WO2025014886A2 - Appareil et procédé de surveillance et de suivi d'efficacité de chauffage, de refroidissement et de rétention de chaleur - Google Patents

Appareil et procédé de surveillance et de suivi d'efficacité de chauffage, de refroidissement et de rétention de chaleur Download PDF

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WO2025014886A2
WO2025014886A2 PCT/US2024/037073 US2024037073W WO2025014886A2 WO 2025014886 A2 WO2025014886 A2 WO 2025014886A2 US 2024037073 W US2024037073 W US 2024037073W WO 2025014886 A2 WO2025014886 A2 WO 2025014886A2
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temperature
hvac
cycles
category
change
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WO2025014886A3 (fr
Inventor
Darryl Harlan NGAI
Charles Gritton
Wilmer Jacob ZWIETERING
Ali Taha
Daniel Simpkins
Edwin Laurence BOOTH
Georgios KARANTONIS
Alec MISHKIN
Hank STOCKER
John Christopher KAUFFMAN
Thad SCHEER
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Dwellwell Analytics Inc
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Dwellwell Analytics Inc
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Publication of WO2025014886A3 publication Critical patent/WO2025014886A3/fr
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Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/32—Responding to malfunctions or emergencies
    • F24F11/38—Failure diagnosis
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
    • F24F11/49—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring ensuring correct operation, e.g. by trial operation or configuration checks
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/50—Control or safety arrangements characterised by user interfaces or communication
    • F24F11/52—Indication arrangements, e.g. displays
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F2110/00—Control inputs relating to air properties
    • F24F2110/10—Temperature

Definitions

  • a sensor node detects intervals during which an HVAC system is active (e.g. using sound and/or electrical transients). The node determines the time of the active interval (such as a heating period or a cooling period) and a change in temperature and/or another environmental parameter during the active interval.
  • a histogram is generated in which a plurality of active intervals collected over a time period (e.g. over the course of a month or another time period) are binned based on the ratio of time to temperature change.
  • An efficiency metric of the HVAC system is determined by fitting a predetermined curve type (e.g. "inverse gaussian") to the histogram. Changes to the efficiency metric may trigger alerts of a potential HVAC fault.
  • Example embodiments include an apparatus and a method for monitoring and reporting on HVAC performance.
  • an HVAC system is monitored to detect a plurality of sample intervals during which the HVAC system is active.
  • the plurality of sample intervals may be, for example, a plurality of heating intervals or a plurality of cooling intervals.
  • An ambient temperature is monitored to determine a temperature change over each of the sample intervals.
  • a sample value is determined for each of the sample intervals, each sample value representing a ratio or other metric between a duration of the interval and the change in temperature (or other environmental parameter) over the course of the respective period.
  • Statistics of the sample values are evaluated and may be used to identify changes in HVAC performance.
  • Example embodiments provide an ability to discriminate between heating and cooling cycles. Differentiation between heating and cooling cycles may be made in some embodiments without direct communication with the HVAC system. Some embodiments operate to compare between different HVAC systems within one house. Some embodiments operate to compare HVAC systems among different houses. Some embodiments operate to normalize the HVAC slopes for heating/cooling in different rooms for a comparable value.
  • Some embodiments operate to normalize the temperature retention slopes for heating/cooling in different rooms for a comparable value. Some embodiments operate to compare temperature retention among different rooms. Some embodiments operate to compare temperature retention among different houses. Some embodiments operate to plot temperature retention as a function of outdoor temperature. Some embodiments use the relation between temperature retention and outdoor temperature to compare over time to see changes in insulation. Some embodiments use the relation between temperature retention and outdoor temperature to compare between rooms. Some embodiments use the relation between temperature retention and outdoor temperature to compare between units of apartments/houses. Some embodiments operate to determine and/or plot HVAC effectiveness as a function of outdoor temperature.
  • a device 100 includes a housing 108 having a rear surface 104.
  • a set of power plug prongs 106 extends from the rear surface of the housing.
  • the illustrated prongs are those compatible with standard North American outlets, other configurations may alternatively be used.
  • FIG.2 is a graph providing a schematic example of a dirt loading curve for a filter.
  • FIG.3 schematically illustrates a system in which heat Q flows between regions of different temperatures.
  • FIG.4 is a graph schematically illustrating the performance of an example advanced multi-mode heat pump.
  • FIG.5 provides example histograms of collections of sample values, which may be used as indicators of heating and/or cooling effectiveness.
  • FIG.6 illustrates an example user interface providing a notification of a level of air filter cleanliness.
  • FIG.7 is a histogram schematically representing the time divided by temperature change of HVAC cycles from a single sensor node, along with a curve fit to the histogram data.
  • FIGs.8A-8B are histograms schematically representing data collected from two sensor nodes in the same home.
  • FIG.9 illustrates an example user interface providing a report of collected data and results of data analysis.
  • FIG.10 illustrates an example user interface providing statistical information on the detected operating cycles of an HVAC system.
  • FIGs.11-13 illustrate examples of user interface elements providing different types of reports of heating effectiveness information.
  • FIGs.14-16 illustrate examples of user interface elements providing different types of reports of cooling effectiveness information.
  • FIG.17 illustrates an example of a user interface element in which temperature retention information is provided in some embodiments.
  • FIG.18A illustrates an example of a user interface element in which temperature retention information is provided in some embodiments.
  • FIG.18B illustrates a graphical display element used to display results in the example of FIG.18A.
  • FIG.19 illustrates a graphical display element used to display room-by-room temperature retention information in some embodiments.
  • FIG.20 illustrates a graphical display element used to display temperature retention statistics in some embodiments.
  • FIGs.21-22 illustrate graphical display elements used to display temperature swing information in some embodiments.
  • FIG.23 schematically illustrates network topology used in some embodiments.
  • FIG.24 is a schematic functional block diagram illustrating a sensor node used in some embodiments.
  • FIG.25 is a schematic functional block diagram illustrating the functional architecture of a monitoring system according to some embodiments.
  • FIG.26 is a schematic functional block diagram illustrating the functional architecture of a monitoring system according to some embodiments.
  • FIG.27A is a graph illustrating a histogram of data collected in different rooms, each with a corresponding inverse Gaussian curve fit.
  • FIG.27B is a graph illustrating the data of FIG.27A, wherein the data from one of the rooms has been scaled.
  • FIG.28 illustrates an example report of historical cooling performance according to some embodiments.
  • FIG.29 illustrates an example report of historical heating performance according to some embodiments.
  • FIG.30 illustrates examples of reports of heating and cooling performance according to some embodiments.
  • FIG.31 illustrates an example of a report of insulation performance according to some embodiments.
  • FIG.32 is a graph illustrating example temperature retention data and a corresponding slope according to some embodiments.
  • FIG.33 is a graph illustrating the slope of the best fit line for the temperature retention rate (TRR) vs temperature as a function of thermal conductivity of the wall in simulated data.
  • FIG.34 is a graph of air filter dirtiness percentage versus HVAC hours of operation that may be used in some embodiments.
  • DETAILED DESCRIPTION As illustrated in FIGs.1A-1F, a device 100 includes a housing 108 having a rear surface 104. A set of power plug prongs 106 extends from the rear surface of the housing.
  • an HVAC system e.g. of a residence
  • An ambient temperature is measured (e.g. by one or more sensor nodes disposed in the residence) to determine a change in temperature or other environmental parameter over each of the respective sample intervals.
  • a sample value is determined for each of the sample intervals, each sample value representing a ratio between the duration of the interval and the temperature change over the course of the respective period.
  • the sample value may represent the duration divided by the temperature change.
  • the sample value may represent the temperature change divided by the duration.
  • the sample value may represent the slope of the change in temperature (or other environmental parameter) over time at or near the beginning of the interval.
  • an environmental parameter other than temperature is used, following any of the techniques described herein.
  • a parameter representing humidity, comfort index, CO 2 level, or air quality may be tracked, and the effectiveness (or change in effectiveness) of an HVAC system may be monitored based on a collection of sample values, each sample value representing a rate of change in the relevant parameter during a respective active interval of the HVAC system.
  • the value representing the rate of change may be, for example a slope of parameter change over unit time, or an inverse slope of time over unit parameter change.
  • a change in HVAC performance may be detected based on a comparison between sample values collected in a first time period (e.g. a prior month or year) and sample values collected in a second time period (e.g. a current month or year).
  • a system may be referred to as an HVAC system even if the system, for example, has only a cooling component or only a heating component, including systems such as PTAC, other portable air conditioners, and radiator systems.
