CN105473354A - Power consumption assessment of an HVAC system - Google Patents

Abstract

Method, system and product for assessments of an operation of a Heating Ventilation or Air-Conditioning (HVAC) unit are provided. In one embodiment, the apparatus comprises: an information receiving module for receiving, directly or indirectly, information from at least one sensor; and a power consumption determination component for indirectly assessing power consumption of an HVAC unit from the information received from the at least one sensor. In another embodiment, the apparatus being configured to receive a set of physical measurements over time from a physical location that is affected by the HVAC unit; fitting the set of physical measurements on a predetermined curve using parametric fit, wherein the predetermined curve has an horizontal asymptote, wherein the predetermined curve has a decreasing slope over time; and comparing a target measurement of the HVAC unit with a location of the horizontal asymptote to determine whether the HVAC unit is expected to reach the target measurement.

Description

Power Consumption Evaluation of HVAC Systems

Cross References to Related Applications

This application claims the U.S. Provisional Application entitled "Power consumption prediction and estimation of an HVAC system by using passive reading soft temperature and/or humidity sensors," filed August 18, 2013 No. 61/867,072 and U.S. Provisional Application No. 61/867,072, filed May 14, 2014, entitled "A completely external and cable free device and system that connects air conditions to the internet and uses sensory data to provide superior comfort energy savings" .61/992,993, the contents of both applications are hereby incorporated by reference in their entirety.

technical field

The present disclosure relates generally to the control of Heating, Ventilation, or Air Conditioning (HVAC) systems, and more particularly to estimating and predicting power consumption of HVAC units.

Background technique

HVAC systems constitute a significant factor in the energy consumption of residential as well as commercial consumers.

Existing HVAC systems typically specify the expected power consumption or capacity of multiple units. However, the actual power consumption of individual units will vary significantly due to one or more of a number of factors, such as installation location or configuration, frequency of use, target temperature, fan settings, local weather which may include outdoor temperature or humidity conditions , room or space dimensions, room occupancy, or other factors.

During normal use, the actual power consumption of the appliance is usually not measured or displayed to the user, thus making it impossible for the user to know the actual power consumption of the appliance. However, actual power consumption, or at least a close estimate of actual power consumption, may be important to the user as it directly reflects the cost the user can bear.

Additionally or alternatively, the manner in which a particular HVAC system is installed and used can directly affect power consumption. The usage pattern may relate to aspects such as the number and type of units installed, the location of the installation, the target temperature, fan settings, or others. Some of these factors represent inherent trade-offs between user comfort and power consumption, or between initial purchase and installation costs and day-to-day power consumption. Therefore, providing the user with information about power consumption is important to the user's decisions, such as how to set up or operate the unit on an ongoing basis, and whether the user should replace the unit with one that is more economical or more efficient for the room or area. suitable unit etc.

In addition to actual power consumption, it may be of value to the user to estimate the efficiency of the unit in relation to cooling, heating, dehumidification, etc. For example, detection of degradation in efficiency of an appliance over time may indicate the need for maintenance actions, such as filter cleaning or replacement, refrigerant change or recharging, and the like.

Some known solutions for estimating the power consumption of an appliance include a power consumption meter connected between the power outlet and the appliance. These meters can provide information such as instantaneous power consumption, cumulative energy consumption, expected monthly energy consumption, and more. While these gauges can be highly accurate, they can be difficult to install because the unit's power plug is not always easily accessible. This is especially the case where the compressor of the HVAC unit may be installed outdoors, mounted on a wall or even hidden above the ceiling. In some cases, there are not even any power outlet plugs and the unit may be connected directly to the power cord.

Furthermore, currently available meters are generally unable to predict future energy consumption, as future energy consumption may be affected by user actions such as changing the target temperature or increasing the fan speed, thus rendering the user an ambiguous binary decision to turn the system on or off .

Description of drawings

The presently disclosed subject matter will be more readily understood and appreciated from the following detailed description taken in conjunction with the accompanying drawings, wherein corresponding or similar reference numerals or symbols indicate corresponding or similar components in the drawings. The drawings provide exemplary embodiments or aspects of the present disclosure and do not limit the scope of the present disclosure unless explicitly stated otherwise. In these drawings:

1 is a schematic diagram of a system for power consumption estimation and prediction of HVAC units, according to some exemplary embodiments of the disclosed subject matter;

FIG. 2A is a graph of exemplary temperature over time in a zone where HVAC is used and a target temperature is achieved;

FIG. 2B is a graph of exemplary temperature over time in areas where HVAC is used and target temperatures are not achieved;

2C is an exemplary graph of measured temperature versus target temperature, according to some embodiments of the disclosed subject matter; and

3 is a flowchart for estimating and predicting power consumption of an HVAC system, according to some exemplary embodiments of the disclosed subject matter.

detailed description

One technical problem addressed by the disclosed subject matter is the need to estimate the current power consumption of appliances, particularly heating, ventilation and air conditioning (HVAC) units. Although factory data or estimates of power consumption are sometimes available, actual power consumption may vary significantly due to installation and use environment and settings, normal wear and tear and damage, influence of other units or appliances, etc.

Such daily estimates specific to a particular unit can be used to evaluate the consumption implications of the unit's current actuation state, or the cumulative consumption implications.

Another technical problem is related to the evaluation of future predictions of power consumption, which is desirable in response to changes in user behavior or other conditions, such as changing target temperature, closing doors, adding one or more occupants to the room, and the like.

