Patent Application
20120216123
The present disclosure relates to the field of energy auditing, and more particularly, to providing an online energy audit service to encourage energy efficiency improvements.
Home energy audits are typically designed to identify energy inefficiencies in homes. Based on the result of an energy audit, certain energy efficiency improvements may be made. Traditionally, energy audits are performed by a professional auditor who visits and physically examines a home. While in-home energy audits are likely to yield accurate measurements of the energy use of a home, they also tend to be costly. In addition, the recommendations made by an in-home auditor may be perceived as biased due to the auditor's affiliations.
Online energy audits provide a cheaper and more convenient alternative to in-home audits. For example, online energy audits may be completed for free by a homeowner at his or her leisure without involving a professional auditor. In addition, recommendations made by an online energy audit may be perceived as unbiased because the person conducting the audit is the homeowner.
However, existing online energy audit tools have failed to lead to significant energy efficiency improvements. One contributing factor to this failure may be a low completion rate of online energy audits due to both the complexity and length of the questions presented in the audits. Another contributing factor may be the lack of adequate incentives to adopt energy efficiency improvements upon the completion of an audit.
a-c illustrate an exemplary survey UI showing conditional survey-question presentation, in accordance with one embodiment.
a-b illustrate a survey UI showing a survey question with individually displayed image-based answer options, in accordance with another embodiment.
In according with various embodiments, an online energy audit system and method is provided to pose and collect responses to a list of survey questions regarding a subject house via a survey UI from a remote occupant. The survey responses are stored in a subject-home energy-use profile associated with the subject house and are used to populate model inputs to an energy-use software model, from which an energy-efficiency score is derived. To help a remote occupant choose the appropriate answers and facilitate the completion of the survey, the survey UI includes question-specific house-feature images associated with each question. In addition, the survey questions are designed to be simple and easy-to-understand, and the survey is kept as short as practicable. An energy-efficiency score of the subject house is presented to the remote occupant in comparison with energy-use data for similar houses, together with an action message to encourage the remote occupant to improve the energy score of the subject house.
As used herein, the terms “survey” and “audit” are used interchangeably. Similarly, the terms “energy consumer,” “remote occupant,” and “homeowner” are used interchangeably to refer to the person who uses the online energy auditing service to assess the energy efficiency of a subject house.
The phrases “in one embodiment,” “in various embodiments,” “in some embodiments,” and the like are used repeatedly. Such phrases do not necessarily refer to the same embodiment. The terms “comprising,” “having,” and “including” are synonymous, unless the context dictates otherwise.
Reference is now made in detail to the description of the embodiments as illustrated in the drawings. While embodiments are described in connection with the drawings and related descriptions, there is no intent to limit the scope to the embodiments disclosed herein. On the contrary, the intent is to cover all alternatives, modifications and equivalents. In alternate embodiments, additional devices, or combinations of illustrated devices, may be added to, or combined, without limiting the scope to the embodiments disclosed herein.
In various embodiments, energy audit server 200 and/or database 120 may comprise one or more physical and/or logical devices that collectively provide the functionalities described herein. In some embodiments, energy audit server 200 and/or database 120 may comprise one or more replicated and/or distributed physical or logical devices. In some embodiments, energy audit server 200 may comprise one or more computing services provisioned from a “cloud computing” provider, for example, Amazon Elastic Compute Cloud (“Amazon EC2”), provided by Amazon.com, Inc. of Seattle, Wash.; Sun Cloud Compute Utility, provided by Sun Microsystems, Inc. of Santa Clara, Calif.; Windows Azure, provided by Microsoft Corporation of Redmond, Wash., and the like. In some embodiments, database 120 may comprise one or more storage services provisioned from a “cloud storage” provider, for example, Amazon Simple Storage Service (“Amazon S3”), provided by Amazon.com, Inc. of Seattle, Wash., Google Cloud Storage, provided by Google, Inc. of Mountain View, Calif., and the like.
In various embodiments, network 150 may include the Internet, a local area network (“LAN”), a wide area network (“WAN”), a cellular data network, and/or other data network.