  • Some embodiments include generating a histogram of the sample values and displaying the histogram to a user. Some embodiments include determining and displaying to a user at least one of the following: a mode, a median, an average, an upper percentile (e.g. upper quintile), or lower percentile (e.g.
  • the method includes, for each of a plurality of bins, each bin being associated with a range of sample values, determining a number of the sample values that fall within the associated range to generate histogram data; and fitting a curve to the histogram data.
  • the curve may be an inverse Gaussian (or Wald distribution).
  • Some embodiments include detecting a change in HVAC performance by detecting a change between (i) at least one first parameter of a first curve fitted to a first histogram of sample values collected in a first time period and (ii) at least one second parameter of a second curve fitted to a second histogram of sample values collected in a second time period.
  • the sample intervals during which the HVAC system is active comprise sample intervals during which the HVAC system is heating. In some embodiments, the sample intervals during which the HVAC system is active comprise sample intervals during which the HVAC system is cooling. In some embodiments, both heating and cooling sample intervals are collected, and they may be processed separately. [0048] In some embodiments, each sample interval is a single “on” cycle extending from a time the HVAC system becomes active to a time it becomes inactive. In some embodiments, each sample interval is an interval of a predetermined duration. [0049] The collection of data as described here regarding active periods of the HVAC system may be used for providing various assessments of HVAC effectiveness as described in greater detail below.
  • some embodiments collect data regarding inactive periods of the HVAC system, which may by used in determining the heat retention (e.g. insulation) and/or heat exclusion (e.g. shading) performance of the residence.
  • an HVAC system is monitored to detect a plurality of sample intervals during which the HVAC system is inactive.
  • An ambient temperature is monitored to determine a temperature change over each of the first and second plurality of sample intervals.
  • a sample value is determined for each of the sample intervals, each sample value representing a ratio between a duration of the interval and the temperature change over the course of the respective period.
  • the sample value may represent the duration divided by the temperature change. In other embodiments, the sample value may represent the temperature change divided by the duration.
  • the sample value may represent a rate of temperature change at a particular time period during the inactive interval, such as at or the start of the interval (e.g., shortly after the HVAC system has become inactive).
  • a change in heat retention or exclusion performance may be detected based on a comparison between sample values collected in a first time period and sample values collected in a second time period. Such changes may be detected or characterized using statistical methods, including curve fitting, as summarized above (with respect to the “active” periods and as described in further detail below.
  • a user is alerted to potential HVAC or heat retention issues in response to a determination that a value (such as mean, median, mode, or other value) derived from the sample values has changed by at least a threshold amount.
  • the threshold may be, for example, a predetermined threshold, a user-defined threshold, or a contextually derived threshold.
  • sample values are processed for display using techniques that allow a user to readily identify changes or potential issues with HVAC or heat retention performance, for example showing comparisons of such processed data between different time periods, between different rooms (or different sensor nodes), or between dwellings that are expected to have comparable results.
  • data presentation techniques are adjusted based on different levels of certainty regarding HVAC performance to provide users with useful information without conveying a misleading level of certainty.
  • sample intervals in which the HVAC system is moving air are determined, and the total duration of such intervals is used to provide information as to an estimated remaining useful life of an HVAC filter. In some embodiments, the total duration of such intervals is used to provide information as to an estimated time to service or estimated time to failure of the HVAC system.
  • Some embodiments operate to determine temperature changes and intervals during which the HVAC system is heating, cooling, or ventilating without any direct communication with any component of the HVAC system itself. For example, one or more sensor nodes may be provided in a dwelling.
  • each sensor node may include a temperature sensor, such as a thermocouple or other type of thermometer, for determining temperature changes.
  • each sensor node further includes a microphone, and the determination of times during which the HVAC system is heating, cooling, or ventilating is based at least in part on audio data collected using the microphone of one or more sensor nodes.
  • HVAC Air Filter “Dirtiness” Metric Based on Operational Time Some embodiments include the implementation of a remote monitoring system to measure HVAC air filter dirtiness over time based just on operational time. The input from the hub device or for this method may be the times when the HVAC system (or fan) is running.
  • HVAC Operational Time calculation is made of how dirty the air filter is by using the HVAC Operational Time. This may be implemented separately and distinctly from the methods of estimating HVAC Air Filter Dirtiness using, for example, trend analysis on HVAC Run Rate or direct classification of the acoustic signature of air ventilation sounds. [0059] The following terms are used in the present disclosure: Term Description HVAC O eration The len th of time the HVAC air handler has been on since the air filter was last of [0060] The air filter gradually picks up dust as air flows through it. Typically, HVAC air filters may be depth loading filters that capture particles throughout the depth of the media. Such filters may have a loading profile approximated by an exponential curve.
  • Example embodiments may use one or more of the following assumptions: ⁇ Assume a linear rate of dust accumulation on the filter as a function of HVAC Operational Time. This makes sense since the dust is distributed in the air and more dust will therefore be collected when there is air flow than when there is not. Assuming a constant average level of dust in the air and a constant average air flow throughout the life of the filter is reasonable. This is a slow metric operating over months and so short-term variations from the average in either dust concentration or air flow are not especially relevant. ⁇ Assume that the filter is a panel type that matches the exponential curve fit given above. ⁇ Assume that the recommended air filter change period of 3 months corresponds to the typical recommended operational time of 3 times per hour for 15-20 minutes at a time on average.
  • An average air conditioner runtime per day may be estimated as 8.9 hours.
  • An average furnace operating time in the winter may be about 5.8 hours per day.
  • a conservative estimate here may be 3 months, not 4 months for the base and also assuming the HVAC operational time that the filter manufacturers used was on the shorter end of normal.
  • dirtiness (e (.0027 * xcurrent) – 1)*100% .
  • UX user experience
  • xcurrent may be adjusted upward by a (multiplicative) factor if the homeowner has pets (more cats and dogs rather than fish) or a larger family. This reflects the fact that there will likely be more dust in the air to collect and the air filter then needs to be changed more frequently. The factor may be 1.5 if the homeowner has one cat or dog and 2 if they have more than one cat or dog.
  • the estimate of dust buildup on the air filter may be adjusted based on humidity measurements.
  • HVAC Efficiency, Effective Outdoor Temperature various thermal model elements are combined to get a single effective outdoor temperature.
  • the various outdoor temperatures may be combined into a single effective temperature T eff
  • the heat conductance through various different components of a dwelling may be combined into a single effective heat conductance, k eff .
  • HVAC Effectiveness/Efficiency Method for User Interface Some embodiments provide a detailed HVAC monitoring method that is based on a number of factors so that to provide useful insights into HVAC operation.
  • Example embodiments may operate to calculate one or more of the following parameters.
  • Such parameters may be delivered to a user for display.
  • Vsymbol Parameter Description or T-Cool-Aveweek Average Cooling Run The average (median) time the HVAC system ran over the past T ime Over Week week to reduce the internal temperature by 1 degree C . %
  • the performance of some HVAC equipment varies with the outdoor and sometimes indoor temperatures. The relevant details are below.
  • EER maximum energy efficiency ratio
  • FIG.4 schematically illustrates the performance of an example advanced multi-mode heat pump.
  • the heat output within each mode decreases with outdoor temperature.
  • the appropriate mode is selected in an attempt to compensate for the heat lost from the home (illustrated with a dashed line), with the outdoor temperature triggering the change between modes.
  • the vertical arrows represent transitions between modes as the outdoor temperature falls. While most heat pumps are significantly simpler than this one, the main point remains – the heat output decreases with outdoor temperature.
  • Heat pumps may include a mode transition to electrical “emergency” heat when the temperature gets to a low temperature (generally around 30-40 degrees F.
  • Example embodiments may operate on the assumption that measured HVAC efficiency for heating will depend on the outside air temperature.
  • Example embodiments may use one or more of the following parameters. Symbol Parameter Description r HRRh HVAC Run Rate for The rate at which the HVAC system is heating the indoor H eating temperature of the room the sensor is in. Units are o C/sec e TRLweek TRL values restricted to t he current week The list of kco values for the current week. l) e. is s; s [0082]
  • a simplified thermal model for the home that may be used in some embodiments is given as follows. This example model ignores other factors like sunlight, wind and room to room heat transfer within the house.
  • these will be the values at the start of the cooling cycle.
  • the sensor node On receipt of a new HRR h for a given sensor node, do the following.
  • the sensor node will supply T e for the calculations below. In some embodiments, these will be the values at the start of the heating cycle.
