阅读说明：本技术 住宅能量效率评级系统 (Residential energy efficiency rating system ) 是由 N.范古尔 P.达瓦罗斯 G.帕塔萨拉蒂 于 2017-08-17 设计创作，主要内容包括：一种估计的住宅能量效率评级机构,包括能量评级数据存储；连接到所述能量评级数据存储的模型训练处理器；连接到所述模型训练处理器的模型参数存储；连接到所述模型参数存储的估计的REER(eREER)计算处理器；以及连接到所述模型训练处理器和所述eREER计算处理器的一个或多个存储。(An estimated residential energy efficiency rating mechanism comprising an energy rating data store; a model training processor connected to the energy rating data store; a model parameter store connected to the model training processor; an estimated REER (eREER) calculation processor coupled to the model parameter store; and one or more memories connected to the model training processor and the eREER computation processor.)

1. An estimated residential energy efficiency rating mechanism comprising:

an energy rating data store;

a model training processor connected to the energy rating data store;

a model parameter store connected to the model training processor;

an estimated REER (eREER) calculation processor coupled to the model parameter store; and

one or more memories connected to the model training processor and the eREER calculation processor.

2. The mechanism of claim 1, wherein the one or more stores are selected from the group comprising: residential structure data storage and consumer demographic data storage.

3. The mechanism of claim 1, further comprising an estimated rer (eREER) data Application Program Interface (API) connected to the eREER computation processor.

4. The mechanism of claim 3, further comprising one or more client applications connected to the eREER data API.

5. The mechanism of claim 3, further comprising:

an eREER store connected to the eREER computation processor and to the eREER data API; and

one or more client applications connected to the eREER computing processor and to the eREER store.

6. The mechanism of claim 5, wherein:

the client application requesting results from the eREER calculation processor on-demand through the eREER data API, wherein the results are computed on-the-fly; or

A client application makes a request for results of the eREER computation processor that have been previously computed and saved in the eREER store.

7. The mechanism of claim 4, wherein the one or more client applications connect to the eREER data API via the internet.

Background

The present disclosure relates to energy usage at a site, and in particular to energy efficiency at such a site.

Disclosure of Invention

A residential energy efficiency rating system may include one or more sensors located at one or more residential assets, a gathering and transmitting device connected to the one or more sensors, a central data receiver and processor connected to the gathering and transmitting device, a historian data store connected to the central data receiver and processor, and a residential energy efficiency rating calculator connected to the historian data store.

Drawings

FIG. 1 is a diagram illustrating an embodiment of a residential energy efficiency rating calculation;

FIG. 2 is a diagram illustrating an embodiment of an estimated residential energy efficiency rating calculation;

FIG. 2a is a flow chart of a residential energy efficiency rating calculation;

FIG. 2b is a diagram of a flow chart for estimated REER (eREER) model training;

FIG. 2c is a diagram of a flow chart for eREER computational interpretation;

FIG. 3 is a diagram of a heating plant cycle, such as during winter;

FIG. 4 is a diagram also relating to the heating plant cycle during winter;

FIG. 5 is a diagram that may indicate a method for inferring a thermal characteristic of a premises;

FIG. 6 is a diagram reflecting overall downtime analysis results;

FIG. 7 is an illustrative county-level example map in which residence thermal efficiency may be indicated in terms of average cycle down time;

FIG. 8 is a diagram of a map with cycle down time for various dwellings and buildings in a residential area;

9-12 are diagrams disclosing temperature versus time graphs for various heater down times;

FIGS. 13-14 are graphs of time versus particle temperature, daily temperature, and daily run time temperature relating to site efficiency;

FIG. 15 is a diagram of a chart of the apparatus used in residential thermal analysis during the first week of the year;

FIG. 16 is a plot of house size versus downtime scatter plot;

FIG. 17 is a diagram of a graph revealing the distribution of house sizes in terms of number of occurrences versus house size;

FIG. 18 is a graph of a two-dimensional density plot in terms of downtime versus house size;

FIG. 19 is a diagram of a graph revealing the distribution of downtime as a function of number of occurrences versus downtime;

FIG. 20 is a plot of a scatter plot with contours in terms of downtime versus age of construction;

fig. 21 is a diagram of a graph of distribution of construction years in consideration of the presence of a house;

FIG. 22 is a graphical representation of a two-dimensional density plot of age versus down time;

FIG. 23 is a diagram of a graph of the distribution of downtime in terms of number of occurrences versus downtime;

fig. 24 is a diagram of a graph showing the distribution of house sizes by efficiency classification;

fig. 25 is a diagram of a graph showing the distribution of houses according to the construction age by the efficiency classification;

FIG. 26 is a diagram of a graph of a low efficiency house in terms of age of construction versus house size;

FIG. 27 is a diagram of a graph of a high efficiency house in terms of age of construction versus house size;

FIG. 28 is a diagram of a graph of the distribution of downtime in terms of minutes versus occurrences;

FIG. 29 is a diagram of a graph involving the cumulative sum of a series of downtime of each device over a multi-week period;

FIG. 30 is a diagram of a graph with a plot of number of weeks versus downtime (in minutes) for a sample house;

