Method and system for HVAC malfunction and inefficiency detection over smart meters data

A method for determining conditions of malfunction or inefficiency of Heating Ventilation and Air Conditioning (HVAC) systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC, said method 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:

• 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 or inefficiency, according to the said weighted indicators;
• e. emitting an alert in relation to the said condition of HVAC malfunction or inefficiency, comprising at least one of:
• HVAC malfunction type, probability, probable cause, and suggested action;
• wherein the analysis of households' power consumption is performed according to the following steps:
• a. obtaining elaborate data pertaining to each household within a household training group;
• b. applying machine learning algorithms to the said accumulated training group data, to build at least one classification model, thus classifying the households' training group to form classification subgroups;
• c. obtaining power consumption data and household profile information pertaining to a general group of monitored households, beyond the household training group;
• d. calculating the best fit of every household in the monitored group of households to said classification subgroups;
• e. extracting weighted failure indications of inefficient or malfunctioning HVAC systems.

A system for determining conditions of malfunction or inefficiency of Heating Ventilation and Air Conditioning (HVAC) systems, within a plurality of monitored households, in which there are no sensors for directly measuring the power consumption per specific HVAC, said system comprising:

• a. a non-transitory computer readable storage device and one or more processors operatively coupled to the storage device on which are stored modules of instruction code executable by the one or more processors;
• b. a plurality of household power meters, installed at a respective plurality of monitored households, said meters transmitting regular household power consumption readings to a server module;
• c. the said server module, configured to monitor the said regular household power consumption and the concurrent environmental conditions at the location of the said plurality of monitored households, wherein said monitored power consumption includes only the total consumption of each household;
• d. a preprocessing module incorporated within the server module, configured to analyze each household's total power consumption and extract weighted failure indications of inefficient or malfunctioning HVAC systems, wherein the analyzing is implemented by applying machine learning algorithm;
• wherein the malfunction indications include at least one of the following:
• power consumption pattern, trends of increase in power consumption, incompatibility with consumption models or top percentage power consumption;
• and
• e. a decision module incorporated within the server module, configured to determine the probability of various HVAC conditions of malfunction or inefficiency, according to the said weighted failure indications, wherein said decision module is configured to emit an alert in relation to the said determined condition of HVAC malfunction or inefficiency, alert comprising at least one of:
• HVAC malfunction type, probability, probable cause, and suggested action.