  • HRR c ⁇ ref [cb] min ( HRR c ⁇ week [cb].P10, HRR c ⁇ ref [cb] ) if HRR c ⁇ week [cb].count > MIN_BIN_COUNT)
  • HRR h ⁇ ref [hb] min ( HRR h ⁇ week [hb].P10, HRR h ⁇ ref [hb] ) if HRR h ⁇ week [hb].count > MIN_BIN_COUNT) [0090]
  • some embodiments evaluate the minimum value because the kco term is multiplied by the difference between interior and exterior temperatures to yield the rate of change in degrees Celsius per unit time.
  • Normalized parameters may be determined as follows.
  • some embodiments operate to graphically illustrate HVAC Effectiveness by plotting HRR c and HRR h for a recent period of time.
  • HVAC performance varies with weather and not just its maintenance condition. That’s why the calculations of HE c and HE h are normalized to bins that factor in weather conditions. This plot does not directly leverage bins and so will change with weather conditions. However, since we seek to minimize that effect, it may be desirable to pick an appropriate window of time. This is a design parameter that can be changed, but in some embodiments, it is to set it to 1 month, which is long enough to capture a broad collection of bins but short enough that seasonal variations can be seen.
  • the nominally 1 month period mentioned below may be a sliding window of time. This means that what we are plotting is the current day’s results plus the prior 30 days. This is then a sliding month, not a calendar month.
  • HVAC system only heats (e.g., winter) or only cools (e.g., summer). In that event, only one of the two possible curves will be shown because there is no data for the other.
  • HVAC systems average just shy of 9 hours a day but generally 10-15 minutes at a time. This will vary for a particular house but suggests that the default x-axis range should be roughly 1 to 30 minutes to a resolution of .1 minute.
  • auxiliary plot data may include defining a function such as PERCENT_FIND(x, p), where: ⁇ x is the list of counts from index 0 at the low end and some finite number M at the high end so the array contains M+1 entries.
  • x will be either HRRc-plot and HRRh-plot .
  • statistics obtained from the main plot data are plotted.
  • HRR c ⁇ plot . P90 PERCENT_FIND(HRR c ⁇ plot , 90) HRR c ⁇ plot .
  • P50 PERCENT_FIND(HRR c ⁇ plot , 50) HRR c ⁇ plot .
  • P10 PERCENT_FIND(HRR c ⁇ plot , 10) [0102]
  • P90 PERCENT_FIND(HRR h ⁇ plot , 90) HRR h ⁇ plot .
  • P50 PERCENT_FIND(HRR h ⁇ plot , 50) HRR h ⁇ plot .
  • P10 PERCENT_FIND(HRR h ⁇ plot , 10) [0103]
  • the method may involve plotting these three values by day or week for the period chosen. Each single data point represents a calculation for the past window of time (here recommended to be 1 month long). So the plot shows how the statistics of the HVAC Effectiveness plots changes over time. [0104] In some embodiments, statistics across bins are plotted. [0105] The HRR’s are normalized by bins and so shifts within a bin should correlate to changes in the system’s operational efficiency.
  • the 90%, 50% (median) and 10% points may be plotted per bin against the temperature values the bins correspond to (e.g., the outside temperature is the x-axis for the heating case) for cooling and heating respectively.
  • the formulas to calculate the relevant stats on HRR in some embodiments are given below.
  • Some embodiments provide for a simplified analytical model. Powerful insights can be had by, in this case, letting collections over longer periods of time average out the factors which may otherwise be considered.
  • Modeling HVAC and home thermal behavior in general is complicated. Example embodiments operate to determine relevant parameters via remote sensing using a collection of sensor nodes in the home (e.g. using audio data collected by sensor nodes for determining HVAC activity). Such embodiments provide useful and actionable insights for those who own and/or manage a home.
  • While the example is described here of an HVAC system cooling a house, the description also applies to cases where the HVAC system is heating the house, only the direction of temperature change is different.
  • analysis is performed based on two types of observations, collected over a relatively long period of time: ⁇ When the HVAC system is running, how long it takes to reduce the room temperature by 1° C. The symbol OnTime may represent this value. ⁇ When the HVAC system is not running, how long it takes for the room temperature to rise by 1° C. The symbol OffTime may represent this value. [0110] In some embodiments, for each HVAC “On” cycle for each sensor node of interest, a new OnTime is calculated. Similarly, for each HVAC “Off” cycle for each sensor node of interest, a new OffTime is calculated. Then, if one plots the distribution (e.g.
  • example embodiments operate to compare the distributions of two time-periods with one another. For example, some embodiments compare the current year’s OnTime distribution with the prior year’s OnTime distribution, and if the HVAC system is degrading, the distribution may indicate a shift towards longer times. Some embodiments operate to detect such a shift and to alert a user to potential changes to the effectiveness of the HVAC system.
  • some of the peak (or mode), median, mean, lower quintile and upper quintile values may be greater than before. If those values are the same, that indicates the HVAC system is running at substantially the same efficiency level as before. [0112]
  • the situation with OffTime is similar.
  • Example embodiments operate to compare the current year’s OffTime distribution with the prior year’s OffTime distribution and if the home’s thermal retention is worse, the distribution may indicate a shift towards shorter times. Some embodiments operate to detect such a shift and to alert a user to potential changes to the effectiveness of the home’s heat retention (e.g. insulation) or exclusion (e.g. shading).
  • some of the peak, median, lower quintile and upper quintile values may be less than before.
  • Some embodiments operate to compare the OnTime distribution for one sensor node with another in the same house. A determination may be made that the one with longer times is not as well controlled. This could indicate that, due to duct design or vent position, the air flow is not as strong in the poorer performing room. It could mean that the thermal retention in that room is lower That in turn could be due to poorer insulation, an open window or the like. Or it could mean another heat source is dragging down performance such as solar radiation. One example is if the poorer performing room has many windows and gets lots of sunlight. [0114] Some embodiments operate to compare the OffTime distribution for one sensor node with another in the same house.
  • a comparison is performed of distributions from similar homes in the same geographic area. A determination may be made of how well a particular home/room is performing relative to neighboring homes.
  • a comparison is performed of distributions from different HVAC units in a single house or different HVAC units in different units of a multi-dwelling building such as an apartment building. Other comparisons may alternatively be made.
  • a comparison of two OnTime distributions is particularly useful when the distributions of correlated factors are roughly equivalent. For example, OnTime is heavily influenced by weather conditions.
  • Some embodiments fit a histogram of HVAC Efficiency Ratios to a curve, such as an Inverse Gaussian curve.
  • a curve such as an Inverse Gaussian curve.
  • the resulting data may be presented in a more easily interpretable form for monitoring the performance of the system. For example, there is a closed form equation for both the mode and the mean of that curve.
  • the fit distribution instead of the raw histogram bins, example embodiments are more robust to statistical variations and measurement noise.
  • the curve is fit to the data with a low number of variables, e.g. two variables for shape and one for height normalization, where the variables provide information regarding the HVAC efficiency.
  • Example embodiments use artificial intelligence (AI) classifiers to determine when the HVAC is on and off. For example, audio data collected by sensor nodes may be converted into a spectrogram that in turn is provided to a convolutional neural network (CNN) trained to discriminate between HVAC active and inactive states. In some embodiments, additional information collected by additional sensors is used.
  • AI artificial intelligence
  • Such additional sensors may be in the same sensor node.
  • Such additional sensor information may include electrical transient signals detected at a power outlet, changes to temperature, humidity, or detected levels of different gases (e.g. CO, CO 2 , or volatile organic compounds) that can be used as an alternative to or in addition to audio signals to provide greater levels of certainty as to the activity, inactivity, and type of activity (e.g. heating, cooling, or ventilating) of the HVAC system.
  • Information from exterior sources may also be employed. For example, external temperature values may be determined (detected through an outdoor sensor or retrieved via online weather resources), with high outdoor temperatures increasing a prior probability of HVAC cooling activity and lower outdoor temperatures increasing a prior probability of HVAC heating activity. This information is used to help determine the efficiency of heating/cooling of the HVAC.
  • data is further collected from a thermostat. Such data may indicate whether the thermostat has requested activation of the HVAC system. Thermostat data may be used to augment a determination made using other sensor data (e.g. audio data) to determine whether the HVAC system is active. Furthermore, a potential failure state may be detected if the thermostat has requested activation of the HVAC system but other sensor data (e.g. audio data and/or temperature data) indicates that the HVAC system has not been activated. [0123] In some embodiments, for each HVAC “on” cycle, the total duration and temperature change are measured. The value of time divided by temperature change is determined, where a higher value means that it is less efficient as it takes longer for the HVAC to change the temperature of the room by a set amount.