FIG. 31 is a diagram of a graph with a plot of weeks versus downtime (in minutes) for another sample house;

FIG. 32 is a graph having a plot that appears to have low ripple and is relatively high in terms of downtime versus number of weeks;

FIG. 33 is a diagram disclosing a graph of equipment runtime profile in terms of outdoor temperature versus runtime;

FIG. 34 is a graph of time versus pellet temperature, daily temperature, and daily run time temperature for a particular home;

FIG. 35 is a graph having a plot that appears to have low ripple and is relatively low in terms of downtime versus number of weeks;

FIG. 36 is a diagram of a graph revealing an equipment run time profile in terms of outdoor temperature versus run time for another home;

FIG. 37 is a graph of a plot of time versus particle temperature, daily temperature, and daily run time temperature for the home recorded in FIG. 36;

FIG. 38 is a diagram of a graphical map showing the geographic distribution of stable locations; and

FIG. 39 is a diagram of a graphical map showing the geographic distribution of unstable locations.

Detailed Description

In implementations described and/or illustrated herein, the present systems and methods may include one or more processors, computers, controllers, user interfaces, wireless and/or wired connections, and the like.

The description may provide one or more illustrative and specific examples or ways of implementing the systems and methods. There may be numerous other examples or ways of implementing the present systems and methods.

Aspects of the systems and methods may be described in terms of symbols in the drawings. The symbols may have virtually any shape (e.g., box) and may indicate hardware, objects, components, activities, states, steps, procedures, and other items.

It is to be understood that the energy efficiency of residential buildings can be valuable to many different owners. Stakeholders may include, but may not necessarily be limited to, home owners who benefit from understanding whether they may reduce and how their energy consumption may be reduced; a purchaser of the dwelling who benefits from understanding the future recurring expenses for the dwelling that it may consider; real estate agents that benefit by more accurately estimating home pricing; an occupancy valuator who may benefit by more accurately estimating the value of an occupancy; utility units that benefit because they may be committed to reduce energy usage in their area and directly pay for or subsidize the cost of energy efficiency improvements in the dwelling; and contractors who benefit by understanding what concerns a dwelling may be good candidates for energy efficiency programs.

Without a premise energy audit, it may be difficult to assess energy performance levels for a premise and recommend energy efficiency improvements. The utility may contract with a third party company to perform premise energy audits, and these audits may be paid or subsidized by the utility, while the premise owner pays some of the costs. These audits may be in-person checks in the field. A problem may be that these in-person checks may be time consuming, expensive, and not necessarily easy to scale for large populations and not necessarily easy to perform in a standardized, consistent manner. Further, home owners may have registered for these, but many audits do not necessarily result in guaranteed energy improvements, or even warrant recommending cost-effective improvements (i.e., a home may be very energy efficient and may not likely benefit from an audit). Interested parties should have a way to target residences with a higher likelihood of achieving cost-effective energy efficiency improvements.

The method can develop a virtual energy audit of the house. The method for estimating the residential energy index may be based on various data sources (including from HVAC data), and may provide an automated, consistent, and continuous method for performing virtual energy audits. Additionally, the statistical procedure disclosed herein may allow for estimating the energy efficiency of a home for which HVAC data may not be available.

While conventional in-person energy audits may be comprehensive and may pinpoint the precise location of heat leaks for each premise, the present method may identify overall energy performance for residential heating and cooling (e.g., building enclosures and heating and cooling equipment), which may generally be the largest contributor in terms of energy costs for the premise. Thus, the present method may enable identification of premises that would benefit most from detailed energy audits, and at the same time also identify the most efficient premises. The present method for estimating the energy index of a dwelling may feature a mainly passive method, which utilizes data from a connected dwelling without any additional sensors, additional measurements. The method may remove this constraint by using connection residence data, which may include device control signals and cycle information about the operation of the device.

A method for calculating an energy index for a premises may include analyzing thermostat data. Outdoor weather data, real estate asset information, and other data may also be used. Specifically, there may be an alternative method as noted later.

One approach may involve analyzing individual cycles and focusing on the duration of the downtime period, where corrections may be made to the duration of the downtime period, and outdoor weather characteristics (such as temperature differences between the interior and exterior, solar radiation or cloud levels) as well as other information (such as house size and age). The downtime period cycle duration may be an approximation of the drop in actual temperature during the time period while the set point and display temperature remain substantially constant. The method may assume that an Installer Setup (ISU) loop configuration setting of the thermostat remains unchanged during the analysis observation period.

Another method may be similar in analyzing the shutdown cycle duration, except that the duration of aggregation may be continued through a longer period of several cycles with constant set points and temperatures up to a maximum aggregation period.

Additional methods may include aggregating downtime duration for longer periods of time, where there may be changes in set points, but analyzing the data at a daily level.

A comparable energy performance index can be reached based on the above downtime parameters. House size, age and other data may be used to further classify and make the dwelling energy index more robust.