Such predictions help to take appropriate decisions in a timely manner, such as raising or lowering target temperatures or changing fan speeds, taking maintenance actions such as filter cleaning or actuating another unit, or making decisions about installing or The longer term decision to replace the HVAC unit. Such a decision is made manually by the user, where the user is informed of the prediction and potentially provided with a recommendation, or automatically without user intervention, user input, user awareness, etc. .

There is therefore a need in the art for methods and apparatus for evaluating current and predicted power consumption of HVAC systems.

In some cases, user control signals are available, and it is also an object of the present invention to provide a method and system for the correlation between such signals and power consumption estimates, thereby providing the user with Information out of decisions related to HVAC usage.

The method and apparatus may be operable using only conventionally available measurements, such as temperature and/or humidity. However, evaluation and prediction can improve given signals such as user control signals, third-party signals such as local weather information, inputs from multiple sensors such as multiple temperature or humidity sensors, and room occupancy and opening signal related to the on/off condition of the

It is also an aim of the present disclosure to provide the user with advice related to the daily operation of the HVAC unit, to take maintenance measures or to change the HVAC settings of the environment, such as adding another unit.

It is another object of the present disclosure to enable the use of external meters, such as meters that provide financial information, where connection of the meters to the HVAC unit is not possible, impractical, or desirable.

One technical solution is to provide a control unit that indirectly estimates and predicts the power consumption of one or more HVAC systems using information obtained from sensors such as temperature and/or humidity sensors. These sensors can be positioned in locations affected by the HVAC unit. For reasons of brevity, the following discussion relates to cooling. However, it should be appreciated that the discussion applies equally to other effects of the HVAC unit, particularly heating or dehumidification. The solution may be implemented by a controller implemented as part of the HVAC unit, or located outside the HVAC unit, eg as part of a remote control of the unit, or on a separate component.

During a calibration phase which may be performed for a particular HVAC model or a particular unit, a long-term cache may be obtained, wherein sensor measurements are received by a controller maintaining such a cache including benchmarks representing the HVAC unit or model A collection of measurements of behavior. Benchmarks can reflect the cooling performance of the HVAC system, for example, how long it takes to reach a certain target temperature, the range of target temperatures that can be achieved, etc. A benchmark may include one or more sets of measurements related to, for example, different target temperatures, different seasons or hours, different levels of area occupancy, or other changing conditions. The baseline behavior may include one or more graphs describing the behavior of the unit, such as room temperature over time. A benchmark may also include a mapping between unit actuation and power consumption of the unit in general or in a specific case. For example, power consumption can be obtained when the unit is running at different motor speeds.

Once this reference is available, measurements are continuously received from the sensors. Measurements may be received from any sensor adapted to output or otherwise provide or report its measurements. Measurements can be collected using sliding time windows and analyzed in comparison to baseline behavior. If the behavior is consistent with either baseline behavior over a period of time, then current conditions and settings can be assumed to be similar to the baseline behavior, and current or cumulative power consumption can be estimated. In addition to measurements, power consumption estimates can also use: user-initiated signals such as signals from remote control devices, infrared sensors, security systems, temperature sensors on smartphones or other devices, ceiling fans, heating systems, Other HVAC units, third-party data such as weather information, signals generated by correlating multiple proximity sensor readings, proximity sensor information, signals received from door or window control systems, etc.

Additionally or alternatively, an alert may be issued to the user. For example, if the cell behavior is consistent with that of an unacceptable target temperature, the user may receive a recommendation to increase the target temperature to avoid useless non-economic behavior.

If the behavior first coincides with the baseline behavior and then a deviation is detected, possible causes of the deviation can be identified and the correct behavior can be suggested to the user. For example, if a sudden rise in temperature or humidity is detected, it can be deduced that an opening in the location where the HVAC unit is installed, such as a door or window, has been opened. If the temperature rise cannot be mitigated, then the user may be advised to close the opening.

If it is detected that the HVAC unit is not as efficient as the associated baseline, it can be deduced that the unit needs periodic maintenance, such as filter cleaning, and corresponding recommendations can be presented to the user.

If the corrective action is related to changing the settings of the HVAC unit, the controller can issue a corresponding command, and the unit settings can be changed automatically without requiring any action on the part of the user. Additionally or alternatively, the user may or may not be aware of changes that have been made.

One or more future power consumptions may be estimated given the baseline behavior corresponding to the current measurement. For example, predicted power consumption may be estimated if current conditions are maintained, if temperature rises or falls by a degree or more, if additional units are operated, etc.

One technical effect of the disclosed subject matter is estimating the actual power consumption of an appliance without directly measuring the power consumption of the unit, but measuring the power consumption of the unit from an external measurement. Power consumption estimates can be important to users because power consumption immediately translates into cost. The estimation may take and incorporate the following signals: signals received from a remote control, third party signals such as local weather information, inputs from multiple sensors such as multiple temperature or humidity sensors, signals from proximity sensors , signals from HVAC units, network signals, etc.

Another technical effect of the disclosed subject matter is the prediction of future power consumption under one or more sets of conditions or assumptions, such that a user can choose whether to take action in view of the predicted power consumption.

Yet another technical effect of the disclosed subject matter is identifying situations where one or more short-term actions may reduce power consumption without significantly impacting performance, and recommending any of these actions to the user, such as changing the target temperature, changing the fan Speed, closing a door or window if it is opened, opening or closing one or more units, etc.

Yet another technical effect of the disclosed subject matter is the option to use any temperature or humidity sensor, thus enabling users to use existing sensors installed for other purposes without incurring the additional costs associated with installing additional sensors .