In various embodiments, energy provider device(s) 110 are operated by a provider of gas, electricity, oil, renewable energy, or the like. Examples of an energy provider include FirstEnergy Corporation of Akron, Ohio; Chesapeake Energy of Oklahoma City, Okla.; Exxon Mobile Corporation of Irving, Tex., and the like. In various embodiments, an energy provider device 110 may provide energy-usage data including cost of energy, energy consumed by certain households, statistical data, and the like. In some embodiments, an energy provider device 110 may also provide information about services provided, energy incentives programs (e.g., rebates), and the like.
In various embodiments, energy consumer device(s) 115 may include a desktop PC, laptop 115A, mobile phone 115B, tablet, or other computing device. In various embodiments, an energy consumer device 115 is connected to network 150 and includes a web browser.
In various embodiments, a government energy program server 125 is operated by a government agency such as Department of Energy, a state, or the like. In various embodiments, government energy program server 125 may provide information and/or statistics related to energy-usage, energy incentives programs (e.g., tax rebates and credits), and the like.
As shown in
Processing server 200 also communicates via bus 220 with database 120 (see
In block 305, routine 300 obtains an energy-use software model 260. As used herein, the term “energy-use software model” refers to a computer software program that is capable of simulating or predicting the energy use of a building based on a set of model inputs. Examples of an energy-use software model include the SIMPLE Model, provided by M. Blasnik and Associates of Boston, Mass. In various embodiments, model inputs correspond to one or more “house characteristics.” As used herein, the term “house characteristic” refers to information about a house that is relevant to determining the energy use of the house.
For example,
Referring again to
In some embodiments, a survey question may be associated with a relatively small number of predetermined answer options. For example, a survey question asking about the foundation type of a house may be associated with three answer options, such as “Slab,” “Basement,” and “Crawl space.” In such embodiments, a remote occupant may select one of the answer options in response to the survey question. In other embodiments, a survey question may be associated with many answer options. In some embodiments, a remote occupant may be required to enter an alphanumeric response (e.g., zip code, year, or the like). Typically, such a survey response may be restricted by one or more constraints associated with the survey question. For example, a survey question asking for the zip code of a house may have the constraint that the response must be a valid zip code in the U.S.
In subroutine block 400, routine 300 uses subroutine 400 (see
In block 315, routine 300 stores (e.g., in database 120) survey responses, such as those collected above, in a subject-house energy-use profile associated with a subject house. Routine 300 ends in 399.
In block 410, subroutine 400 presents the survey question to the remote occupant in a survey UI. In various embodiments, subroutine 400 provides client-side instructions (e.g., Hypertext Markup Language, JavaScript, and the like) necessary for presenting survey questions and for collecting survey responses in a web browser on energy consumer device 115.
In block 415, subroutine 400 obtains answer options associated with the survey question. In decision block 416, subroutine 400 determines whether there are any question-specific house-feature images associated with the answer options. In various embodiments, an answer option may be associated with one or more question-specific house-feature images, where each house-feature image depicts a representative house feature corresponding to a predetermined answer option. Such house-feature images are typically provided to help a remote occupant select an appropriate answer option for the current question. For example, a house-feature image may depict a house feature in an easy-to-understand and/or interesting way (see
If there are one or more image-based answer options associated with the current survey question, then beginning in opening loop block 420, subroutine 400 processes each answer option associated with the current survey question in turn. In block 425, subroutine 400 obtains an image corresponding to the answer option. In block 430, subroutine 400 presents the house-feature image to a remote occupant in the survey UI. In closing loop block 435, routine 400 iterates back to opening loop block 420 to process the next answer option associated with the current survey question, if any.
Otherwise, if there is no image-based answer option associated with the current question, then in block 417, subroutine 400 provides answer-option selection controls to the remote occupant via the survey UI. In some embodiments, subroutine 400 may provide answer option selection controls that do not require alphanumeric data entry (e.g., links, buttons, check boxes, dropdown lists, and the like). For example, subroutine 400 may provide a map control that a remote occupant may click on to select a zip code or other location-related data for a subject house. For another example, subroutine 400 may provide a thermostat control for a remote occupant to select a heating set point for a subject house.