Having a calculated home energy efficiency rating (rer) for a statistically large number of homes may allow ratings to be estimated for homes for which there is no connected residence data. This may be accomplished by identifying inputs (such as building size, age, and type of construction) to statistical modeling techniques (such as neural networks, recursive models, or decision trees) that may be related to ratings.

Using the calculated residential energy efficiency ratings for a statistically large number of residences and known information about the residences and their occupants, the parameters of the mentioned statistical modeling techniques can be estimated using statistical training techniques such that the statistical model most accurately estimates the residential energy efficiency ratings for residences for which the residential energy efficiency ratings cannot be directly calculated.

The algorithms described herein may be implemented in a system as depicted in fig. 1 and 2. FIG. 1 is a diagram illustrating an embodiment of a residential energy efficiency rating calculation. In this system, there may be example residential assets 11, 12, and 13 with a variety of sensors. The example residential asset 11 may have an outdoor temperature sensor 14, an indoor temperature sensor 15, an HVAC status sensor 16, a setpoint sensor 17, HVAC equipment 18, and aggregation and transmission equipment 19. The example residential asset 12 may have an indoor temperature sensor 15, an HVAC status sensor 16, HVAC equipment 18, and an aggregation and transmission device 19. An example residential asset 13 may have an indoor temperature sensor 15, HVAC equipment 18, and an aggregation and transmission device 19.

The outdoor temperature sensor 14 may be a sensor placed outside the house that captures the outdoor temperature. The sensors 14 may be wired or wirelessly connected to a device 19 that transmits the collected data to the central device data receiver 20 via the internet 40. One or more of these sensors may be present without any of these sensors.

The indoor temperature sensor 15 may be a sensor that is placed inside a house to capture indoor temperature. The sensors 15 may be wired or wirelessly connected to a device 19 that transmits the collected data to the central device data receiver 20 via the internet 40. One or more of these sensors may be present without any of these sensors.

The indoor set point sensor 17 may be a sensor that knows the temperature set point of a thermostat that may be in the home. This knowledge can be obtained by sensing the setpoint value directly or because the indoor setpoint sensor 17 can know the action history. The sensors 17 may be wired or wirelessly connected to a device 19 that transmits the collected data to the central device data receiver 20 via the internet 40. One or more of these sensors may be present without any of these sensors.

The HVAC status sensor 16 may be a sensor that is aware of the status of the HVAC equipment 18. This knowledge may be obtained by sensing the state directly or because the HVAC state sensor 16 may know the action history. The sensors 16 may be wired or wirelessly connected to a device 19 that transmits the collected data to the central device data receiver 20 via the internet 40. One or more of these sensors may be present without any of these sensors.

The HVAC equipment 18 may be a single-stage or multi-stage HVAC system. In some cases, the status of the HVAC equipment 18 may be transmitted to the central plant data receiver 20 via the sensor 16 and the aggregator and transmitter 19.

The aggregator and transmitter 19 may have various sensors in and around the home connected to it, either wired or wirelessly. The device 19 may collect data from the mentioned sensors and send the data to the central device data receiver 20 across the internet 40, either wired or wireless. Examples of such devices may include a connected thermostat, a connected water leak detector, a connected water heater, and a connected residential security system.

The device data receiver 20 may receive sensor data from the field, perform any necessary decoding and/or parsing and save it in the historian data store 21. Historian data store 21 may store historical data that may be used as input to rer calculation 27.

HVAC state inferences can be made without necessarily being able to directly observe or record HVAC states. The processor 22 may infer the HVAC state from other data, such as indoor temperature changes. The inferred HVAC states may be stored in the historian data store 21 as appropriate.

The weather data store 23 may be for commonly available weather data. Here, the historical data store 21 may be supplemented with weather data when certain relevant weather-related values (such as outdoor temperature, wind speed, and outdoor humidity) are not necessarily directly measurable. The weather data store 23 may forward the data to the historian data store 21 or may query the weather data store 23 directly by the rer calculation 27 as appropriate.

The residential structure data store 24 may be for certain information about the residential structure, which may be related to the thermal efficiency of the computing structure. This information may include values such as age, size, and construction method of the structure. The home structure data can be queried directly from the rer calculation 27.

The consumer demographic data store 25 may be some demographic information for the occupants of the residential structure, which may be related to the thermal efficiency of the computing structure. This may include values such as income, age, family composition, and family interests of the structure's households. The home structure data can be queried directly from the rer calculation 27.

The customer data store 26 may be various and mostly static customer data, such as structure location, for storage and use by the rer calculation. The customer data may be queried for direct use in the rer calculation 27 and may be queried directly from the rer calculation 27.

The rer calculation 27 may involve a processor that is present to calculate a residential energy efficiency rating. The processor may ingest data from data sources such as a historian data store 21, a weather data store 23, a residential structure data store 24, a consumer demographic data store 25, and a customer data store 26. Additional data from other data sources may also be included. There may be many variations of the rer calculation, including the following items.

A method may involve analyzing individual cycles and focusing on the duration of the downtime period, where to correct for the duration of the downtime period, and outdoor weather characteristics, such as temperature differences between the interior and exterior, solar radiation or cloud cover levels, and other information such as house size and age. The downtime period cycle duration may be an approximation of the drop in actual temperature during the time period while the set point and display temperature remain substantially constant. The method may assume that an Installer Setup (ISU) loop configuration setting of the thermostat remains unchanged during the analysis observation period.