Yet another technical effect of the disclosed subject matter is the ability to overcome differences in sensor measurements, such as readings from sensors of an HVAC unit and readings from sensors external to the HVAC unit. The disclosed subject matter can be used to estimate readings of sensors of an HVAC unit based on readings of external sensors. The estimated readings can be used to take actions and operations on the HVAC unit, such as determining what target temperature the HVAC unit should be set to.

Referring now to FIG. 1 , shown is a schematic diagram of a system for power consumption estimation and prediction of HVAC units, according to an embodiment of the disclosed subject matter.

The power consumption prediction system, generally designated 100, communicates with one or more sources of information such as, but not limited to, one or more temperature sensors 110 or one or more humidity sensors 120, one or more user action transmitters, 124, such as a remote control device that communicates commands from a user, or communicates with one or more additional signal sources 128, such as but not limited to weather information sources, occupancy sensors, opening sensors, and the like. Depending on the type of sensor or source, each sensor or source may be mounted on, or otherwise combined with, or form part of, an HVAC unit, be located anywhere in the HVAC-related area, or be located remotely. System 100 may be configured to receive information from sources such as 110, 120, 124, 128, etc., directly or indirectly. In some exemplary embodiments, information may be received via a wired connection or a wireless connection. Additionally or alternatively, information may be received via a computerized network such as the Internet, a local area network (LAN), etc., to which both system 100 and the source may be connected.

It should be appreciated that any sensor may be a general purpose commercially available sensor adapted to provide information or measurements in any channel and format acceptable to system 100, such as wired or wireless communication protocols. No sensor needs to be of a specific type or mounted using a specific mounting method. However, it should be appreciated that sensors such as temperature or humidity sensors need to be installed so that their measurements are representative of the relevant area, eg the area affected by the HVAC system being measured.

System 100 may include one or more processors 104 . Processor 104 may be a central processing unit (CPU), microprocessor, electronic circuit, integrated circuit (IC), or the like. Processor 104 may be used to perform calculations required by system 100 or by any one of the subcomponents of system 100 .

In some exemplary embodiments of the disclosed subject matter, system 100 may include input/output (I/O) devices 108 such as displays, buttons, pointing devices, keyboards, touch screens, and the like. I/O device 108 may also include a disk drive or may provide communication with a storage device or a network.

System 100 may include a man-machine interface (MMI) module 112 that may be used to provide output to and receive input from a user using one or more of I/O devices 108 . Outputs may include estimated, cumulative or forecasted power consumption, or information based on such power consumption, such as recommendations, expected costs, and the like. The MMI module 112 is also used to display to the user current measurements such as temperature or humidity measured by sensors external to the HVAC unit, temperature or humidity measured by built-in sensors of the HVAC unit, or estimates of these measurements, target temperature or humidity, The expected time it will take for the HVAC unit to cool the room to the target temperature, the expected power consumption required to cool the room to the target temperature, or any other relevant information.

System 100 may include one or more information receiving components 132 for receiving information from one or more of temperature sensor 110, humidity sensor 120, user action transmitter 124, external information source 124, or other sensors or information sources. Multiple measurements or information. As another example of additional sensors, in some exemplary embodiments, a vibration sensor mounted on the HVAC unit may provide information that is helpful in estimating whether the HVAC compressor is actuated.

The system 100 may also include an HVAC information obtaining module 136 for receiving information directly or indirectly from the HVAC system. For example, the HVAC unit may send signals indicating motor on/off, fan speed changes, flap direction changes, etc. These signals can be received or intercepted by module 136 and the extracted information can be used.

In some exemplary embodiments, system 100 may include storage device 116 . The storage device 116 may be a hard drive, a flash drive, random access memory (RAM), a memory chip, or the like. In some exemplary embodiments, storage device 116 may store program code operable to cause processor 104 to perform actions associated with any of the subcomponents of system 100 . The components detailed below may be implemented as one or more sets of interrelated computer instructions executed, for example, by processor 104 or another processor. A component may be implemented as assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or a source written in any combination of one or more programming languages Code or object code, including object-oriented programming languages such as Smalltalk, C++, etc., and traditional procedural programming languages such as the "C" programming language or similar programming languages.

Storage 116 may include a long-term acquisition and analysis module 140, also referred to as a long-term module, for acquiring and analyzing information describing baseline behavior of the HVAC unit as provided by the manufacturer or in a particular environment.

The long-term analysis module 140 may receive measurements and extract information about the cooling characteristics of the HVAC unit, referred to as the "learned characteristics" of the unit, e.g., how long the HVAC unit takes to reach a target temperature, which target is able to be achieved temperature etc. Information may be collected over a longer period of time, such as hours, week, month, year, etc., which is sufficient to learn the characteristics of the HVAC unit and its operation. Information may be collected and stored for future use. For example, the information can be used later to refine the approximations of other modules detailed below. Long-term behavior can be used when looking for deviations from daily or expected behavior or operating modes of the HVAC unit. However, it can be appreciated that in some applications, the long-term analysis module 140 may be omitted.

Long-term behavior can be represented by a collection of points, each point representing the temperature at a particular point in time. If a visual display is provided, multiple points may be connected to form a graph such as that shown in FIGS. 2A and 2B . Additionally or alternatively, long-term behavior may be represented as a series of HVAC behaviors, such as motor on/off, motor or fan speed changes, etc., each of which may be associated with a time stamp. Long-term behavior can hold information such as power consumption associated with a particular state, power consumption of a motor such as on-state, power consumption at different motor speeds if the motor is available, power consumption at different temperatures, etc. .