  • FIG.7 illustrates a histogram showing the time divided by temperature change of HVAC cycles from a single sensor node. Investigations have shown that the overlay with an inverse Gaussian distribution fits the data well. The observation that such an overlay fits well for this histogram opens the door to some embodiments as disclosed herein. [0125] The observation that the inverse Gaussian distribution (or “Wald distribution”) fits the histogram well indicates that the heating/cooling of the room is well modelled as heating/cooling from the HVAC plus environmental heating/cooling, where the environmental term is random and has a normal distribution. In general, the mean is proportional to the size of the room and the heat flow rate from the HVAC and is substantially independent of the environmental heating/cooling.
  • the environmental heating/cooling only affects the variance of the distribution (width of the distribution).
  • one sensor node is chosen for each HVAC/PTAC zone. The choice may be made through the input of a user, an installation professional, or it may be made automatically.
  • data is taken from multiple sensor nodes to describe the HVAC efficiency of the full house.
  • FIGs.8A-8B illustrate histograms of data collected from two sensor nodes in the same home. As seen in FIG.8A, Sensor 1 has a lower distribution mean than Sensor 2. Multiplying each value of the time/degree Celsius for Sensor 2 by a factor gives the rescaled results of FIG.8B. The rescaled distributions of the two curves in FIG.8B match up quite closely.
  • rescaling is performed to combine data from different sensor nodes in a residence to provide additional analytical results.
  • rescaled histogram data from different sensor nodes can be pooled together into a single combined histogram, and changes to the statistics of the combined histogram may be used to detect potential HVAC issues.
  • Q is the heat from a source
  • C is the heat capacitance
  • ⁇ T is the change in temperature.
  • the dQ e /dt term it is generally about 0 because we are typically in a steady state situation in the time scale of a HVAC cycle, resulting in the derivative of 0.
  • the temperature of the room is relatively stable over the approximately 10 minute time frame, which is the typical time scale of an HVAC cycle.
  • r ⁇ is a scaling factor and Wt is described by the Wiener process.
  • the Wiener process is the continuous version of the random walk. What is left is: for the temperature to change by 1°C.
  • methods are implemented to adjust some of the estimated values in order to optimize the quality of our diagnostic data shown to a user.
  • One element in some of the HVAC data calculations and visualizations analyzed below involves some inverses of derivatives (“ratios”) of particular segments of the temperature time series capturing HVAC operational cycles. This group of parameters includes HER heat , HER cool , TRR heat , and TRR cool . Their units may all be minutes per ° C, and in example embodiments they range in value from 0 to 60. Described herein are performance metrics for such parameters.
  • For the Air Filter Cleanliness method some embodiments gauge the quality of the aggregate HVAC Operational Time estimates produced by an AI system over the course of the average day. A performance metric - the Operational Time Error Bound – is described in the table below.
  • FIG.9 illustrates an example report of collected data and results of data analysis, which may be displayed in a user interface in some embodiments.
  • the temperature reports may be direct readings and not inferences, in which case no adjustment is required.
  • the gray vertical bands on the chart reflect the result of inferences by the system as to intervals during which the HVAC is running. Depending on the AI performance levels, adjustments may be implemented.
  • Some embodiments provide data such as that shown in FIG.10. Such data may be displayed in a user interface.
  • the following metrics may be applied to the HVAC On/Off determination along with associated timing. For this table, the focus is on the augmented state of when HVAC is either cooling or heating, rather than if the HVAC air handler is running without heating or cooling.
  • the measure “IoU” refers to “intersection over union,” measuring the amount of overlap (intersection) between the predicted state from the classifier and the true state divided by the union of those two (for a particular state). Other measures of performance may alternatively or additionally be used.
  • a user interface is provided as shown in FIG.11.
  • the following metrics apply to HVAC On/Off determination along with associated timing. For this chart, the focus is on the augmented state of when HVAC is heating, rather than if the HVAC air handler is running without heating.
  • Ratio Error Bound for HERheat ⁇ 20% across the range consider two possible actions: o Remove the Min and Max displays on the chart and just leave the Average (Median) and/or o Check if Ratio Error Bound is better at one end of the range than the other. In that event, consider adjusting the region of operation so that the Ratio Error Bound is ⁇ 10%. ⁇ If the Ratio Error Bound for HER heat > 20%, suggest raising the required N HER to a higher number (e.g., 200) before displaying the chart at all. Then only show the Average (Median) number.
  • a higher number e.g. 200
  • heating effectiveness is displayed using a chart as shown in FIG.12.
  • the cycles that are analyzed to compute HERheat ratios are, in general, a subset of the total detected HVAC cycles.
  • the method for computing the HERheat ratios operates to reject that cycle as being unsuitable (too noisy or too weak) to compute the ratios.
  • the following adjustments may be made to the interface: ⁇ Change “Total runtime cycles” to “Total cycles analyzed” ⁇ Change the vertical axis label to, e.g., “Number of cycles analyzed”. [0157]
  • NHER the number of samples in the collection being analyzed for the displayed Heating Effectiveness
  • the chart should be used as is.
  • further actions may be used: o Do not display the extremes on either end - the bottom and top 5% o Shade the curve to illustrate the error bounds. o Make the tick marks very coarse or do not label them at all so as to convey only the shape of the distribution and not precise values. o If the Ratio Error Bound for HER heat > 30%, consider not showing the graph at all. [0158] In some embodiments, a chart such as that of FIG.13 is provided in a user interface. This screen plots the average (median) HER heat over the period of interest. [0159] In some embodiments, cooling effectiveness is provided in a user interface as illustrated in FIG.14. [0160] The following metrics apply to HVAC On/Off determination along with timing associated with that.
  • This chart focuses on the augmented state of when HVAC is cooling, rather than when the HVAC air handler is running without cooling.
  • the use of median and other percentile operations provides some built-in protection.
  • the following are implemented: ⁇ For normal operation of the chart, N HER (the number of samples in the collection being analyzed for the displayed Cooling Effectiveness) should be > 50. This allows for the 5% level to accommodate at least 1 outlier on each end. ⁇ If the Ratio Error Bound for HER cool ⁇ 10%, the chart should be used as is.
  • Ratio Error Bound for HERcool 20% across the range, consider two possible actions: o Remove the Min and Max displays on the chart and just leave the Average (Median) and/or o Check if Ratio Error Bound is better at one end of the range than the other. In that event, consider adjusting the region of operation so that the Ratio Error Bound is ⁇ 10%. ⁇ If the Ratio Error Bound for HERcool > 20%, suggest raising the required NHER to a higher number (say, 200) before displaying the chart at all. Then only show the Average (Median) number.
  • cooling effectiveness information is provided in a user interface as shown in FIG.15.
  • the cycles that are analyzed to compute HER cool ratios are, in general, a subset of the total detected HVAC cycles.
  • the method for computing the HER cool ratios may operate to reject that cycle as being unsuitable (too noisy or too weak) to compute the ratios.
  • the following adjustments to this UX screen may be implemented in some embodiments: ⁇ Change “Total runtime cycles” to “Total cycles analyzed” or something to that effect. ⁇ Change the vertical axis label to “Number of cycles analyzed”. [0163]
  • NHER the number of samples in the collection being analyzed for the displayed Cooling Effectiveness
  • the chart may be used as is.
  • Ratio Error Bound for HER cool > 20% across the range If 10% ⁇ Ratio Error Bound for HER cool ⁇ 20% across the range, consider three possible actions: o Shading the curve to illustrate error bounds around the main curve o Check if Ratio Error Bound is better at one end of the range than the other. In that event, consider adjusting the x-axis of the chart so that the Ratio Error Bound is ⁇ 10% across the chart. o Adjusting the header/title text to make it clear that only the rough shape is accurate. ⁇ If the Ratio Error Bound for HER cool > 20%, suggest raising the required N HER to a higher number (say, 200) before displaying the chart at all.
  • cooling effectiveness trend data may be provided in a user interface using a chart such as that of FIG.16. This screen plots the average (or the median in some embodiments) HERcool over the period of interest.
  • temperature retention data may be provided in a user interface using a chart such as that of FIG.17.
  • NTRR the number of samples in the collection being analyzed for the displayed Temperature Retention
  • NTRR the number of samples in the collection being analyzed for the displayed Temperature Retention
  • ⁇ the Ratio Error Bound for TRRcool (or TRRheat ) ⁇ 10%
  • the chart should be used as is.