In other embodiments, where there are many answer options associated with a question (e.g., zip code, year built, and the like), subroutine 400 may provide answer option selection controls that require alphanumeric data entry (e.g., text boxes) for a remote occupant to enter an alphanumeric response (e.g., using a keyboard).
In some embodiments, the survey UI may present no answer option selection controls that require alphanumeric data entry. In such embodiments, a remote occupant taking the survey does not need to type anything to respond to the survey questions. In other embodiments, survey UI may, for some questions, provide alphanumeric data entry controls for a remote occupant to enter an alphanumeric response via a keyboard.
In block 440, subroutine 400 obtains a survey response corresponding to the current survey question. In some embodiments, subroutine 400 may check the validity of a survey response. For example, subroutine 400 may check that a valid U.S. zip code is entered as a response to a survey question asking for the zip code of a house. In other embodiments, subroutine 400 may provide a default survey response if a remote occupant does not enter a survey response.
FIGS. 10 and 12-13 illustrate exemplary survey UIs showing survey questions and answer options, in accordance with various embodiments.
a-b illustrate a survey UI showing a survey question with individually displayed image-based answer options, in accordance with one embodiment. In the illustrated embodiment, the image displayed is changed dynamically to correspond to the selected answer option. For example, when answer option “No insulation” 1330 is selected (as shown in
Referring again to
If the survey is determined to be completed, then subroutine 400 returns collected survey response(s) in ending block 499. Otherwise, if survey is not yet completed, then in block 450 subroutine 400 selects the next question to present to the remote occupant. In some embodiments, subroutine 400 may select the next question based on at least one prior survey response. For example, subroutine 400 may select different questions about the heating system depending on the remote occupant's response about the type of energy (e.g., gas, electric, or oil) used by the heating system. In other embodiments, subroutine 400 may select the next question based on a predetermined order.
a-c illustrate an exemplary survey UI that presents survey questions based on the response to a previous question, in accordance with one embodiment. For the illustrated survey question 1105 regarding the type of foundation of a house, if the remote occupant selects answer option “Slab” 1130, then no further question is displayed, as shown in
Referring again to
For example, subroutine 400 may determine not to ask a question about the type of wall insulation if the house vintage indicates that the house was built after 1980, but to assume that the wall insulation is of a standard type. For another example, subroutine 400 may determine not to ask a question about roof type (e.g., reflective or standard) if the zip code provided by the remote occupant indicates that the house is located in a cool climate. For yet another example, subroutine 400 may determine not to ask a question related to air ducts in basement or crawl space (e.g., percentage of air ducts in attic) if the remote occupant indicates that the house does not have a basement or crawl space.
If in decision block 455, subroutine 400 determines to ask the question selected in block 450, then subroutine 400 loops back to block 410 to present the survey question to the remote occupant. Otherwise, if subroutine 400 determines not to ask the present question, then subroutine 400 loops back to decision block 445 to determine whether the survey is completed. If the survey is completed, as described above, subroutine 400 returns collected survey responses in ending block 499. Otherwise, subroutine 400 selects the next question in block 450, as described above.
In subroutine block 600, routine 500 uses subroutine 600 (see
In subroutine block 700/800, routine 500 uses a subroutine such as subroutine 700 (see
In block 510, routine 500 determines a relationship between the subject house and the comparison energy-use data. In various embodiments, the relationship may include a mathematical relationship (e.g., greater than, less than, and the like) between the energy-efficiency score of the subject house and the energy-efficiency score of a comparison house, a ranking of the energy-efficiency scores of the subject house and the comparison houses, and the like.
In block 515, routine 500 selects an action message to encourage the remote occupant taking the survey to improve the energy-efficiency score of the subject house. In various embodiments, an action message is selected based at least in part on the energy-efficiency score of the subject house and/or the relationship between subject house and comparison energy-use data. For example, an action message may include information such as the following:
In various embodiments, the action message is selected to encourage a remote occupant completing the survey to improve the energy-efficiency score of the subject house. In some embodiments, the action message is selected based on behavioral economics. For example, the action message may provide social (e.g., “You are contributing to global warming.”), psychological and/or economic incentives for a homeowner to take action (e.g., “Your energy-efficiency score is less than your neighbors' homes.”, “Your estimated 3 year savings is $4,011 if you improve your energy efficiency.”, or the like).
