Abstract: The present invention provides a method for determining consumption of appliances within a surveyed household in which there are no sensors for measuring consumption of specific appliances.

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: 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 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.