  • 10% Ratio Error Bound for TRR cool (or TRR heat ) ⁇ 20% across the range
  • o Remove the Min displays on the chart and just leave the Average (Median) and/or o Check if Ratio Error Bound is better at one end of the range than the other.
  • temperature retention information is provided in a user interface as shown in FIG.18A.
  • the slope and offset of the mean/median/mode or other value may be compared between different time periods, different rooms, and/or different dwellings to compare temperature retention.
  • the cycles that are analyzed to compute TRR cool and TRR heat ratios are, in general, a subset of the total detected HVAC cycles.
  • the algorithm computing the TRR cool and TRR heat ratios may operate to reject that cycle as being unsuitable (too noisy or too weak) to compute the ratios.
  • N TRR the number of samples in the collection being analyzed for a particular box-and-whiskers element
  • N TRR the number of samples in the collection being analyzed for a particular box-and-whiskers element
  • ⁇ the whiskers portion of the display may be omitted for that element and just display the main box section for that slice of outdoor temperatures. If NTRR ⁇ 15, leave the box blank. Note that this determination may be done separately for every potential box and whiskers element. Therefore it follows that some elements will be blank, some will just have the main box section and some will be full box-and-whiskers elements.
  • Ratio Error Bound for TRR cool (and TRR heat ) ⁇ 10% the chart may be used as is.
  • ⁇ If 10% ⁇ Ratio Error Bound for TRRcool (and TRRheat ) ⁇ 20% across the range, consider three possible actions: o Leave the whiskers off entirely and consider fuzzing/widening the line width of the bottom and top of the box section. o Check if Ratio Error Bound is better at one end of the time range than the other. In that event, consider adjusting the y-axis of the chart so that the Ratio Error Bound is ⁇ 10% across the chart. o Adjusting the header/title text to make it clear that only the rough shape is accurate.
  • temperature retention statistical information is provided in a user interface as shown in FIG.20.
  • Information regarding the number of “best retention days” may be used to indicate the number of days (e.g. in the last twelve months) during which it took 60 minutes or more for a one degree change in temperature.
  • Information regarding the “total below average retention days” may be used to indicate the number of days (e.g. in the last twelve months) on which retention was below the average year-to-date retention.
  • temperature swing information is provided in a user interface as shown in FIG.21.
  • temperature swing statistics are provided in a user interface as shown in FIG. 22.
  • air filter cleanliness information is provided in a user interface as shown in FIG.6. The interfaced reflects determination of the HVAC Operational Time. In this case, the relevant time is when the Air Handler runs whether or not heating or cooling is happening. The air filter collects more dirt when the air is flowing through it, and it does not matter if the system is actively heating or cooling the house.
  • HVAC Operational Time Error Bound (25% of average daily HVAC Operational time)
  • reduce the granularity of the display e.g., reducing the number of “dots” in the air filter that indicate its dirtiness level.
  • HVAC Operational Time Error Bound > (25% of average daily HVAC Operational Time) consider two possible adjustments: o Adjust algorithm to weight calendar time more heavily than operational time in computing the level of dirtiness. o Remove the sub-heading item about changes week to week in amount of dirtiness that week. Reporting HVAC Effectiveness. [0177] Some embodiments provide information regarding HVAC effectiveness.
  • values used include HERheat[ci], HERcool[ci], TRRheat[ci], and TRRcool[ci] for each HVAC cycle ci. These values may be computed on a hub device based on data collected by one or more sensor node devices. [0178] In the case of HER values, this is an inverse slope measurement on the temperature trend measured by a given sensor node during the portion of the HVAC cycle when the system is On (either heating or cooling). The units for HER may be minutes per degree Celsius, although other units may be used in other embodiments.
  • TRR values this is an inverse slope measurement on the temperature trend measured by a given sensor node during the portion of the HVAC cycle when the system is Off (either heating or cooling).
  • the units for TRR may be minutes per degree Celsius, although other units may be used in other embodiments.
  • This following describes the calculations used in presenting data regarding HVAC effectiveness in a user interface.
  • the methods detailed below for computing HVAC Effectiveness, Heating Effectiveness, Cooling Effectiveness, Temperature Retention and Temperature Swings all have an implicit perspective. Each sensor node offers a different observation point, a different perspective, on those parameters. This is perhaps easiest to understand with temperature swings that vary from room to room because, in part, the Temperature Retention varies room to room.
  • HVAC Effectiveness also varies room to room because, in part, the venting and air flow is different in each room. Some embodiments present to the user the median of all the calculated effectiveness ratings for the home. Some embodiments present to the user the worst calculated effectiveness ratings for the home. [0182] Data used in determining HVAC effectiveness may be calculated using a method such as the following. The following steps may be repeated. ⁇ On the completion of every HVAC On Cycle, an HVAC Effectiveness Rate (HER) is calculated and stored for either heating or cooling depending on which mode the HVAC is operating. ⁇ On the completion of every HVAC Off Cycle the Thermal Retention Rate is calculated and stored for either heating or cooling depending on whether the outdoor temperature is greater than or less than the indoor temperature.
  • HER HVAC Effectiveness Rate
  • the Thermal Retention Rate may also be calculated and stored. Note that in many of those cases, the TRR will be greater than 60.
  • a threshold amount of time e.g. two hours
  • the Thermal Retention Rate may also be calculated and stored. Note that in many of those cases, the TRR will be greater than 60.
  • the table below gives descriptions for a method for calculated HER’s and TRR’s using single point temperature values at the edges of an HVAC On or Off cycle respectively.
  • a more refined estimation method of the HER’s and TRR’s may be performed.
  • the method may include taking the slope of a spline fit or using a smoothed derivative function, exponential decay function or something similar.
  • a hub device may perform the calculation along with determining whether the cycle is heating or cooling and then send those results to a cloud service for processing.
  • the hub device may report such difficulty to the cloud service.
  • the cloud service may ignore those data points in providing the user interface display.
  • an initial determination is made of whether the HVAC is in an “on” state or an “off” state based on, for example, sounds detected (or not detected) by a sensor node, and a subsequent determination is made of whether the HVAC is heating or cooling (or merely circulating air) based on a change (or lack of change) in temperature detected by the sensor node during the “on” state.
  • heating effectiveness information is provided in a user interface as shown in FIG.11. Elements used for this display are given in the following table. Item How to Calculate [0188] In some embodiments, heating effectiveness information is provided in a user interface as shown in FIG.12. This is plot of the entire list of HER heat [ci] values for the time interval of interest (e.g., last 30 days).
  • heating effectiveness information is provided in a user interface as shown in FIG.13. This is a plot of the median(HERheat[ci]) for each month over the interval of interest (e.g., the last 12 months).
  • cooling effectiveness information is provided in a user interface as shown in FIG.14. Elements used for this display are given in the following table. Item How to Calculate ° [0191] In some embodiments, cooling effectiveness information is provided in a user interface as shown in FIG.15.
  • cooling effectiveness information is provided in a user interface as shown in FIG.16. This is a plot of the median(HERcool[ci]) for each month over the interval of interest (e.g., the last 12 months). The gap in the plot is because during the winter months there were no HVAC cooling cycles.
  • temperature retention information is provided in a user interface as shown in FIG.17. Parameters used for this display are described in the following table.
  • temperature retention information is provided in a user interface as shown in FIG.18A. This is plot of the box and whiskers processing of the entire list of TRR cool [ci] and TRR heat [ci]values for the time interval of interest (e.g., last 30 days). Note that for the plot, the calculations for heating and cooling are separated into the orange and blue lines respectively. Also note that they are plotted against the outside temperature applicable for each cycle of interest. [0196] In this example there is a separate plot for Daytime and Nighttime. In the example figure above, the selection is indicated in the upper right as a Daytime plot.
  • Daytime and Nighttime are shown in different plots.
  • users tend to configure their home differently at nighttime by, for example, closing the blinds or drapes. This changes the thermal retention properties of the home.
  • sunlight can affect the apparent thermal retention during certain hours of the daytime but, of course, nighttime by definition has no sunlight.
  • the sunrise and sunset times noted in weather data (which may be retrieved over a network) is used to separate Daytime and Nighttime for the calculations. Cases where the HVAC Off duration spans the boundary between Day and Night by less than 1 hour (or some other predefined duration) may be put in the category it was in longest.
  • FIG.18B illustrates a graphical display element 1750 used to display results in some embodiments.
  • upper whisker 1752 illustrates 95th percentile value
  • upper box edge 1754 illustrates an upper quartile value
  • band 1756 illustrates a median value
  • lower box edge 1758 illustrates a lower quartile value
  • lower whisker 1760 illustrates a 5th percentile value.