In some embodiments, the long-term behavior information may be provided by the manufacturer of the HVAC or by a third party, for example, in any format such as text, spreadsheet, or any suitable format. Behaviors can be provided in advance and saved in a computer readable form. As an example, behaviors may be stored in computer readable files that save behaviors in any computer readable form. Additionally or alternatively, the behavior may be saved in a data store.

In some embodiments, long-term behavioral information may be provided by a user who may enter observations of specific unit behavior. The mapping can be provided using a user interface through which the user can select a target temperature or humidity and receive HVAC behavior such as compressor on/off status, motor speed, fan speed, fan direction, flap movement, etc. The HVAC behavior can be provided to the system 100 as a baseline behavior.

In yet other embodiments, the behavior may be obtained automatically by the system, for example after installation or reset of the unit. Behavior can be obtained by setting target temperatures and receiving associated measurements. In some embodiments, the long-term analysis module 140 may learn a correlation between local weather information and the time or energy required to cool the space. This learning can be done passively, but in some embodiments, this learning can be done actively by cooling the space at multiple times when different outdoor temperatures are reported, such as when the user is known to be going outside. Report.

Reference is now made to FIGS. 2A and 2B which show graphs 200 and 204 of temperature versus time, respectively, under two conditions. Figure 2A shows temperature as a function of relative or absolute time in environments and settings where the target temperature is reached and maintained, whereas Figure 2B shows temperature as a function of relative or absolute time in environments and settings where the target temperature is not reached temperature that changes over time. The two graphs represent the effective behavioral regions 208 and 212 respectively at moments after the start of the cooling operation. FIG. 2A then shows a region 216 where the target temperature is reached and maintained, which is illustrated by the curves having substantially equal minima. This behavior is obtained by periodically switching the compressor on and off. However, FIG. 2B shows a region 220 in which the temperature change decreases infinitely over time and the measured temperature asymmetrically reaches a steady state temperature. This behavior is typical when the target temperature is not reached, causing the unit to operate in an inefficient manner. In this case, an increase of the target temperature by more than one degree may be provided for a temperature measurement which is not significantly different from the situation of FIG. 2B , but which may result in operating the HVAC unit in a more efficient manner.

It should be appreciated that the long-term analysis module 140 may be used to evaluate one or more benchmarks related to other behaviors of the unit, such as an estimate of the difference between the device's external measured temperature and internal temperature, the device's fan speed, As detailed below in connection with dedicated modules.

It should be appreciated that the received measurements or modules listed below may be physically stored in the same device as one or more measurement sensors. In other applications, sensor information may be received from a measurement sensor, which may be located at any location, and some or all of the processing may be done on a different platform nearby or remotely.

Referring back now to FIG. 1, storage device 116 may include power consumption determination component 144, which optionally includes a plurality of specific modules for identifying specific behaviors, and for integrating these behaviors, analyzing these behaviors, establishing recommendations, providing The current and expected power consumption of the unit or provide a suggested module or modules. Certain modules may include any one or more of the modules detailed below, although it should be appreciated that other modules may also be used.

In some embodiments, power consumption determining component 144 may include a motor on/off module 148 that determines whether a motor is actuated at a given time.

In typical operation of an HVAC unit, the temperature is reduced until the temperature reaches a target temperature. During this phase, the motor may be stopped and started in a periodic cycle based on the unit's thermostat readings to maintain the temperature substantially constant, for example as shown in Figure 2A. However, if the target temperature is not reached, the motor will not be stopped, and the room will eventually reach thermal steady state, as shown in Figure 2B. Using the pattern shown, the motor on/off module 148 can determine at a given point in time whether the motor is currently on or off. This determination can be done by examining the graph of measured values for a drop in temperature or humidity indicative of motor action.

When estimating whether the motor is on or off, if the temperature pattern is similar to that of Figure 2A, then it can be determined that the motor can be turned on until the target temperature or humidity represented by the minima of the periodic pattern is reached. After reaching this pattern, it is deduced that the motor turns on during the temperature drop and turns off during the temperature rise. However, if the observed graph is similar to that of Figure 2B, then it can be determined that the motor is always on.

In another embodiment, the motor on/off module 148 may determine whether the motor is on or off based on a signal received from a vibration sensor that is mounted on or near the HVAC unit or compressor and that senses vibrations of the HVAC unit. Additionally or alternatively, a microphone may be used to sense the sound made by the motor when it is operating.

In environments where a user controls the HVAC unit, such as by providing an on/off command, such information can be provided as input from the user (eg, by intercepting commands to the HVAC unit) and can be automatically derived from sensors.

The power consumption determination component 144 may include a motor speed estimation module 152 that may be used when the HVAC unit is an inverter air conditioner that supports actuation with a variable motor speed. In such a system, the effect of changing the motor speed can be determined by observing the slope of the temperature/humidity change when the motor is turned on. The motor speed is basically related to the derivative of the temperature or humidity versus time graph, so that the steeper the slope, the higher the motor speed. Possible motor speeds, which may be discrete or continuous, may be obtained from information provided by the long-term acquisition and analysis module 140 .

The power consumption determination component 144 may include a power derivative (dP/dT) estimation module 156 for estimating the power consumption required to cool or heat a room by one degree, assuming the motor will be turned on (at a particular speed, if appropriate) .

