Disclosed are system 100 and method of energy efficient hot and cold water management. The system 100 comprises: a control unit 101 for dynamically controlling the functioning of the system 100; at least a water storage tank 102 with at least one of a water level sensor, at least a heating element 1021, at least a temperature sensor 1022 or any combination thereof; at least an auxiliary water storage tank 103 with at least one of a water level sensor, at least a temperature sensor 1032; at least a water mixer unit 105 having at least a temperature sensor 1051; a user interface unit 1012 for controlling and monitoring different parameters including temperature, water level, opening and closing of valves 108 and 109; and a power supply with power backup unit 104 for providing basic power for proper functioning of the system 100.

A method for controlling a water heater comprising: receiving report information transmitted by a user-wearable device at a predetermined frequency; determining whether a user is coming back according to user location information included in the report information; and if the user is coming back, turning on a return water pump to permit water outputted from a water outlet of the water heater to return to the water heater through a water return port. The present disclosure determines a movement direction and the user location by using report information transmitted by the user-wearable device. If the user is coming back, the return water pump is turned on to permit water to return, so that the user can take a hot bath immediately after arriving home, thereby improving user's experience.

A burner appliance is disclosed. The burner appliance includes a byproduct sensor in an exhaust flue and/or a barometric pressure sensor to detect an environmental pressure at the burner appliance. By calculating concentrations of combustion byproducts in the exhaust with the byproduct sensor, a controller can adjust blower speed and/or fuel rate to modify combustion efficiency. By calculating the environmental pressure at the burner with the barometric pressure sensor, the controller can adjust blower speed and/or fuel rate to modify combustion efficiency. The barometric-pressure data can also be used to adjust blower speed control bands, thereby calibrating the control bands based on environmental pressure. The environmental pressure can be indicative of altitude and/or weather conditions. Methods of operating said burner appliance are also disclosed.

A collection of methods for monitoring the operation of a heating system configured to heat a space is provided. One method includes obtaining, by one or more computing devices, data from a fuel sensor for a period of time, the fuel sensor configured to detect an amount of fuel within a fuel supply for a furnace of the heating system. The method further includes determining, by the one or more computing devices, an adjustment to the operation of the heating system based, at least in part, on the data obtained from the fuel sensor. The method additionally includes adjusting, by the one or more computing devices, the operation of the heating system according to the adjustment.

A hot water supplier monitoring system includes a monitoring server monitoring hot water suppliers via a communication network. The hot water supplier supplies hot water by adjusting temperature of hot water heated by a heat exchange unit using combustion heat from combustion operation. The monitoring server accumulates operation data of combustion operation transmitted from the suppliers, and predicts and notifies malfunction occurrence for each supplier based on trend of change in the data over time. The data includes number of flame quenchings of the combustion unit in combustion operation. The monitoring server divides the suppliers into groups according to installation areas, compares the number of quenchings of same period among the groups, compares with previous number of quenchings of same group, and, for a specific group determined having an abnormal number of quenchings, excludes the data determined having an abnormal number of quenchings and predicts malfunction occurrence for each supplier.

A hot water supplier monitoring system includes a monitoring server monitoring hot water suppliers via a communication network. The hot water supplier supplies hot water by adjusting temperature of hot water heated by a heat exchange unit using combustion heat from combustion operation. The monitoring server accumulates operation data of combustion operation transmitted from the suppliers, and predicts and notifies malfunction occurrence for each supplier based on trend of change in the data over time. The data includes number of flame quenchings of the combustion unit in combustion operation. The monitoring server divides the suppliers into groups according to installation areas, compares the number of quenchings of same period among the groups, compares with previous number of quenchings of same group, and, for a specific group determined having an abnormal number of quenchings, excludes the data determined having an abnormal number of quenchings and predicts malfunction occurrence for each supplier.

A sensor cleaning system for a vehicle is disclosed. In particular, the sensor cleaning system has anti-freezing and defrosting functions. The sensor cleaning system includes: an air tank configured to store compressed air; a heating element disposed in the air tank and configured to apply heat to the air tank; and a controller configured to operate the heating element when a preset condition is satisfied.

A sensor cleaning system for a vehicle is disclosed. In particular, the sensor cleaning system has anti-freezing and defrosting functions. The sensor cleaning system includes: an air tank configured to store compressed air; a heating element disposed in the air tank and configured to apply heat to the air tank; and a controller configured to operate the heating element when a preset condition is satisfied.

A method for remotely diagnosing an abnormality in a climate control device includes the following steps: (a) receiving, at a diagnostic device remote from the climate control device, a signal representing one or more operating parameters of the climate control device, (b) generating, at the diagnostic device, an operating state metric at least partially from the signal representing the one or more operating parameters, (c) comparing, at the remote diagnostic device, the operating state metric to a reference metric, and (d) diagnosing, at the remote diagnostic device, the abnormality in response to a difference between the operating state metric and the reference metric.

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 (P) surpasses a predefined threshold (Pth), and henceforth label said detected events as “water heater activation” events; For each house (i) in the training set, and per each consumption day (d), define a binary label L.sub.id. Said label marks the water heater's activity as either ‘Normal’ or ‘Abnormal’ per house i and day d. Initialize all labels as ‘normal training a machine learning algorithm, to create at least one classification model, wherein all monitored households are classified according to said acquired data and parameters; and Using the Activation Events Classification Model after the training stage, to predict the binary label, L.sub.id, or number activation per day that a specific household (i), from beyond the household training set has surpassed a predefined number of water heater activation events (n) within a day.