For another example, the action message may present a remote occupant with concrete, manageable, and customized action items or projects that a remote occupant can easily take on (e.g., “Improve attic insulation to modern standards. This will add 13 points to your energy-efficiency score.”).
In block 520, routine 500 provides a comparison UI to present the energy-efficiency score of the subject house in relation to the comparison energy-use data and/or the action message. In various embodiments, the comparison UI is provided to encourage a remote occupant to improve the energy efficiency of the subject house. For example, comparison UI may include a graphical depiction of the energy-efficiency score, its relationship with comparison energy-use data, potential energy savings (e.g., in utility bills) if the remote occupants were to make improvements, and the like, to facilitate decision-making. In various embodiments, potential energy savings may be calculated based at least in part on energy cost, applicable energy rebate, tax credit, and other incentive programs offered by governments and/or service providers, and the like. For another example, the comparison UI may have a simple design (e.g., one page, easy-to-understand message, and the like) to make it easy for a remote occupant to take action. In block 520, routine 500 provides the comparison UI, such as described above, to energy consumer device 115 to be rendered in a web browser. Routine 500 ends in block 599.
For example,
Comparison UI 1400 also includes a ranking 1410 of the energy-efficiency score of the subject house in relation to energy-efficiency scores of other comparison houses and an action message 1415 encouraging a remote occupant to take action. Comparison UI 1400 further includes an “Improve my score” button 1420 which would take a remote occupant to another UI with, e.g., a list of available energy contractors/vendors within the subject house's zip code.
In addition, comparison UI 1400 includes a “Your Customized Action Plan” section where an action message presents the remote occupant with options 1425A-B to improve energy efficiency for specific aspects of the subject house, e.g., air sealing and ventilation, attic insulation, and the like. Each option may be associated with an improved-home energy-efficiency score (discussed below). When a remote occupant selects a particular energy efficiency option (e.g., by clicking on the “Get Started” button), the remote occupant may be taken to another UI with, e.g., a list of available energy contractors/vendors for that particular option.
In block 610, subroutine 600 determines the model inputs associated with an energy-use software model (e.g., see item 260 of
For example,
Referring again to
If it is determined that the current model input is not associated with any survey response from the subject-house energy-use profile, then in block 640 subroutine 600 obtains a constant input value and in block 645, subroutine 600 populates the model input with the obtained constant input value. In various embodiments, a constant input value is selected from one or more predetermined possible input values corresponding to the current model input and is used to reduce the quantity of survey responses that need to be collected to populate the model inputs of an energy-use software model (e.g., item 260 of
In some embodiments, subroutine 600 may automatically obtain energy-usage data associated with the subject house from an energy-provider device 110. For example, such energy-use data may include cost of energy, utility data for the subject house or houses in the same zip code, and the like. In some embodiments, subroutine 600 may further obtain constant input values corresponding to model inputs by selecting among a range of predetermined values based at least in part on the automatically-obtained energy-usage data. For example, subroutine 600 may set the constant input value corresponding to a model input related to “Electrical load other than lighting” to “Average” if automatically-obtained energy-usage data indicates that electrical usage other than lighting associated with the subject house and/or similar houses is about average.
Otherwise, if it is determined, in decision block 620, that the current model input is associated with one or more survey responses from the subject-house energy-use profile, then in decision block 625, subroutine 600 determines whether the one or more survey responses map directly to the current model input. In various embodiments, a survey response maps directly to a model input when there is a one-to-one correspondence between possible survey responses and possible model input values.
For example, Table 2 illustrates exemplary model inputs whose input values are directly mapped to survey responses, in accordance with one embodiment. For example, in one embodiment, a survey response to a question about the finished floor area of a house is mapped directly to a corresponding numeric value for a model input related to “Finished floor area.” For another example, one of each of the three possible survey responses selected for the question regarding the foundation type of a house is mapped directly to corresponding model input value for a model input related to “Foundation type.”
If it is determined in decision block 625 that there is a direct mapping between survey responses and model input values for the current model input, then in block 650 subroutine 600 obtains the model input value corresponding to the survey response associated with the model input in block 650 and populates the model input with the corresponding input value in block 655.