Using a parametric model such as an exponential model, the initial temperature drop when cooling starts can be obtained. The model can be obtained using the long-term behavior obtained by the long-term acquisition and analysis module 140, and the slope of the curve can be used to estimate the derivative dP/dT for the current control setting. Because dP/dT is estimated online, the function can further extrapolate the derivative of temperature, assuming the compressor is turned on. This allows predicting how long it will take to cool a room one degree in time at any point in time, and thus predicting the associated power consumption. The dP/dT derivative can be determined from the known power consumption of the device for the current motor speed, which can be given by the device specification.

It should be noted that the power derivative estimation module 156 can be related to the humidity measurement and can be used to estimate the power consumption required to decrease/increase the humidity by one unit of measure.

The power consumption determination component 144 may include an internal temperature estimation module 160 for estimating the internal temperature of the HVAC unit. Typically, this temperature is used for thermostatic control of the unit and is not reported externally. To estimate the internal temperature state of the HVAC system, the external temperature measured by one or more sensors, such as sensors mounted on or near the HVAC unit, may be recorded and analyzed. Specifically, the temperature at the local minimum of the wave in region 216 of FIG. 2A may represent the target temperature of the HVAC unit. Thus, multiple data points may be collected, each data including time and measured temperature, and optionally, target temperature. Once such measurements are collected, a parametric fit of the data, such as a linear fit, can be performed for evaluating the deviation between the measured temperature and the internal measured temperature.

Referring now to FIG. 2C , an exemplary collection of such data points, and a linear fit between these data points is shown.

It can be appreciated that even if the thermostat cannot reach the equilibrium state, for example as shown in FIG. 2B , the target temperature can still be derived through the above-mentioned parameter fitting of the temperature versus time curve.

It can also be appreciated that additional data, such as outside temperature, room humidity, occupancy sensor signals, etc., can be considered and included in the model for estimating the internal temperature measured by the unit, since the internal temperature and the external temperature Respond differently to weather and weather changes, eg where the unit is positioned on an exterior wall or near a window, and react differently to different room occupancy, etc.

In some exemplary embodiments, module 160 may be configured to estimate one or more other internal measurements of the HVAC, such as temperature or humidity.

Referring back now to FIG. 1 , the power consumption determining component 144 may include a fan speed estimation module 164 . The fan speed can be parameterized according to the cooling-induced lag of the system. This time lag can be measured because the time required for the temperature drop to become apparent after the HVAC starts cooling operation. This time measurement can be significant even when the temperature is measured very close to the HVAC unit.

The measured time lag between the start of HVAC operation and the temperature drop can be used as data points in any clustering algorithm such as but not limited to the K-means algorithm for determining fan speed, which is usually discrete , such as low, medium, and high. In some embodiments, a more direct approach may be used. Airflow from the HVAC unit can cause light vibrations, the magnitude of which is related to fan speed. Thus, if a sensor is placed on the HVAC unit itself whether integrated with the system or not, the sensor can use the output from the vibration sensor to sense wind speed.

The power consumption determining component 144 may include an air filtration state estimation module 168 for sensing the state of the air filtration of the HVAC system. A clogged air filter in an HVAC system poses a health threat and reduces the efficiency of the HVAC system. Therefore, it is beneficial for the user to know if the filter needs to be cleaned. The state of the filter can be detected using a passive temperature/humidity sensor and requires continuous measurement of time to reach a specific target temperature. If the time becomes too long, the efficiency of the HVAC unit exhibits a gradual decrease. A gradual decrease in efficiency can be detected and can be used as an indication that the filter needs to be cleaned. Additionally or alternatively, if the output of the vibration sensor drops to a lower fan speed, the sensor can indicate clogging of the filter.

It should be appreciated that while analyzing the behavior or state described above can benefit from using information obtained by the long-term analysis module 140, it is not necessary to have such long-term information. The behavior or state can optionally be achieved by analyzing the measured values and performing a parametric fit of the measured values on a curve, such as of the form y=1/x,y=1/sqrt(x),y=1 Exponential curve of /ln(x), etc. An information model can thus be obtained, and extrapolation of the information model can provide such information. For example, it can be determined whether the asymptote of the curve lies below or above the target measurement. In some cases, a target measurement below the asymptote of the curve may indicate that the target temperature was not reached. Additionally or alternatively, a target measurement above the asymptote of the curve may indicate that the target temperature can be efficiently reached. Additionally or alternatively, the length of time required to reach the target temperature or a temperature near the target temperature, and the required power consumption may also be estimated and provided to the user. If the difference between the HVAC unit's internal temperature measurement and the external temperature has been analyzed, then either scale can be used to represent the target temperature. It should also be noted that the comparison between the target temperature and the asymptote of the curve can be performed based on measurements of the same range of values, such as converting the internal target measurement to the value range of the external measurement, or converting the external measurement to the internal The numeric range of the target measurement.

The power consumption determination component 144 may include an integration, analysis and prediction module 172 for integrating outputs from the particular modules described above or from different modules and analyzing the integrated data for estimating the power consumption of the HVAC unit. Power consumption determining component 144 may also receive measurements or indications from a plurality of sensors. It should be understood that certain modules detailed above may be implemented as part of the integration, analysis and prediction module 172 . Alternatively, specific modules may be executed external to the integration, analysis and forecasting module 172 and information from the specific modules may be used by the integration, analysis and forecasting module 172 . It should be appreciated that the integration, analysis and prediction module 172 may sometimes use information received from the HVAC unit itself, such as compressor on/off status, fan speed, etc., and use this information for predictions, including power consumption predictions.

