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: 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 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 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: The present invention provides a method for identifying abnormalities in personal energy consumption/usage. The method comprising the steps of: generating personal dynamic forecast model of energy usage patterns in sub-hourly resolution for defined time periods, based on historical personal usage data considering environmental/weather condition (temp, humidity), wherein the dynamic model apply adaptive gradient boost iterative learning algorithm using predefined periodical features and determining abnormalities of actual energy usage in defined time period by comparing predictions of the forecast model, wherein the predictions are calculated by applying the generated forecast model with the actual environmental condition at the relevant time period and identifying delta between the actual usage and the predicted usage patterns which exceeds a predefined threshold for predefined duration.