The second method may be similar to the method of analyzing the duration of the shutdown cycle, except that the duration may be aggregated through a longer period of time with several cycles with constant set points and temperatures up to a maximum aggregation period.

A third method may include aggregating downtime period durations for longer periods of time, where there may be changes in the set point, but analyzing the data at a daily level.

The results of the rer calculation 27 may be stored in a rer data store 28. The rer data store 28 may contain a rer calculation history stored in the data store. The data may then be accessed through an Application Program Interface (API) 29 and made available to the client application 30.

The REER data API 29 may refer to REER data made accessible through an interface that provides appropriate monitoring and access control for the data. The API 29 may be used by the client application 30 for the client's specific purpose.

A wide variety of client applications 30 may use the rer data for specific purposes. The application 30 may access the REER data through the REER API 29.

FIG. 2 is a diagram illustrating an embodiment of estimated residential energy efficiency rating calculation. There may be an energy efficiency rating data store 38 containing a history of energy efficiency ratings for a large number of residential dwellings. The efficiency rating may be rer. There may be a residential structure data store 24 containing information about residential dwellings. This information may include data such as the age, size, and configuration type of the residence. There may be a consumer demographic data store 25 containing information about consumers who may live in the residence. This information may contain data such as household income, household make-up, and interests.

The data contained in data stores 38, 24, and/or 25, and potentially other data sources, may be accessed by model training processor 31, which model training processor 31 uses the data to calculate statistical model parameters. The statistical model may be a neural network, a recursion, a decision tree, or other statistical model, as appropriate. The model parameters may be stored in a model parameter data store 32.

Data from the parameter and data stores 32, 24, and/or 25, and potentially other data sources, may be accessed by an estimated residential energy efficiency rating (eREER) calculation processor 33 to calculate an estimated residential energy efficiency rating for any dwelling for which appropriate data may be available. This calculation may occur on demand or in a batch manner. In either case, the results may be stored in the eREER data store 34.

The eREER results may be made accessible through an API 35 accessible by the client applications 36 and/or 37. In this example, the client application 36 may request on-demand eREER calculations for one or more specific assets through the API 35. In this case, the erer can be calculated instantaneously using appropriate data. On the other hand, the client application 37 may make a request for eREER results that have been previously computed and may be stored in the eREER store 34.

Fig. 2a is a diagram of a flow chart for the rer calculation. At symbol or step 230, customer information, such as a home location, may be loaded from the customer database 26 for the next home.

At step 231, historic equipment data, such as HVAC equipment runtime, may be loaded for the home from the equipment data store 21. Typically, days (such as 7 days) of data may be loaded to stabilize the final rating. At step 232, additional data, such as outdoor weather data, may be loaded from data stores 23, 24, and 25. The data range may match the data retrieved in step 231.

At step 233, loops may be identified in the device data by observing events that indicate that the HVAC equipment has been turned on. A cycle may be a period of time between two consecutive events indicating that the HVAC device has been turned on. To minimize external disturbances, such as solar radiation or activity in the home, certain periods of the day may be excluded from the analysis. Cycling may also be omitted if certain temperature ranges are observed (or for other reasons).

At step 234, each cycle may include a period of time when the HVAC equipment is then continuously on, followed by a period of time when the HVAC equipment is then continuously off. The down time characteristics may include the time the HVAC equipment is turned off and the temperature difference between the interior and exterior of the house.

At step 235, the previous steps may derive a plurality of down time cycle characteristics calculated for each home. To arrive at the singular, a representative cycle may be selected. Examples may include averaging or median cycling, or mathematical modeling methods may be used to characterize the relationship between the downtime and the inside/outside temperature difference.

At step 236, the calculated downtime model may be scaled to a more convenient rating system, such as a rating star rating. At step 237, all or some of the results may be stored in the rer data store 28 for later retrieval. At step 338, if all homes have not been processed, continue with the next home; otherwise it can be stopped.

Fig. 2b is a diagram of a flow chart for eREER model training. At symbol or step 239, the heat efficiency rating may be loaded from the rating data store 38. The rating may be the rer or some other rating. At step 240, relevant structure data may be loaded from data stores 24 and 25. These stores may include data about the building, its residents, the building environment, and the like. At step 241, data features to be used as input to the statistical model may be computed. Examples of features that may be calculated may be the structure surface to volume ratio and the number of windows per surface area. At step 242, to ensure that the statistical model captures reality, model parameters may be calculated using a subset of the data (training data). The model may then be tested against the remaining data (i.e., test data). This may ensure independent verification of the model. In this step, the data may be split into two data sets. The segmentation criteria may typically be based on a random basis. At step 243, the eREER model may be trained using numerous types of statistical methods. Examples may include neural networks, recursions, and decision trees. In each of the possible methods, training data may be used to calculate model parameters that best represent the data. At step 244, the model may be validated using the test data. Since this data is independent of the training data, any deviations in the model parameters that may have been created in step 243 should be detected in this step. Various statistical criteria may exist to determine model performance. Examples of these may include determining coefficients and mean square error. At step 245, if the test shows model parameters that cannot be satisfactorily executed against the test data, the process may return to step 241 to check for existing features and create new features. At step 246, satisfactory model parameters may be saved to the model parameter store 32.

