveyed household, of appliances having no load sensors, said method implemented by one or more processing devices to perform: Acquiring at an edge device located at the house hold, the load data of the household; Realtime compression of load measurements; Calculating indicators which represent the measured load data, wherein the indicators provide partial representation of the measured data, wherein the partial representation include data pattern or specific type of measurement or schedule of measurement which is associated with the presence of specific type of appliances; Transmitting the calculated indicator and compressed data from edge device to cloud server; Applying learning algorithm at the cloud server, only on the calculated indicators for identifying presence of appliance.

Abstract: A system for automatic detection of inefficient household thermal insulation includes a server module and a plurality of household client modules. The system performs the following steps: acquiring data relating to each monitored household; identifying periods of HVAC down-time and determining indoor temperature gained during these periods; extracting parameters of temperature gain, relating to the measured rate of temperature gain during the down time; training a machine learning algorithm, to create at least one classification model, wherein all monitored households are classified according to the parameters of temperature gain; producing expected values for parameters of temperature gain per each household, according to household's class membership; producing the ratio between the expected and measured values for parameters of temperature gain per each monitored household; comparing the ratio among similar households; and identifying inefficiently insulated household according to the comparison.

Abstract: A method for identifying an electric water heater having excessive and abnormal electricity consumption for detecting inefficiency or even a malfunction comprising the following steps: The present invention provides a method for automatic detection of inefficient household heater within a group of monitored households, implemented by a server module and a plurality of household client modules, wherein each of said a server module and plurality of household client modules comprising one or more processors, operatively coupled to non-transitory computer readable storage devices, on which are stored modules of instruction code, wherein execution of said instruction code by said one or more processors implements the following actions: acquiring data relating to each monitored household, including at least part of: environmental conditions, power consumption of each water heater, household profile parameters, and household residents' profile parameters; detect events wherein the water heater's power consumption

Abstract: The present invention provides a method for automatic detection malfunction or inefficiency of electronic heating device, the method comprising: acquiring data related to each monitored household for generating a training set, including power consumption of each electronic heating device, power consumption of each household, household profile parameters, and household residents' profile parameters; training an electric Heating classification model for identifying existence of electronic heating based on load data; Determining the of existence of electronic heating and type of the device based on the Heating classification model; Training an insights model based on daily load pattern to identify activation pattern of the electronic heating devices using periodic household power consumption readings with no temperature; Prediction Detection and Identification of HVAC activation pattern using Periodic household power consumption readings with no temperature; Clustering aggregating in winter time activation pa

Abstract: Systems and methods are provided for predicting inefficient HVAC operation, by obtaining first training data for HVACs in a training set of households during a first period of moderate weather; obtaining second training data for HVACs in the training set of households during a subsequent period of harsher weather; generating classification labels of the household locations of the training set according to the second training data; applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of harsher weather conditions; obtaining operational data pertaining to HVACs in an operational set of households during a second period of moderate weather; and applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set during a second subsequent period of harsher weather.

Abstract: The present invention provides a method for determining conditions of malfunction or inefficiency of HVAC systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC. The said method comprise the steps of: a. monitoring the power consumption of a plurality of households; b. monitoring the concurrent environmental conditions at the location of the said plurality of households; c. analyzing each household's power consumption, and extracting weighted failure indications of inefficient or malfunctioning HVAC systems; d. determining the probability of various HVAC conditions of malfunction of inefficiency, according to the said weighted indicators; and e. emitting a an alert in relation to the said condition of HVAC malfunction of inefficiency, comprising at least one of: HVAC malfunction type, probability, probable cause, and suggested action.

Abstract: A method for monitoring heating, ventilation, and air conditioning (HVAC) systems includes: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC consumption or generating additional derived parameters, in an aggregation process; Calculating the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of summer weather conditions; Obtaining operational data pertaining to HVACs in an operational set of households during a second period of spring weather; and Applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set, during periods of summer weather using only overall

Abstract: The present invention provides a method for monitoring a plurality of heating, ventilation, and air conditioning (HVAC) systems and predicting inefficient HVAC operation, implemented by one or more processors operatively coupled to a non-transitory computer readable storage device, on which are stored modules of instruction code that when executed cause the one or more processors to perform the following steps: Obtaining first training data for HVACs in a training set of households during a first period of spring weather; Obtaining second training data for HVACs in the training set of households during a period of summer weather, Preprocessing the training data to identify repeating patterns of HVAC coemption or generating additional derived parameters, in an aggregation process Calculating a “Household Efficiency Score”: the amount of energy required to change house temperature; Applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an

Abstract: The present invention provides a method for forecasting load and managing a control plan for households having electric appliances wherein the control plan determines the activation of the electric appliances at pre-defined control periods.