  • room-by-room temperature retention information is provided in a user interface as shown in FIG.19. What is shown for each sensor node in figure is the Average (Median) Temperature Retention time for that room for the period covered in the graph of FIG.18A.
  • temperature retention statistics are provided in a user interface as shown in FIG.20.
  • the TRRcool[ci] and TRRheat[ci] are considered together, as one bundle when analyzing the stats. Losing 1 degree C in 10 minutes in the winter is bundled in with gaining 1 degree C in 10 minutes in the summer.
  • temperature swing information is provided in a user interface as shown in FIG.21.
  • the minimum temperature shown for each day is the lowest indoor temperature detected by any sensor node in the house that day.
  • the maximum temperature shown for each day is the highest indoor temperature detected by any sensor node in the house that day.
  • the full temperature swing of the house for each day goes from the lowest temperature measured by any sensor node in the house that day to the highest temperature measured by any sensor node in the house that day.
  • the lowest and highest temperatures for a particular day may not be from the same sensor node.
  • temperature swing information is provided in a user interface as shown in FIG.22. In contrast with the summary Temperature Swing chart of FIG.21, for the statistics here, each sensor node is considered separately.
  • the “Biggest swing” entry is identifying the sensor node that experiences the greatest swing in temperature (e.g. from low to high) during the day.
  • Expansion of HVAC Monitoring Methods [0205] Described herein is an example method of modeling the system across multiple zones. While most smaller homes have just one HVAC system, it is relatively common for larger homes to have more than one HVAC system. Furthermore, some apartments use room-based PTACs (packaged terminal air conditioners) which effectively make them multi-zone residences. Example embodiments account for multi-zone operation. [0206] In the development of a thermal model of the home there are multiple choices to be made regarding the structure and the type of model to be used. One approach is a physical model that is directly based on first principles from heat transfer.
  • Such a model uses known thermal conductivity values that describe the building in which the sensor nodes are placed.
  • black-box model which utilizes the data to train a neural network to model the thermal properties of the house.
  • a black-box model maybe accurate but it is difficult to interpret it to find the values of interest.
  • a physical model requires information about the environment that may not be available, and thus using it would thus require many assumptions.
  • Example embodiments use a hybrid approach which may be described as a grey-box model.
  • a grey-box thermal model combines information about the system from physics with system identification methods to model the system accurately while preserving the meaning of the data. The details of an example model used in some embodiments are given below.
  • An example analysis uses an analogy between heat and charge to create an electric circuit that models the flow of heat with in a multizone building.
  • each zone is modeled as a capacitor connected to ground.
  • the flow of heat between zones is modeled as the flow of charge from one zone to another through a resistor.
  • the flow of heat into the building due to energy from the sun and temperature difference with the outdoors is modeled using a voltage source and a current source that are connected to the zones through resistors as well.
  • Node analysis gives the equation below for the temperature in a given zone: of these states we will perform calculations and store the results to be used in the other states.
  • HVAC OFF, SUN OFF 1. During this state the System has no energy input and so the k values can be determined. 2. This yields a system of n equations, one for each zone. To calculate the values of interest we solve the system of equations described above. During a period of time there will be many measurements and statistical methods can be used to aggregate the data. 2. HVAC ON, SUN OFF 1. During this state the k values stored can be used to solve for the HVAC output for a given zone. 3. HVAC OFF, SUN ON 1. During this state the k values stored can be used to solve for the Sun Input for a given zone. 4.
  • the HVAC Input into the system can be calculated using stored k values and stored sun input values.
  • the K Values will be the thermal will be the product of the thermal conductivity of the wall between two zones and 1/C, where C is the thermal capacitance of the zone.
  • the following assumptions may be used: ⁇ Sun Radiation Energy does not vary greatly over 15 min periods. ⁇ K Values do not vary greatly during daytime [0212]
  • Thermal Conductivity K values are found using previous data.
  • Each sensor node may be treated as a zone.
  • no assumptions about the relationship of the zones to each other are made.
  • the Outside Environment is treated as a zone.
  • Such embodiments generalize more easily rather than a hardcoded estimate of k values. Such embodiments update to reflect variations in thermal resistivity values due to weather conditions. Example embodiments operate to provide useful information without requiring information on the relative locations of the sensor nodes. It may be assumed that all zones are adjacent, such that dT/dt for each zone in an n-zone home may be assumed to be the sum. Thermal Model, First Order Approximation. [0214] An example of thermodynamic method used to model the change in temperature over time as the HVAC system turns on and off is described here. [0215] In some embodiments, it may be assumed that the only q source is from the HVAC system. Sources such as solar, human presence and other heating/cooling sources are absorbed into k.
  • k is constant for the HVAC cycle. Example embodiments take k as the peak of the distribution from the last cycle (If the cycles are very short, the average can be taken instead since a peak in a small sample size has less significance).
  • One or more sensor nodes 3602a-c may be disposed in a residence, e.g. in different rooms. Each node may be plugged in to an electrical outlet.
  • the sensor nodes are in wireless communication with a hub node 3604, e.g. using a WiFi connection or other local area network.
  • the hub node 3604 may further have a connection to a wide-area network 3606 such as the internet through which a networked service 3608 running on one or more servers, such as a cloud service, may be accessed.
  • Users may have personal devices such as a computer 3610 or mobile computing device 3612 that can also access the networked service 3608 over the network 3606.
  • the user interfaces described herein are displayed when the user accesses the networked service on their personal device.
  • the user’s personal devices may be capable of communicating directly with the hub node 3604 and/or with the sensor nodes 3602a-c to view the user interfaces or to exchange other information.
  • the sensor nodes 3602a-c may be capable of communicating over the network 3606 without the intermediation of the hub node (e.g. through a router).
  • the sensor node includes a temperature sensor 418 in communication (e.g. over a bus or other internal connection) with a processor 2408.
  • the temperature sensor is used in collecting the temperature measurements described herein.
  • the sensor node may also include sensors that operate to assist in determining whether the HVAC system is in an “on” or an “off” state.
  • the sensor node may include a microphone 2402 that collects audio data that may be used in determining whether the HVAC system is in operation.
  • audio data is provided to the hub node to interpret the audio data of one or more sensor nodes to determine whether the HVAC system is in operation.
  • the sensor node may further include power monitoring circuity 2424. Such circuitry may detect changes in power supply voltage that may be characteristic of the HVAC system being active or inactive, or changing states between active and inactive.
  • the sensor node further includes a memory 2406, which may include a non-transitory memory.
  • the memory may store collected data (e.g. temperature and audio data).
  • the memory may further store instructions that are executable by the processor for causing the processor to perform any of the methods described herein.
  • a network interface 2410 may be provided to allow for communication with hub devices, other sensor nodes, or other equipment.
  • the hub node likewise includes a memory, which may include a non-transitory memory, a processor, and one or more network interfaces for connection (e.g. a wireless connection) with the sensor nodes and with the internet (possibly through a router).
  • the memory may store collected data (e.g. temperature and audio data) received from one or more sensor nodes.
  • the memory may further store instructions that are executable by the processor for causing the processor to perform any of the methods described herein.
  • Any feature described herein as a module may be implemented with structures including, but not limited to, one or more processors and at least one storage medium (e.g.
  • a non-transitory storage medium storing instructions that are operative, when executed on the one or more processors, to perform any functions associated with the module.
  • a module may further include any appropriate environmental sensors (e.g. a thermometer, hygrometer, microphone) or input or output devices (e.g. screens, keyboards, network interfaces) used to implement the functions associated with the module.
  • computing operations may be implemented by circuitry other than a processor, such as by a field-programmable gate array (FPGA) or other logic circuitry.
  • the componentry used to implement a module may in some embodiments be distributed among different physical devices that communicate with one another to perform the associated functions. Example heating/cooling monitoring systems and methods.
  • Example systems and methods perform HVAC monitoring to monitor the health of an HVAC system.
  • HVAC systems are designed to heat, cool, and move air.
  • Examples of HVAC components as the term is used herein include a furnace, air conditioner, heat pump, portable air conditioner, window units, and the like.
  • an HVAC system operates by blowing hot or cold air into the room. The HVAC turns on and off to control the temperature of the room. The thermostat sets when to turn off/on the HVAC based on the temperature of the thermostat. HVAC performance may be measured by reference to how quickly it can increase/decrease the temperature of the room. As the HVAC deteriorates, it takes longer to heat/cool the room. In the worst case, it won’t affect the room temperature at all.