Fig. 2c is a diagram of a flow chart for eREER computational interpretation. At symbol or step 247, the relevant structure data may be loaded from data stores 24 and 25. These stores may include data about the building, its residents, the building environment, and the like. At step 248, data features to be used as input to the statistical model may be computed. Examples of features that may be calculated may be the structure surface to volume ratio and the number of windows per surface area. At step 249, the model parameters may be loaded from the data store 32. At step 250, eREER may be calculated using the appropriate data features and previously estimated model parameters. At step 251, the calculated eREER value may be saved to the eREER data store 34.

The following discussion may relate to residential hot reference marking and virtual energy auditing. FIG. 3 is a diagram 50 of an HVAC heating plant cycle, such as during winter. In theory, the actual temperature may fluctuate around the set point. The target temperature may be 20 degrees celsius as shown by line 51. The upper limit 52 may be 21 degrees celsius and the lower limit 53 may be 19 degrees celsius. Limits 52 and 53 may define a dead band 54. The temperature may be indicated by a waveform 55. The on-time of the heater is shown by bracket 56 and the off-time of the heater is shown by bracket 57. The operation and downtime of the heater is repeated in a cyclic manner.

FIG. 4 is also a diagram 60 of an HVAC heating plant cycle during winter. In practice, the diagram shows the on-time 61 as indicated by a line or solid area, and the down-time 62 of the heater as indicated by a blank interval. Bracket 63 identifies on timeline 64 of diagram 60 10 hours with 35 cycles and no temperature change (indicated at line 65 relative to temperature line 66).

FIG. 5 is a diagram 68 that may indicate a method for inferring a thermal characteristic of a house. The curve 69 indicates the on and off periods of the heater. Set points SP + db/2 and SP = db/2 are indicated on the left side of the graph 68, which reveals a dead band 71 with a temperature drop relative to the set point, as indicated by the curve 72.

During each off period, the indoor temperature may decrease (dead band) by a certain number of degrees fahrenheit if the thermostat is actively controlled (e.g., not automatically turned down). The downtime for each cycle may be related to (T _ in-T _ out).

Lumped capacity analysis can reveal that:

dead band/downtime = dT/dT = f (δ T) = δ T/time constant;

(T _ out-T _ in)/dead band/dead time) = time constant;

assume 1 degree fahrenheit. Dead band (T _ out-T _ in) = time constant estimate.

The downtime analysis method may be recorded. The existing available data intended for utilization may include user interface data, heating and cooling requirements, and weather. The cycles that can be analyzed (the previous, through, and following data points) can be performed with a constant set point, a constant display temperature, a display temperature at the set point always through a cycle, a night-time only cycle to avoid the effects of solar greenhouse warming (i.e., 7pm-10 am), a cycle in the warming-only mode, and a cycle that does not have a data gap longer than 2 hours. The cycle down time analysis may be recorded as a proxy for the rate of temperature drop.

Additional steps for analysis may be recorded. A weather system may be used to handle all devices. It can be recorded that only about one third of the devices keep outdoor weather in their UI data. The metrics can be combined regardless of the (inside/outside) temperature difference. This may allow the overall year's insulating efficiency of the dwelling to be calculated, not necessarily only winter. This may also allow for comparisons of scores between houses. A weekly metric may be calculated. This may allow for the acquisition of an insulation score history for each house over time. During summer cooling, the performance of the dwelling may be evaluated. This may allow for calculation of insulation for the entire year of the dwelling, not just winter.

Assumptions and analysis may be recorded. A lumped capacity thermal system may be assumed. In addition to thermal performance, the temperature drop (heating season) may be due to the surface area of the enclosure, the thermal capacity of the house (such as how many people are in the house and how dense the heat is), and the thermostat location in the house. The latter two indicate the impact (focusing on overall variability), and may be used from, for example, InfoGroup^TMOr house data from other data sources to obtain building area and floor number to obtain surface area estimates.

Additional analysis may provide (T _ out-T _ in) ((down time) = time constant estimate R _ equivalent = (T _ out-T _ in) ((down time)) (surface area/(heat capacity). Assuming equal heat capacity, the higher the R _ equivalent, the better the shell thermal properties may be. Other data sources may be found to reinforce the fiducial markers.

Can record the houseAnd (4) producing a data source. Utilizing InfoGroup^TMAnd 3, obtaining the size and the construction age of the house by using YLM data. The devices may be selected based on criteria such as one thermostat per location, a user address matching the location address, down time efficiency data available from the past time to the present time, devices and users that are still registered and online, and InfoGroup^TMOr other provider demographic data available for the device.