The power consumption determination component 144 may include a recommendation module 176 for establishing one or more recommendations for daily use, maintenance activities, or long-term decisions related to the HVAC unit. If the behavior first coincides with the baseline behavior and then a deviation is detected, possible causes of the deviation will be identified and the correct action suggested to the user. Additionally or alternatively, the system 100 may automatically take a suggested corrective action, such as communicating an instruction to the HVAC unit or other unit of the installation.

For example, if a sudden rise in temperature or humidity is detected, it may be deduced that a door or window has been opened, and if the temperature rise cannot be abated, the user may be advised to close the door or window.

In another example, if the target temperature can be reached but not at a later time, or if a moderate increase in temperature is detected, it can be deduced that more people enter the room and the second unit if possible need to be manipulated.

In another example, if the measured temperature indicates an unattainable temperature, the user may also be advised to increase the target temperature to enable the device to work more efficiently with similar results.

In addition to power consumption, it is also valuable to the user to estimate the efficiency of the unit, which may be related to cooling, heating, dehumidification, etc. For example, detection of degradation in efficiency of an appliance over time compared to baseline behavior may indicate the need for maintenance actions, such as filter cleaning or replacement, refrigerant change, and the like.

Additionally or alternatively, if inefficient behavior is detected over an extended period of time, one or more long-term actions may be suggested to reduce power consumption, such as repairing or upgrading a unit, installing another unit, etc.

Aggregated data including estimates or forecasts of the unit's power supply, or possible recommendations for the unit's operation or environment and possible consequences may be displayed to the user, sent to a monitoring or control system, stored in a file or database, or in a Other ways are used. Power consumption may be accumulated over a period of time, such as a month. Power consumption during this time period can also be inferred and then compared to actual power consumption. The power consumption may be provided in units such as kilowatts/hour, a cost to account for electricity rates that may vary depending on the date or time, etc.

In some embodiments, if power consumption exceeds a predetermined threshold, either temporarily or cumulatively, an alert may be issued to the user, or another action may occur, such as stopping the unit.

In some embodiments, the minimum temperature reached may be automatically set to a target temperature that can be efficiently reached while allowing the motor to be periodically de-energized.

It should be appreciated that integration, analysis, and prediction module 172 and recommendation module 176 may also use additional inputs, such as, but not limited to, one or more of the following:

User Operation Signals: Knowing how the user controls the HVAC unit can provide important information for evaluating power consumption, if such information is available. First, if the appliance is powered off, the power consumption is zero, thus eliminating many possible error modes. Second, the difference between the target temperature and the measured temperature can be modeled so that it can be known when a periodic on/off state of the thermostat is desired, thereby further refining and validating the estimate.

Local weather signals: Outdoor temperatures can affect how long it takes to cool a particular area. Therefore, the local outside temperature combined with the long-term behavior applicable to the relevant temperature can be used to determine the power required to cool the area to the target temperature.

It should be appreciated that determined or predicted power consumption may be provided to power consuming components to provide further information regarding the power consumption of the unit, even when such components cannot be connected to the HVAC unit. Such a component may provide information such as the temporary cost of the unit or the cost of an hour of actuation, as well as forecasts of projected monthly costs, and the like.

Referring now to FIG. 3 , a flow diagram of steps in a method for estimating or predicting power consumption of an HVAC unit is shown.

At step 300, a long-term model of HVAC unit behavior may be obtained. This model can be obtained from the manufacturer, the user, by operating the unit under various conditions and measuring the behavior, etc.

At step 304, the obtained model may be analyzed. For example, the following status or data may be obtained: determining whether to operate the unit at an achievable target temperature; determining whether to operate the unit at an unattainable target temperature; determining whether to increase or decrease the current temperature by one degree (or any other The amount of temperature) required power consumption, etc.

At step 308, daily information may be received from one or more sensors, such as a temperature sensor or a humidity sensor. Sensor readings may be received continuously, at intervals, or in any other manner and using any desired protocol.

At step 312, additional information as follows may be received: user actions such as opening or closing a unit, changing target temperature, etc.; occupancy sensor information; weather reports or forecasts; sensors to detect whether a door or window has been opened, etc.

At step 316 , one or more of the following power consumption or prediction aspects may be determined: such as whether the motor is on or off; current motor speed; power required to increase or decrease the current temperature; and the like.

Aspects can be determined using the measurements received from the sensors in steps 308 and 312, optionally by fitting or comparing these data to the long-term behavior acquired in step 300 and analyzed in step 304, or by Deviations from long-term behavior are identified, for example, by evaluating the effect of a predetermined behavior such as opening or closing a door or window.

In step 320, power consumption aspects may be integrated for power consumption estimation or prediction. Power consumption estimates may be temporary or cumulative over a period of time. Forecasts can use extrapolations of current conditions. Additionally or alternatively, financial aspects of power consumption may be determined by taking into account fees or fee policies.

Additionally or alternatively, if a particular condition is detected, one or more recommendations related to the actuation, maintenance or installation of the unit or other units may be determined, as detailed in connection with the recommendation module 164 in FIG. 1 .

At step 324, an estimate or forecast, whether power or financial, of the unit may be provided, eg displayed to a user on a display device, stored in storage device and file, sent to a monitoring device, etc. Additionally or alternatively, established suggestions may also be provided, eg displayed. Additionally or alternatively, the suggestions are performed automatically, with or without user approval.

It should be appreciated that the disclosed methods and apparatus can be used to evaluate the power consumption of other appliances, and that the disclosed methods and apparatus are not limited to HVAC units, and can be used in water heating systems, heating systems, pool heating, refrigerators, fish tanks, ovens, Thermostat systems, etc.