  • FIGs.25 and 26 provide schematic overviews of HVAC monitoring according to some embodiments.
  • an HVAC AI classifier is implemented at a dwelling, while HVAC system analysis is performed by a cloud service.
  • FIG.26 schematically illustrates a flow diagram of an overall HVAC monitoring process that may be implemented in some embodiments.
  • an adaptive late sensor fusion (LSF) algorithm is implemented.
  • the Adaptive LSF algorithm may be implemented as a plug-in to a probabilistic event finder.
  • an event finder adapter may be used with the probabilistic event finder to obtain adaptive thresholds instead of fixed thresholds.
  • the embodiments described herein may be used for any type of time series data. However, example embodiments are described here with reference to the example of temperature date as applied to HVAC monitoring.
  • an event finder adapter provides a learning operation, an adapting operation, and a use operation. In some embodiments, in the learning operation, at the end of each HVAC cycle, the derivative of the second-degree polynomial fitted on the temperature data is used to determine the rate of heating or cooling.
  • the cycle may be categorized in one of the following buffers: • If the HVAC was off, the slope is positive, and the outdoor temperature was greater than 27C: Add the slope to the 'up and off' buffer. • If the HVAC was on, the slope is negative, and the outdoor temperature was greater than 15C: Add the slope to the 'down and on' buffer. • If the HVAC was on, the slope is positive, and the outdoor temperature was smaller than 27C: Add the slope to the 'up and on' buffer. • If the HVAC was off, the slope is negative, and the outdoor temperature was smaller than 15C: Add the slope to the 'down and off' buffer. [0231] In the adapting operation, at the end of each HVAC cycle, the system adapts to the new information that was added in the learn phase.
  • the 95th percentile of the buffer is determined for each of the four buffers (up/down and on/off combinations). These four values per sensor are stored as thresholds. [0232] In the use operation, when the HVAC LSF is called, the slope of the temperature data is determined for each sensor. If there are at least 200 values in the 'up' or 'down' buffer, depending on the slope that was found, the threshold found in the adapt phase is used. Otherwise, a default, hardcoded value of 75 minutes per degree Celsius is used as a threshold. The LSF is performed using the appropriate threshold.
  • a method for each of a plurality of cycles of an HVAC system, data is obtained representing a measured rate of indoor temperature change during the cycle and an outdoor temperature during the cycle.
  • Each of a plurality of the cycles is classified in one of a plurality of predetermined cycle categories, based at least in part on the associated measured rate and the outdoor temperature.
  • the categories may include some or all of the categories “up and off,” “down and on,” up and on,” and “down and off” as described above, although the categories may differ in different embodiments.
  • a report is provided regarding statistics of the cycles in the reported category.
  • the report provides an indication of an average time per temperature change for the cycles in the reported category, though other statistics may alternatively be used such as the median, mode, or some other value.
  • the classifying of the cycles is further based at least in part on a threshold rate of temperature change.
  • the threshold rate of temperature change for each category is adjusted based at least in part on cycles previously classified in the respective category.
  • Some embodiments further categorize the cycles based on a separate determination of whether the HVAC system is on or off. Such a determination may be made based, for example, on a neural network classifier operating on audio information received by a sensor node.
  • a cycle may be classified as “up and on” or “down and on” only in response to a determination that the HVAC system is on, and the cycle may be classified as “up and off” or “down and off” only in response to a determination that the HVAC system is off.
  • a change e.g. a fault
  • heating performance may be detected based on a change in statistics of cycles in the “up and on” category.
  • a change e.g. a fault
  • cooling performance may be detected based on a change in statistics of cycles in the “down and on” category.
  • a change e.g.
  • HVAC analysis is performed as follows. For each HVAC cycle, the hub node, calculates the HER values for each individual sensor. The HER values are stored on a cloud database. A histogram of the HER data is generated. On the cloud, analysis is performed to generate an overlay that removes statistical noise. Using the overlay, a calculation is made of the typical (mode) and average HER value. [0239] In some embodiments, HER calculations may be performed as follows. HER values are calculated for each sensor individually. A smoothing function may be applied that results in linearized temperature segments (same function as the LSF).
  • a cycle may be considered a cooling event only when the outdoor temperature is greater than the indoor temperature. It may be considered a heating event only when the outdoor temperature is less than the indoor temperature.
  • the threshold temperatures may be independent of the indoor temperature.
  • a report is provided as described herein regarding statistics of the cycles in the reported category.
  • the report may indicate, for example, an indication of an average time per temperature change (e.g. minutes per °C) for the cycles in the reported category.
  • the classifying of the cycles is further based at least in part on a threshold rate of temperature change, and in some such embodiments, the threshold rate of temperature change for each category is adjusted based at least in part on cycles previously classified in the respective category.
  • the categories are as follows: a first category in which the rate of indoor temperature change is positive and the outdoor temperature is above a first threshold temperature (e.g.27°C); a second category in which the rate of indoor temperature change is negative and the outdoor temperature is above a second threshold temperature (e.g.15°C); a third category in which the rate of indoor temperature change is positive and the outdoor temperature is below a third threshold temperature (e.g.27°C); and a fourth category in which the rate of indoor temperature change is negative and the outdoor temperature is below a fourth threshold temperature (e.g.15°C).
  • a first threshold temperature e.g.27°C
  • a second category in which the rate of indoor temperature change is negative and the outdoor temperature is above a second threshold temperature (e.g.15°C)
  • a third category in which the rate of indoor temperature change is positive and the outdoor temperature is below a third threshold temperature (e.g.27°C)
  • a fourth category in which the rate of indoor temperature change is negative and the outdoor temperature is below
  • a cycle is classified in the first or fourth category only in response to a determination that the HVAC system is off during the respective cycle. Such a determination may be made using a classifier operating on audio data, for example.
  • a cycle is classified in the second or third category only in response to a determination that the HVAC system is on during the respective cycle.
  • the threshold temperatures may be determined in various ways. For example, as in examples above, the first and third threshold temperatures may be the same, and the second and fourth threshold temperatures may be the same, but not all embodiments necessarily follow this constraint. In some embodiments, the threshold temperatures are based at least in part on an indoor temperature.
  • Some embodiments include detecting a change in heating performance based on a change in statistics of cycles in the third category. Some embodiments include detecting a change in cooling performance based on a change in statistics of cycles in the second category. Some embodiments include detecting a change in heat retention properties based on a change in statistics of cycles in the first and/or fourth categories. [0246] Some embodiments include an apparatus comprising one or more processors configured to perform any of the methods described herein. [0247] In some embodiments, overlay calculations are performed. The goal of the overlay is to summarize the HER statistics with a minimal number of variables. The model may be based on a steady state heating/cooling and random heating cooling.
  • the model has been found to map to Brownian motion with an inverse gaussian distribution.
  • An example overlay calculation uses two variables and is based on the average and variance of the data. This mathematical model has been found to match real life and simulation data. [0248] Examples of sensor node data are shown in FIGs.27A-27B. The overlay is calculated for each individual room and uses 200 entries in this embodiment for a statistically significant sample size.
  • the lower 95% of the data may be selected to calculate the parameters to account for false positives
  • Parameters of the overlay are calculated by: ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ h ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ [0249]
  • Statistical analysis may be performed with scaling for different rooms.
  • the temperature swings at different sensor nodes may vary throughout the house, depending for example on the distance to a vent, the size of the room, and obstacles such as furniture.
  • FIG.27A illustrates histograms of data from two rooms, Room 1 and Room 2, each of which is fitted with a respective inverse Gaussian.
  • FIG.27B illustrates a histogram in which the data from Room 2 has been scaled to align with the data of Room 1, resulting in substantially similar distributions and largely overlapping inverse Gaussian curves.
  • Some example embodiments of statistical scaling use scaling factors. Each sensor node may have a scaling factor associated with it to account for the variation in temperature swings throughout the house. A learning period may be used that consists of reaching 200 entries.
  • Rooms with lower temperature swings typically report fewer entries as they are more likely to trigger the “unsure” threshold.
  • the first sensor node that reaches 200 entries is labelled as the reference node and given a scaling factor of 1. If multiple nodes reach the threshold at the same time, one of them may be chosen arbitrarily to be the reference sensor node.
  • the scaling factor is applied to the data for those nodes. Overlay parameters may be calculated. The time period where the subsequent sensor had entries may be used to account for later installed sensor nodes.
  • Example embodiments allow for multiple nodes in a single histogram. With multiple sensor nodes throughout a house, each may have its own statistical distribution with different modes and means. Some embodiments operate to combine them together to generate a single histogram.