FIG. 6 is a graph 75 reflecting the results of an overall downtime analysis, in terms of temperature difference between the inside and outside in degrees Fahrenheit versus cycle downtime (in minutes). More cycle down time reflects more efficiency of the heating system. Bar 76 reveals virtually all cycle down time across the entire one hour timeline for outside temperatures, inside temperatures minus outside temperatures of 10-20 degrees fahrenheit. For 20-30 degrees Fahrenheit, the bar area 77 is the cycle down time bar and the area 78 is the cycle in time. For 30-40 degrees fahrenheit, the cycle down time is greater as indicated by the cycle in time of bar area 79 and bar area 81. For the cooler outer 40-50 degrees, the shutdown cycle strip area 82 appears larger, and the area 83 appears slightly smaller relative to the corresponding area for the 30-40 degree range. This may indicate a slightly more efficient situation for the 40-50 degree range.

FIG. 7 is an exemplary map 85 of an illustrative county where the dwelling thermal efficiency may be indicated in terms of average cycle downtime over a period of days with inside/outside temperature differences between 30-40 degrees Fahrenheit.

Fig. 8 is a diagram of a map 88 with cycle down time for various residences and buildings in a residential area. The cycle down time for the individual house 91 is indicated on the map 88 as 0-10 minutes. This can be considered a low score and has an inefficient situation. The cycle down time for the apartment complex 92 is indicated on this map as being greater than 50 minutes. The single family house 93 is indicated as having a cycle down time of 40-50 minutes, which may be considered an efficient situation. The single family house 94 is indicated as having a cycle down time of 10-20 minutes, which may be considered a rather inefficient situation. The individual house 95 and apartment complex 96 are indicated as having cycle down times of 20-30 minutes and 30-40 minutes, respectively, which can be considered as fair or moderate in terms of efficiency situations. The building area of the completed space and the date of construction of the individual house or apartment may be information provided along with the cyclical downtime for the structures 91-96 on the map 88.

Fig. 9 is a diagram disclosing a graph 101 of temperature versus time. The heater down time was 5.5 hours with a drop of 6 degrees fahrenheit. This can be considered as an efficient situation with a drop of 1.1 degrees fahrenheit per hour. This data may reflect the situation for an apartment built in 2006. The outdoor temperature may be about 20 degrees fahrenheit and the delta temperature may be about 50 degrees fahrenheit. This difference may be applicable to subsequent similar graphs.

Fig. 10 is a diagram illustrating a graph 102 of temperature versus time. A 6 degree drop over a 3.4 hour period may be considered an inefficient situation at 1.7 degrees per hour. This data may reflect the situation in 1977 for a house of 1,137 square feet.

Fig. 11 is a diagram showing a graph 103 of temperature versus time. A 6 degree drop in 6.5 hours of down time can be considered an efficient situation at 0.9 degrees per hour. This data may reflect the situation of a 2,550 square foot single family house built in 1986.

Fig. 12 is a diagram illustrating a graph 104 of temperature versus time. A 5 degree drop in 2.5 hours of downtime may be considered an inefficient situation for an 2,060 square foot single family house built in 1951 with a 2.0 degree fahrenheit drop per hour.

Fig. 13 is a graph 105 of time versus particle temperature 106, daily temperature 107, and daily run time temperature 108. This data may relate to a 2006 apartment with efficient scenarios. Symbols 111, 112, 113, 114, 115, 116, 117, 118, and 119 may represent heat SP, display temperature, outdoor temperature, indoor humidity, daily average heat SP, daily average outdoor temperature, daily average indoor humidity, actual heat RT, and heat RT estimate, respectively. The data of the graph 105 may relate to apartments built in 2006 (which have an efficient scenario).

Fig. 14 is a graph 121 of time versus particle temperature 106, daily temperature 107, and daily run time temperature 108. Symbols 111-119 represent the same items as those in fig. 13. The data of graph 121 may relate to an 1,137 square foot single family house (which has an inefficient situation) built in 1977.

Fig. 15 is a diagram of a chart 125 of devices used in residential thermal analysis during the first week of a month. The chart 125 may show all devices with any data in the bars 126 and 127. The bar 126 may represent 360,630 devices that have outdoor weather in the UI data, and the bar 127 may represent 782,670 devices that do not have outdoor weather in the UI data. In the absence of outdoor weather, the residential thermal model is not necessarily available. The bar 126 may be broken down into a sub-bar 128 having 185,456 devices acceptable for the dwelling thermal model and a sub-bar 129 having 75,174 devices. The latter device with respect to the dwelling thermal model is not necessarily available without a heating run time (with constant SP and room temperature).

FIG. 16 is a plot of house size versus downtime in square feet and minutes, respectively. Several contours 132 may be located at 10 to 20 minutes and 1,000 to 2,000 square feet.

FIG. 17 is a diagram of a graph 134 that reveals the distribution of house sizes in terms of number of occurrences versus house size in square feet. The graph 134 may have a correspondence to the graph 131 of fig. 16.

Fig. 18 is a graph of a 2D density plot 136 in terms of downtime (in minutes) versus house size (in square feet). The density of fig. 137 may be at 5 to 30 minutes and 1,000 to 3,500 square feet.