It should be appreciated that with regard to power consumption estimates or forecasts, external meters may be used for receiving estimates or forecasts and providing additional information such as temporal power consumption, cumulative power consumption, expected monthly power consumption, and the like. Use of the disclosed methods and systems enables deployment of external meters when it is difficult, impractical, undesirable, or even impossible to connect the meters directly to the HVAC unit. In addition, the gauge may provide information related to predicted energy consumption that may be affected by user actions such as changing the target temperature or increasing fan speed.

It should also be appreciated that the disclosed methods and apparatus can be applied to environments where multiple units may be used so that models, estimates and recommendations also take into account the interaction of the multiple units with each other and the environment.

As will be understood by those skilled in the art, the disclosed subject matter can be a system, method and/or computer program product. A computer program product may include a computer readable storage medium (or medium) having computer readable program instructions thereon causing a processor to perform aspects of the present disclosure.

A computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. A computer readable storage medium may be, for example, without limitation, electronic storage, magnetic storage, optical storage, electromagnetic storage, semiconductor storage, or any suitable combination of the foregoing. A non-exclusive list of specific embodiments of a computer readable storage medium includes the following examples: portable computer diskette, hard disk, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), memory sticks, floppy disks, recordings on them such as punched cards or raised structures in slots, etc. Mechanically encoded means with instructions and any suitable combination of the foregoing. Computer-readable storage media as used herein are not to be understood as transient signals in nature, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating via waveguides or other transmission media (e.g., light passing through fiber optic cables) pulses) or electrical signals propagated through wires.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to a respective computing/processing device or an external computer or external storage device via a network such as the Internet, a local area network, a wide area network, and/or a wireless network. . The network may include copper transmission cables, optical transmission cables, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and sends the computer readable program instructions for storage in a computer readable storage medium in the respective computing/storage device.

Computer readable program instructions for performing operations of the presently disclosed subject matter may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or in the form of a or any combination of programming languages, including object-oriented programming languages such as Smalltalk, C++, and traditional procedural programming languages such as the "C" programming language or similar programming languages. Computer readable program instructions can execute entirely on the user's computer and partly on the user's computer as a stand-alone software package that resides partly on the user's computer and partly on a remote computer or entirely on a remote computer or server . In the latter case, the remote computer may be connected to the user computer via any type of network, including a local or wide area network, or may be connected to an external computer (eg, via the Internet using an Internet service provider). In some embodiments, an electronic circuit comprising, for example, a programmable logic circuit, a field variable gate array (FPGA), or a programmable logic array (PLA) may execute computer-readable program instructions by using state information of the computer-readable program instructions To personalize electronic circuits, thereby performing aspects of the disclosed subject matter.

Aspects of the disclosed subject matter are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosed subject matter. It should be understood that individual blocks of any flowcharts and/or block diagrams, and combinations of blocks in the flowcharts and/or block diagrams can be implemented by computer program instructions.

These computer readable program instructions may be provided to a processor of a general purpose computer, a processor of a special purpose computer, or other programmable data processing apparatus to create a machine for The executed instructions create means for the user to perform the function/act specified in a block or blocks of the flowchart and/or block diagram. These computer-readable program instructions can also be stored in a computer-readable memory, which can instruct a computer or other programmable data processing device to function in a specific manner, so that the computer with the instructions stored therein can The readable storage medium includes manufacturing instructions including instructions for performing aspects of the functions/acts specified in one or more blocks of the flow diagrams and/or block diagrams.

Computer-readable program instructions can also be loaded into a computer, other programmable data processing equipment or other devices, so as to form a series of operation steps to be executed on the computer, other programmable devices or other devices, so as to generate a computer-executed process, thereby Instructions executed on computers, other programmable devices, or other devices implement specific functions/behaviors in one or more blocks of the flowcharts and/or block diagrams.

The flowchart and block diagrams in the figures illustrate algorithms, functions, and operations of possible embodiments of systems, methods and computer program products according to various embodiments of the disclosed subject matter. In this regard, each block in a flowchart or block diagram may represent a module, a section, or a portion of instructions including one or more executable instructions for performing specified logical functions. In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It should also be noted that individual blocks of the block diagrams and/or flow diagrams, and combinations of blocks in the block diagrams and/or flow diagrams, can be implemented by special purpose hardware-based systems that perform the specified function or behavior , or a combination of special-purpose hardware and computer instructions that execute.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present disclosure. As used herein, the singular forms "a" and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise. In addition, it should be understood that the term "comprising" used in the specification means that the listed features, wholes, steps, operations, elements and/or components appear, but does not exclude the presence or addition of other features, wholes, steps, operations, elements , one or more of the components, and/or combinations thereof.

The corresponding structures, materials, acts, and equivalents of all means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements. The specification of the disclosed subject matter has been presented for purposes of illustration and description, but is not intended to be exhaustive or to limit the disclosed subject matter to what is disclosed. Many modifications and changes will be apparent to those skilled in the art without departing from the scope and spirit of the disclosed subject matter. Embodiments were chosen and described in order to best explain the principles of the disclosed subject matter and their practical application, and to enable others of ordinary skill in the art to understand the disclosed subject matter, for the various embodiments, with several modifications is suitable for a specific purpose.