  • Some embodiments analyze the last N cycles (e.g. the last 200 cycles).
  • the performance chart may show the histogram of all the cycles recorded in the last 12 months and may also include a plot of only the last 200 cycles.
  • the typical (mode) HER value may likewise be calculated for all cycles from the last 12 months as well as only the last 200 cycles.
  • a comparison between the typical (mode) HER values shows possible changes in recent behavior of the HVAC effectiveness. Changes in the recent HVAC effectiveness may be due to different weather conditions, thermostat setting changes or changes in the HVAC system. If the outdoor temperature has been stable, increased amount of time for the typical performance could be due HVAC degradation, and an alert may be provided.
  • Example embodiments provide for the display of historical trends, as shown in FIGs.28-29.
  • an example embodiment calculates the overlay and corresponding mean and modes.
  • the mean and modes are plotted as a function of time, along with the outdoor temperature. This allows for comparison over time. It also enables monitoring over multiple seasons.
  • historical trends are calculated as follows. At a regular interval (e.g. each day at midnight), the performance histogram is updated as follows. The overlay is calculated for the last 200 cycles. A database is used to store the results of the overlay, e.g. the typical (peak) value, the mean value, and the number of days for the previous 200 cycles. The minimum/maximum for the performance is calculated from the list of stored typical overlays stored in the database. Add to the historical trend daily.
  • the darkness of the color for the trend-line changes depending on the number of days required to measure the 200 past cycles. For example, the darkest shade may be used when the number of days for the past 200 cycles is less than 20, the lightest shade may be used when the number of days is 80, and results with greater than 80 days are not shown.
  • performance is shown with needle gauges as shown in FIG.30.
  • needle gauges may be used as a quick summary of the HVAC performance for both heating and cooling.
  • the scaling may be set by the historical HVAC performance, where the “12 o’clock” position is the typical performance over the last 12 months. The needle then may be used to indicate the typical performance from the last 200 HVAC cycles.
  • Variables used for HVAC calculations in some embodiments are shown in the following table: Variable Definition sensor nodes. Each sensor node has been found to follow an Inverse Gaussian behavior, though different distributions may be used in other embodiments. To get a whole-house display, some embodiments use the following procedure. [0259] At least a threshold number, such as 200, of HERi,j terms are collected. The number of HVAC cycles vary for each sensor and some may reach this number before others. There may be multiple sensor nodes for a given zone, but the point of this display is to have a whole house or whole residence view that combines all the zones together. So any sensor node i which has more than 200 HERi,j terms may be included in that combination view.
  • a particular sensor node i has less than 200 HER i,j terms, in some embodiments it is excluded from the summary histogram. In some embodiments, for the following steps, only the sensor nodes that satisfy this criterion are to be used. [0260]
  • the parameters ⁇ i , ⁇ i , mode i , and ⁇ i are calculated for each sensor node. This may be done for each sensor node i by using the following formulas applied to just the HER i,j terms for sensor node i: [0261]
  • the overlay function in some embodiments is given by the taking the sum of the Inverse Gaussian Functions for each sensor node i: by taking the mean of the scaled HER values. [0267] The peak is the determine this by finding the tpeak where it is the time corresponding to the peak value of the overlay. [0268] In some embodiments, the following values are used in calculating HVAC performance. Variable Definition t x_lambda i (t1, The lambda term used for the overlay. This is calculated from x_HER i,j values t2) with timestamps between t1 and t2. [0269] Days of heating/cooling operation may be calculated as follows.
  • days_operation + 1.
  • x_si x_modex_i-ref / x_modei [0273]
  • the scaling factor, x_si once determined, remains constant and may be stored for future use. In cases where the scaling factor, x_si, is not determined until x_N i > 200, it is not necessary to plot a sensor node’s HER values until it has recorded 200 HER values.
  • Example embodiments have been found to work well when the sensor node is placed near a vent when the HVAC unit is inaccessible because of the Adaptive Threshold feature that automatically calibrates in situ. The Adaptive Threshold dynamically adjusts to circumstances. Example temperature retention monitoring systems and methods.
  • TRR Temperature Retention Ratio
  • the TRR values are calculated over time and the statistics may be shown to the user.
  • the TRR values may be plotted in a box & whisker plot as a function of outdoor temperature. As the outdoor temperature is further away from the room temperature, the TRR value decreases.
  • An overview of approximate equations describing home heat loss is as follows.
  • the home may be approximated as a cube of side length ⁇ with no ground contact.
  • the walls are considered to have a thickness ⁇ with ⁇ ⁇ ⁇ .
  • the temperature outside the house is considered to have a constant value ⁇ ⁇ .
  • the temperature difference is thus given by ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ ⁇ .
  • the wall has a thermal conductivity of ⁇ , and the ⁇ value is given by ⁇ ⁇ ⁇ / ⁇ .
  • the heat flux into the house is given by an area of ⁇ ⁇ 6 ⁇ ⁇ , so the total heat flow into the house is given by [0280]
  • the temperature change is given by the above equations gives the temperature by 1°C, in the case where ⁇ ⁇ ⁇ 1°C, is approximately on the specific heat ⁇ ⁇ and the mass ⁇ ⁇ ⁇ ⁇ , this result may be expressed as product ⁇ ⁇ ⁇ ⁇ is empirically assigned a typical or average value, e.g.
  • thermal retention is calculated as follows. 1. TRR values are calculated for each sensor node individually 2. The TRR value is calculated from the temperature data after a HVAC heating/cooling cycle.
  • TRR heat would be measured after a heating cycle.
  • TRR cool would be measured after a cool cycle.
  • 3. Perform a smoothing function that results in linearized temperature segments (same function as the LSF) 4. Measure the first linearized slope after the HVAC cycle. a) “TRR heat” measures how quickly the house is cooling after a heating cycle b) “TRR cool” measures how quickly the house is heating after a cooling cycle c) If the slope is the same sign as the HVAC cycle, then consider it invalid. E.g. after a heating HVAC cycle, if temperature is increasing after the HVAC cycle, then we have an invalid TRR value and do not record it. 5.
  • FIG.32 shows the slope of the best fit line for the TRR vs temperature as a function of thermal conductivity of the wall in simulated data.
  • FIG.18A shows an example temperature retention report. It may be seen in the report that there are different slopes for heating and cooling seasons and different effective R-values for the two seasons. It may also be noted that the effective R-value is room dependent.
  • FIG.34 illustrates an example graph of dirtiness versus time that may be used in some embodiments.
  • a lookup table may be used to determine expected life.
  • Some embodiments calculate an expected remaining lifetime. The calculated expected remaining lifetime may be calculated in various ways. Typical usage from the general population may be used. Calculations may be based off runtime over last period of time with the expectation that this usage will. Some embodiments learn the user behavior over several seasons and make predictions based off the expected outside temperature. Some embodiments seek feedback from the user whenever they check the filter.
  • the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

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Abstract

Un procédé selon certains modes de réalisation consiste, pour chaque cycle d'une pluralité de cycles d'un système CVC, à obtenir des données représentant un taux mesuré de changement de température intérieure pendant le cycle et une température extérieure pendant le cycle. Certains des cycles sont classés dans l'une d'une pluralité de catégories de cycles prédéterminées. La classification est basée au moins en partie sur le débit mesuré associé et sur la température extérieure. Pour au moins l'une des catégories, un rapport est fourni concernant des statistiques des cycles dans cette catégorie. Le rapport peut indiquer un temps moyen par changement de température (par exemple minutes par °C) pour les cycles dans cette catégorie. Un changement des statistiques dans le temps peut être détecté et rapporté en tant qu'indication de problèmes de CVC et/ou d'isolation.
PCT/US2024/037073 2023-07-07 2024-07-08 Appareil et procédé de surveillance et de suivi d'efficacité de chauffage, de refroidissement et de rétention de chaleur Ceased WO2025014886A2 (fr)

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CN120970020A (zh) * 2025-10-16 2025-11-18 陕西普赛能源科技有限公司 面向峰谷电价差异化的热泵环境温度自适应调控系统

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US7848900B2 (en) * 2008-09-16 2010-12-07 Ecofactor, Inc. System and method for calculating the thermal mass of a building
US20220026101A1 (en) * 2018-02-20 2022-01-27 Komfort Iq Inc. System and method for multi-zone climate control
KR20210063970A (ko) * 2019-11-25 2021-06-02 엘지전자 주식회사 공기 조화기 및 그 제어 방법

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