FIG. 19 is a diagram of a graph 139 revealing the distribution of downtime in terms of number of occurrences versus downtime (in terms of minutes). The graph 139 may have a correspondence with the graph 136 of fig. 18.

FIG. 20 is a diagram of a scatter plot 141 with a contour 142. The graph is presented as downtime in minutes versus years of construction. Contour line 142 covers an area located between the construction years of about 1955 and 2010 and having a downtime of from about 5 to 30 minutes.

Fig. 21 is a diagram of a distribution chart 144 of the year of construction. The chart 144 reveals the number of occurrences of the dwelling versus the age of construction. The graph 144 may have a correspondence to the graph 141 of fig. 20.

FIG. 22 is a graph of a 2D density plot 146 of age of construction versus down time. The downtime is in minutes. The density control 147 of the graph exhibits a downtime between 1955 and 2010 and from about 5 to 30 minutes.

FIG. 23 is a diagram of a graph 149 with a distribution of downtime in terms of number of occurrences versus downtime (in terms of minutes). Graph 149 may have a correspondence with graph 146 in fig. 22.

Fig. 24 is a diagram of a graph 151 showing the distribution of house sizes sorted by efficiency in terms of square feet relative occurrence. Curve 152 represents a low efficiency house and curve 153 represents a high efficiency house.

Fig. 25 is a diagram of a graph 154 showing the distribution of houses built by efficiency classification in terms of the number of relative occurrences of the year of construction. Curve 155 represents a low efficiency house and curve 156 represents a high efficiency house.

Fig. 26 is a diagram of a graph 158 of inefficient houses (i.e., downtime < 14) in terms of years of construction versus house size (in square feet). The density control 159 was presented between 1950 and 2005 and between 1500 and 4000 square feet.

Fig. 27 is a diagram of a graph 161 of high efficiency house (i.e., downtime > 24) in terms of construction years versus house size (in square feet). The density control 162 is presented between 1959 and 2010 and between 1000 and 4000 square feet.

The downtime may be calculated for a 9 week period to record score stability by evaluating the score over the 9 week period. FIG. 28 is a graphical representation of a graph 164 of the distribution of downtime in terms of minutes versus number of occurrences.

FIG. 29 is a diagram of a chart 166 involving the cumulative sum of a series of downtime of each device over a 9 week period from 1 month, 1 day, to 3 months, 3 days. Graph 166 is plotted as a range of downtime per unit (minutes) versus percentage of unit as indicated by curve 167. The 10% device can see a mid-range of more than 35 minutes of down time over a 9 week period, which may look poor. The 50% device can see a mid-range of down time in 10 minutes over a 9 week period, which may look good. The score was formed from 113,335 devices.

The overall range of downtime over a period of 9 weeks may be used as a stability metric. Fig. 30 is a diagram of a graph 169 of a plot 171 of weeks versus downtime (in minutes) for a sample house. The house may be considered to have a stable score. Fig. 31 is a diagram of a graph 173 having a plot 174 of weeks versus downtime (in minutes) for another sample house. The house may be considered to have an unstable score. Fluctuations in curves 171 and 174 may indicate fractional stability, respectively.

A stable and efficient house can be recorded. FIG. 32 is a graph 176 having a curve 177 that appears to have low ripple and is relatively high in terms of downtime versus weeks.

FIG. 33 is a diagram of a graph 179 revealing an equipment run time profile in terms of outdoor temperature (in degrees Fahrenheit) versus run time hours. Fig. 181 shows heating and fig. 182 shows cooling. This data may be from a 1900 square foot house built in 1965 with a fireplace in madison.

Fig. 34 is a diagram of a graph like the graphs in fig. 13 and 14. FIG. 34 is a graph 184 of time versus pellet temperature 106, daily temperature 107, and daily run time temperature 108. This data may relate to the house recorded for fig. 33. Symbols 111, 112, 113, 114, 115, 116, 117, 118, and 119 may represent heat SP, display temperature, outdoor temperature, indoor humidity, daily average heat SP, daily average outdoor temperature, daily average indoor humidity, actual heat RT, and heat RT estimate, respectively.

A stable and inefficient house can be recorded. FIG. 35 is a diagram of a graph 186 having a curve 187 that appears to have low ripple and is relatively low in terms of downtime versus number of weeks.

FIG. 36 is a graph 189 that reveals an equipment run time profile in terms of outdoor temperature (in degrees Fahrenheit) versus run time hours. Fig. 191 shows heating and fig. 192 shows cooling. The data may be 3500 square feet of house built in 2013 with a fireplace, from spring city, colorado.

Fig. 37 is a diagram of a graph like the graphs in fig. 13 and 14. Fig. 37 is a graph 194 of time versus pellet temperature 106, daily temperature 107, and daily run time temperature 108. This data may relate to the premises recorded in fig. 36. Symbols 111, 112, 113, 114, 115, 116, 117, 118, and 119 may represent heat SP, display temperature, outdoor temperature, indoor humidity, daily average heat SP, daily average outdoor temperature, daily average indoor humidity, actual heat RT, and heat RT estimate, respectively.