Claims (25)

1. An apparatus having a processing unit and a storage device, comprising: an information receiving module for directly or indirectly receiving information from at least one sensor; and A power consumption determining component for indirectly evaluating power consumption of a heating ventilation air conditioning (HVAC) unit using information received from at least one sensor. 2. The apparatus of claim 1, further comprising a long-term acquisition and analysis module for acquiring information describing at least one behavior of the HVAC unit. 3. The apparatus of claim 2, wherein the long-term acquisition and analysis module is for analyzing at least one behavior of the HVAC unit. 4. The device of claim 3, wherein at least one behavior is selected from the group consisting of: measurements over time to achieve and maintain target measurements; and The measured value during the time when the target measured value was not reached. 5. The apparatus of claim 4, wherein the measured value is selected from the group consisting of temperature and humidity. 6. The device of claim 1, wherein the power consumption determining means is to determine current, accumulated or predicted power consumption. 7. The device according to claim 1 , wherein the power consumption determining component comprises an integration, analysis and prediction module for estimating power consumption based on information from at least one module selected from the group consisting of : a motor on/off determination module; a motor speed estimation module; a power derivative estimation module; a fan speed estimation module; an internal temperature estimation module; and an air filtration state estimation module. 8. The apparatus of claim 1, wherein the power consumption determining means is operable to identify a condition selected from the group consisting of: a door in the area where the HVAC unit is located is open, a window in the area where the HVAC unit is located is open; Occupancy changes in the area where the HVAC unit is located. 9. The apparatus of claim 8, further comprising a long-term acquisition and analysis module for acquiring information describing at least one behavior of the HVAC unit, wherein the condition is identified based on a deviation from the at least one behavior. 10. The device of claim 1, further comprising a display for displaying at least one of: power consumption, cost of power consumption, information based on power consumption; current or target temperature measured by an external sensor or humidity; current or target temperature or humidity as measured by a sensor that is part of the HVAC unit; an estimate of the time required to cool to the target temperature; an estimate of the energy required to cool to the target temperature. 11. The apparatus of claim 1 , wherein the power consumption determining component comprises, or is in communication with, a motor on/off determination module for determining a power on/off of the HVAC unit. Whether the motor is on or off at a given time. 12. The apparatus of claim 11, wherein the motor of the HVAC unit is determined to be on when the measured temperature or humidity is changed, and otherwise the motor of the HVAC unit is determined to be off. 13. The apparatus of claim 1, wherein the power consumption determining component comprises or is in communication with a motor speed estimation module for estimating the speed of a motor of the HVAC unit. 14. The apparatus of claim 13, wherein the motor speed is estimated based on deriving measured temperature or humidity with respect to time. 15. The apparatus of claim 1, wherein the power consumption determining component comprises, or is in communication with, a power derivative estimation module for determining a target temperature or humidity of an environment of the HVAC unit Increase or decrease the power consumption required by one unit. 16. The apparatus of claim 1, wherein the power consumption determining component comprises or is in communication with an internal temperature estimation module for evaluating the internal temperature of the HVAC unit. 17. The apparatus of claim 1, wherein at least one sensor is selected from the group consisting of a temperature sensor and a humidity sensor. 18. The device of claim 1, wherein the information receiving module is configured to receive information from at least one source selected from the group consisting of: weather information module, user action transmitter; occupancy rate Sensors and sensors for sensing whether a door or window is open. 19. An apparatus having a processing unit and storage means, comprising: an information receiving module configured to receive information from at least one source, at least one source selected from a combination of a temperature sensor and a humidity sensor; a long-term acquisition and analysis module for acquiring information describing at least one behavior of a heating, ventilation and air conditioning (HVAC) unit; and power consumption determining means for evaluating power consumption of the HVAC unit based on information received from at least one sensor and based on at least one behavior of the HVAC unit, wherein the power consumption determining means comprises at least one module selected from the group consisting of: Motor on/off determination module; motor speed estimation module; power derivative estimation module. 20. A method comprising: receiving information from at least one sensor, directly and indirectly; and Power consumption of a heating ventilation air conditioning (HVAC) unit is evaluated indirectly by a processor using information received from at least one sensor. 21. The method of claim 20, further comprising: obtaining information describing at least one long-term behavior of the HVAC unit; Wherein, evaluating the power consumption of the HVAC unit is also based on long-term behavior and includes determining at least one aspect selected from the group consisting of: motor on/off, motor speed; power derivative; fan speed; internal temperature; and air filtration status . 22. A device comprising: A processor configured to perform the following steps: receiving a set of physical measurements from a physical location affected by a heating, ventilation and air conditioning (HVAC) unit over a period of time; fitting a set of physical measurements to a predetermined curve using parametric fitting, wherein the predetermined curve has a horizontal asymptotic line, wherein the predetermined curve has a slope that gradually decreases over time; and The target measurement for the HVAC unit is compared to the position of the horizontal asymptote to determine if the HVAC unit is expected to reach the target measurement. 23. The device of claim 22, wherein the device includes a display, wherein the processor is further configured to provide an output to the user on the display based on determining whether the HVAC unit is expected to achieve the target measurement. 24. A device comprising: A processor configured to perform the following steps: receiving a set of physical measurements from a physical location affected by a heating, ventilation and air conditioning (HVAC) unit over a period of time; fitting a set of physical measurements to a predetermined curve using parametric fitting, wherein the predetermined curve has a horizontal asymptotic line, wherein the predetermined curve has a slope that gradually decreases over time; and The time period measured until the target measurement value is reached at the physical location is estimated based on the fitted curve. 25. The device of claim 24, wherein the device includes a display, wherein the processor is further configured to display the time period to a user on the display.