Otherwise, if it is determined in decision block 625 that there is no direct mapping between survey responses and model input values for the current model input, then in block 630 subroutine 600 infers an input value based on one or more survey responses. In various embodiments, a model input value may be inferred to facilitate the completion of the survey. For example, a model input value may be inferred when a remote occupant is unsure about an input value and/or to reduce the number of questions that need to be asked in the survey.
In some embodiments, a model input value may be inferred based on a survey response to a survey question that is directly related to the model input. For example, Table 3 illustrates exemplary model inputs whose values may be inferred from directly-related survey responses, in accordance with one embodiment. For example, the numeric input value for model input related to “AC seer” may be inferred from the survey response to the question of “What best describes your home's primary air conditioner?” For another example, the numeric input value for model input related to “Attic insulation” may be inferred from the survey response to the question of “How much attic insulation do you have?”
In various embodiments, inferred input values represent a subset of all possible model input values. For example, the inferred input values {9, 11, 13, 0} for model input related to “AC seer” may represent a subset of possible model input values {0-26} associated with the model input.
In various embodiments, inferred values may be used to facilitate a remote occupant selecting an appropriate response to a survey question. For example, an ordinary homeowner may not understand what an “AC seer rating” is for the house's AC. However, a homeowner is more likely to know how old an air conditioner is. Therefore, an inference between the age and the seer rating of an air conditioner, such as illustrated in Table 3, allows a survey to ask a relatively simple question (e.g., age of an AC) for a relatively abstruse model input (e.g., AC seer rating).
In other embodiments, a model input value may be inferred based on a survey response to a survey question that is indirectly related to the model input. Indirectly-related survey responses may include the vintage of a house, the zip code of a house, and the like. In some embodiments, subroutine 400 may select, based at least in part on the indirectly-related survey responses (e.g., vintage of a subject house), an input value for one of model inputs from a group of varying input values.
Table 4 illustrates exemplary inferred model input values based on indirectly-related survey responses, in accordance with one embodiment. For example, the wall insulation of a house may be inferred from the vintage of the house. In this example, if the house vintage indicates that the house is built after 1980, then the model input value for model input related to “Wall insulation” is inferred to be “Standard Insulation.” For another example, if the foundation type of the house is not “Crawl space” or “Basement,” then the model input value for a model input related to “% Duct in attic” may be inferred to be “75%.”
In some embodiments, a model input value may be inferred based on multiple (directly and/or indirectly related) survey responses. For example, an inferred value may be selected, based at least in part on the vintage of a subject house, from a group of varying input values. Table 5 illustrates, in an exemplary embodiment, how the model input value for model input related to “Air tightness” may be inferred from a group of varying input values based on both the house vintage (indicated by the leftmost column of Table 5) and response to the survey question of “How drafty does your home feel?” (indicated by the top row of Table 5).
In other embodiments, multiple model input values may be inferred from a single response to a survey question. For example, Table 6 illustrates, in an exemplary embodiment, how exemplary model input values for model inputs related to “Duct leakiness” and “Duct insulation” may be inferred from the response to the question of “What type of ducts are in your home.”
In some embodiments, multiple model input values may be inferred based on multiple survey responses questions. For example, Table 7 illustrates, in an exemplary embodiment, how model input values for model inputs related to “% Duct in attic” and “% Duct in crawl space” may be inferred from two survey responses, foundation type and whether there are ducts in attic (which, in one embodiment, may be inferred from a response to a question asking whether there are ceiling vents in the house). This example also illustrates that multiple model input values maybe inferred from one survey response (e.g., when foundation type is “Crawl space” or “Slab”), thus reducing the number of questions that need to be asked.