Fig. 38 is a diagram of a graphical map 196 showing the geographic distribution of stable locations. The houses at these locations have a small range of values in terms of downtime during the first 9 cycles of the year. These houses are in the first quarter because about 27,000 houses have a down time range below 6 minutes. The darker shading on the map indicates the concentration of stable positions.

FIG. 39 is a diagram of a graphical map 198 illustrating a geographic distribution of unstable locations. Houses at these locations have a large range of values in terms of downtime over the first 9 weeks of the year. These houses are in the last quarter because about 27,000 houses have a range of downtime in excess of 20 minutes. Darker shading on the map indicates the concentration of unstable locations.

Briefly summarized, a residential energy efficiency rating system may include a customer data store, a historian data store, a weather data store, a residential structure data store, a residential energy efficiency rating store, and a residential energy efficiency rating calculator coupled to the customer data store, the historian data store, the weather data store, the residential structure data store, and the residential energy efficiency rating store.

Information for the residence may be loaded from the customer data store. Device data may be retrieved from the historical device data store for a predetermined duration for the residence. Other relevant data may be retrieved from the weather data store and the residential structure data store. Heating, ventilation, and air conditioning (HVAC) cycles can be identified during a designated observation window. The HVAC cycle down time cycle duration may be calculated for each relevant cycle.

The HVAC cycle down time duration may be used as a proxy for the rate of temperature change in the home.

The HVAC downtime duration may be calculated when one or more predetermined conditions are met. Examples of the one or more predetermined conditions may include an external temperature range, HVAC system activity, a time at which external influences are minimized, and so forth.

Multiple efficiencies may be calculated over a period of time. The representative efficiency may be selected as the overall efficiency for that period.

The system further comprises: one or more sensors located at one or more residential assets; an aggregation and transmission device connected to the one or more sensors; and a central data receiver and processor connected to the aggregation and transmission means and the historian data store. The one or more sensors may provide collected data regarding the one or more residential assets. The collected data may be provided to the aggregation and transmission device, which transmits the data to the central data receiver and processor.

The collected data to the aggregation and transmission means may be processed and saved in a historical data store as needed. An internet may be connected between the aggregation and transmission means and the central data receiver and processor, such that the collected data to be provided to the aggregation and transmission means may be transmitted to the central data receiver and processor via the internet.

Data from the historical data store may be input to the residential energy efficiency rating (rer) calculator. The output from the rer calculator may go to a rer data Application Program Interface (API) having an output connected to one or more client applications via the internet.

An estimated residential energy efficiency rating mechanism may include: an energy rating data store; a model training processor connected to the energy rating data store; a model parameter store connected to the model training processor; an estimated REER (eREER) calculation processor coupled to the model parameter store; and one or more memories connected to the model training processor and the eREER computation processor.

The one or more stores may be selected from the group comprising: residential structure data storage and consumer demographic data storage.

The mechanism may further include an estimated rer (eREER) data Application Program Interface (API) connected to the eREER computation processor.

The mechanism may also include one or more client applications connected to the eREER data API.

The mechanism may further include an eREER store connected to the eREER computation processor and to the eREER data API; and one or more client applications connected to the eREER computing processor and to the eREER store. A client application may request results on-demand from the eREER calculation processor through the eREER data API, wherein the results are calculated on-the-fly; or the client application may make a request for results of the eREER computation processor that have been previously computed and saved in the eREER store.

The one or more client applications may connect to the eREER data API via the internet.

A method for calculating a residential energy efficiency rating may include: obtaining sensor data regarding a residential asset; aggregating and transmitting the sensor data to a device data receiver and processor; processing the sensor data at the device data receiver and processor; storing the sensor data in a history storage data device; storing a state of a heating, ventilation, and air conditioning (HVAC) system in a history storage device; storing the weather data in a weather storage device; storing the residential configuration data in a configuration data storage device; storing the consumer demographic data in a demographic data store; and calculating a residential energy efficiency rating (rer) for the residential asset from data of one or more items selected from the group consisting of the historical data store, the weather data store, the structural data store, the demographic data store, and a customer data store.

The method may further include storing the calculated rer information in a rer data storage device.

The method may further include monitoring and controlling access to the rer data by one or more client applications having an estimated rer (ereer) data Application Program Interface (API). The rer data may be available to the one or more client applications via the internet.

The sensor data may be obtained from one or more items of the group consisting of an outdoor temperature sensor, an indoor temperature sensor, HVAC status data, a setpoint setting, a connected thermostat, a connected water leak detector, a connected water heater detector, and a connected residence security system detector.

The method may further include inferring a setpoint of the residential asset or a state of an HVAC system from the temperature data; and storing the state or setpoint of the HVAC system in the history storage.

Any publications or patent documents noted herein are hereby incorporated by reference to the same extent as if each individual publication or patent document was specifically and individually indicated to be incorporated by reference.

In this specification, some of the subject matter may be of a hypothetical or prophetic nature, although set forth in another manner or tense.

Although the present systems and/or methods have been described with respect to at least one illustrative example, many variations and modifications will become apparent to those skilled in the art upon reading the present specification. It is therefore the intention that the appended claims be interpreted as broadly as possible in view of the relevant art to include all such variations and modifications.