Abstract: The invention discloses a system and method for automatic detection of inefficient household thermal insulation, comprising a server module and a plurality of household client modules.

Abstract: Systems and methods are provided for predicting inefficient HVAC operation, by obtaining first training data for HVACs in a training set of households during a first period of moderate weather; obtaining second training data for HVACs in the training set of households during a subsequent period of harsher weather; generating classification labels of the household locations of the training set according to the second training data; applying the first training data and the classification labels to train a supervised machine learning algorithm, to generate an HVAC classification model predictive of inefficiency during periods of harsher weather conditions; obtaining operational data pertaining to HVACs in an operational set of households during a second period of moderate weather; and applying the HVAC classification model to predict inefficiency of HVACs at individual households in the operational set during a second subsequent period of harsher weather.

Abstract: A non-transitory, computer readable storage device includes software that, while being executed by a processor, causes the processor to choose, based on user activity, a plurality of candidate parameters to be monitored from a plurality of event messages. Further, the processor executes the software to estimate a level of similarity between the chosen plurality of candidate parameters by computing a similarity score for at least two of the chosen candidate parameters. Still further, the processor executes the software to determine a plurality of parameters from the chosen candidate parameters if the similarity score for the plurality of parameters is greater than a threshold.

Abstract: Method and systems for analyzing event log elements are provided. In one example, a method includes receiving an event log element in a computer. A similarity index is calculated between the event log element and a text element. A threshold of similarity is calculated. The similarity index is compared to the threshold. If the similarity index is greater than the threshold, the event log element is grouped into a cluster with the text element to create a file of cluster assignments.

Abstract: The present invention provides a method for determining conditions of malfunction or inefficiency of HVAC systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC. The said method comprise the steps of: a. monitoring the power consumption of a plurality of households; b. monitoring the concurrent environmental conditions at the location of the said plurality of households; c. analyzing each household's power consumption, and extracting weighted failure indications of inefficient or malfunctioning HVAC systems; d. determining the probability of various HVAC conditions of malfunction of inefficiency, according to the said weighted indicators; and e. emitting a an alert in relation to the said condition of HVAC malfunction of inefficiency, comprising at least one of: HVAC malfunction type, probability, probable cause, and suggested action.

Abstract: The present invention provides a method for forecasting load and managing a control plan for households having electric appliances wherein the control plan determines the activation of the electric appliances at pre-defined control periods.

Abstract: Method and systems for analyzing event log elements are provided. In one example, a method includes receiving an event log element in a computer. A similarity index is calculated between the event log element and a text element. A threshold of similarity is calculated. The similarity index is compared to the threshold. If the similarity index is greater than the threshold, the event log element is grouped into a cluster with the text element to create a file of cluster assignments.

Abstract: Determining suspected root causes of anomalous network behavior includes identifying anomalous components in a network exhibiting anomalous behavior from a plurality of network components, assigning a likelihood score to network components based on a scoring policy that considers recent change events affecting the anomalous components, and identifying a subset of the network components that are suspected to be root causes based on the likelihood score.

Abstract: In one example implementation, a log analysis system can comprise an activity engine to monitor user activity of a computer system, a baseline engine to generate an expected baseline of a log, and an abnormality engine to compare the log to the expected baseline to identify an abnormality, compare the abnormality to a user activity volume based on a correlation between the user activity volume and the log activity, and classify the log.

Abstract: A non-transitory, computer readable storage device includes software that, while being executed by a processor, causes the processor to choose, based on user activity, a plurality of candidate parameters to be monitored from a plurality of event messages. Further, the processor executes the software to estimate a level of similarity between the chosen plurality of candidate parameters by computing a similarity score for at least two of the chosen candidate parameters. Still further, the processor executes the software to determine a plurality of parameters from the chosen candidate parameters if the similarity score for the plurality of parameters is greater than a threshold.