In some embodiments, a model input value may be inferred from data that is not directly obtained from the remote occupant completing the survey. For example, Table 8 illustrates how the model input value for “Attic insulation” may be inferred from the house vintage and Heat Degree Day (HDD) value for the house, in accordance with one embodiment. An HDD is a measurement of energy requirement to heat a given building. Generally, the heating requirements for a given structure at a specific location are considered to be proportional to the number of HDD at that location. HDDs are typically defined relative to a base outside temperature above which heating is not required. One popular approximation method of HDD is to take the average temperature on any given day, and subtract it from the base temperature. If the value is less than or equal to zero, that day has zero HDD. But if the value is positive, that number represents the number of HDD on that day. For illustrative purpose, the HDD in Table 8 represent the annual HDD for a given house. In this embodiment, when a remote occupant is unsure about the type of attic insulation in the house, a numeric insulation R-value may be inferred from a combination of the vintage of the house and the HDD of the house as shown in Table 8.
As discussed above, in various embodiments, model input values may be inferred from data (e.g., HDD value) that is not directly obtained from the remote occupant. In some embodiments, such data may be obtained from or based on data from external sources such as energy providers, government agencies (e.g., Department of Energy, Census Bureau, and the like), climate data providers, and the like. In other embodiments, such data may be calculated based data obtained from a remote occupant (e.g., zip code, vintage of a house, heating setpoint, and the like).
In various embodiments, inferred model input values may be used to reduce the quantity of survey responses that need to be collected to populate a given model input (see discussion of Table 4, Table 6, and Table 7, above) and/or to facilitate the remote occupant entering the proper response (see discussion of Table 3, Table 5, and Table 8, above).
Still referring to
In closing loop block 660, subroutine 600 iterates back to opening loop block 615 to process the next model input, if any.
Once all model inputs have been processed, in block 665, subroutine 600 obtains an energy-efficiency score based on output from the energy-use software model with populated model inputs. In various embodiments, an energy-use software model is populated with model inputs as described above. Further, in some embodiments, an energy-use software model may also be populated with additional data obtained from sources other than remote occupant. For example, an energy-use software model may be populated with climate data for the subject house's zip code location from a weather service provider, energy-usage data (e.g., utility cost, usage statistics, and the like) from an energy provider, and the like.
In various embodiments, an energy-efficiency “score” may be derived at least in part from an energy-usage estimate provided by an energy-use software model. For example, in some embodiments, an energy-usage estimate may be expressed in units of energy consumed, such as in million British thermal unit (“MBtu”). In some other embodiments, an energy-usage estimate may be expressed as the amount of money spent on energy, such as in U.S. dollar. In yet other embodiments, an energy-use software model may provide an energy-efficiency score instead of or in addition to an energy-usage estimate.
As described above, an energy-efficiency score indicates the energy efficiency of a subject house. In some embodiments, an energy-efficiency score may be computed by comparing the energy-usage estimate of the subject house with the energy-usage estimate of an idealized house (see discussion of
In various embodiments, comparison energy-use data may be used to generate an efficiency score a subject house, an action message for the remote occupant of the survey, and the like. As described above, in various embodiments, comparison energy-use data may include the energy-efficiency score of an actual or hypothetical comparison house, energy-efficiency scores of a collection of comparison houses similar to the subject house, and the like.
In ending block 699, subroutine 600 returns the energy-efficiency score.
In block 705, subroutine 700 selects one or more predetermined “improvable” house characteristics from the house characteristics associated with a subject house. Typically, an “improvable” house characteristic is one that a homeowner is more likely to modify to improve the energy efficiency of the subject house. Examples of improvable house characteristics may include insulation, HVAC, windows, appliances, energy usage, and the like. In contrast, a homeowner may be unlikely to modify a “non-improvable” house characteristic to improve the energy efficiency of the house. Examples of non-improvable house characteristics may include the location of the house, the number of stories, the number of occupants, foundation type, and the like. For example,
In some embodiments, subroutine 700 identifies a home-improvement package offered by a vendor and selects one or more improvable characteristics that correspond to the home-improvement package. In various embodiments, a home-improvement package may be designed to improve some or all aspects of home energy use, including insulation, HVAC, weatherization, appliances, and the like. In various embodiments, vendors of home-improvement packages may include energy providers, contractors, manufacturers, retailers, and the like. In some embodiments, a vendor may be partnered with the energy audit service. For example, if a partner contractor provides HVAC services, subroutine 700 may identify improvable characteristics corresponding to HVAC of the subject house such as water heating type, AC seer rating, and the like. For another example, if a partner energy provider provides natural gas, subroutine 700 may identify improvable characteristics corresponding to the type of energy used for the house such as for heating, water heater, clothes dryer, cooking, and the like.
In block 710, subroutine 700 selects one or more non-improvable survey responses, from the subject-house energy-use profile, corresponding to one or more non-improvable house characteristics (e.g., vintage and location of the house, number of stories, number of occupants, foundation type, and the like).
In block 715, subroutine 700 obtains one or more idealized responses corresponding to the one or more improvable characteristics selected above. In various embodiments, an idealized response represents what the response would have been for a more energy efficient house, or an “idealized house”. As used herein, an “idealized house” refers to a house with improved energy efficiency for some or all improvable house characteristics of the subject house. For example, if the subject house currently uses an electric water heater but it is determined (based on the cost of electricity and gas, for example) that a gas heater would be more energy efficient, an idealized response to the “water heater type” house characteristic would be “gas.”
In block 720, subroutine 700 generates an “improved-home energy-use profile” for the subject house. In various embodiments, an “improved-home energy-use profile” is generated according to the one or more non-improvable responses of the subject house and the one or more idealized responses corresponding to the selected improvable characteristics of an idealized house, discussed above. For example, an improved-home may have the same vintage, number of stories, number of occupants, and foundation type, but may have better duct insulation and/or air sealing than the subject house.
In block 725, subroutine 700 obtains an energy-efficiency score for the improved-home energy-use profile. In various embodiments, block 725 is performed via an energy-use software model such as item 260 of
In ending block 799, subroutine 700 returns comparison energy-use data including the energy-efficiency score for the improved-home energy-use profile. In some embodiments, subroutine 700 may generate one or more improved-home energy-use profiles, and hence energy-efficiency scores, corresponding to one or more home-improvement packages offered by partner vendors. In some embodiments, such improved-home energy-use profiles and energy-efficiency scores may be used to generate a customized action plan for a remote occupant (see e.g., items 1425A-B of
In block 805, subroutine 800 selects a collection of comparison houses similar to the subject house. In various embodiments, selected comparison houses may be actual or hypothetical. In some embodiments, selected comparison houses may be similar to the subject house with respect to one or more of size, location, and vintage. For example, comparison houses may include actual houses with the same zip code as the subject house or houses with similar vintage and/or size as the comparison house. For another example, comparison houses may include idealized houses (see
Beginning in opening loop block 810, subroutine 800 processes each comparison house in turn.
In block 815, subroutine 800 obtains an energy-efficiency score of the current comparison house. In various embodiments, an energy profile is generated for the comparison house and an energy-efficiency score is obtained by feeding the comparison-house energy profile into an energy-use software model (e.g., item 260 of
In closing loop block 820, subroutine 800 iterates back to opening loop block 810 to process the next comparison house, if any.
In ending block 899, subroutine 800 returns comparison energy-use data including the energy-efficiency scores for the comparison houses. As discussed in relation to block 510 of
Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that alternate and/or equivalent implementations may be substituted for the specific embodiments shown and described without departing from the scope of the present disclosure. This application is intended to cover any adaptations or variations of the embodiments discussed herein.
This application claims the benefit of priority to U.S. Provisional Application No. 61/446,025, filed Feb. 23, 2011, titled “ADAPTIVE VISUAL HOME ENERGY PROFILE METHOD AND SYSTEM,” filed under Attorney Docket No. EVOW-2011002, and naming the following inventors: Leo Shklovskii, Aaron Goldfeder, Scott Case, and Michael Blasnik. This application also claims the benefit of priority to U.S. Provisional Application No. 61/446,028, filed Feb. 23, 2011, titled “HOME ENERGY PROFILE MODIFICATION BASED ON MULTI-VARIABLE ENERGY EFFICIENCY RECOMMENDATIONS AND SOCIALLY RELATIVE COMPARISONS METHOD AND SYSTEM,” filed under Attorney Docket No. EVOW-2011003, and naming the following inventors: Leo Shklovskii, Aaron Goldfeder, Scott Case, and Michael Blasnik. The above-cited applications are incorporated herein by reference in their entireties, for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 61446025 | Feb 2011 | US | |
| 61446028 | Feb 2011 | US |
