Patent Application
20230392941
The present disclosure relates generally to a system and corresponding method for operating a personal energy consumption account for a user. The use of fossil fuels and various other products causes the release of greenhouse gases, which poses a tremendous challenge to humanity. This is a large-scale problem, with multiple facets. Quantifying an individual's actions and behavior in terms of its effect on the environment is not a trivial matter. Accordingly, it may be difficult for an individual to understand the effect of their daily actions and behavior on climate issues such as global warming.
Disclosed herein is a system for operating a personal energy consumption account for a user. The system includes a mobile application adapted to interface with the user and provide access to the personal energy consumption account to the user. A controller is in communication with the mobile application, the controller having a processor and tangible, non-transitory memory on which instructions are recorded. The controller is configured to obtain respective activity data of the user in a plurality of domains. The controller is configured to calculate respective carbon dioxide emissions for the plurality of domains. The plurality of domains is grouped into one or more energy clusters each defining a respective range of carbon dioxide emissions. The controller is configured to determine a respective score for the one or more energy clusters based in part on the respective carbon dioxide emissions, a respective average population consumption value and a respective energy budget. The controller is configured to generate recommendations for the user to improve the respective score.
The mobile application may be embedded in a smart device belonging to the user. In some embodiments, the mobile application is accessible through a communications interface in a vehicle. The controller may be configured to represent the respective score as a visual graphic in the mobile application. In one embodiment, the visual graphic includes a tree such that the respective score is proportional to a number of leaves on the tree and/or a respective color of the leaves.
The controller may be configured to generate a user model based in part on the respective activity data of the user in the plurality of domains, the user model being a machine-learning model. The controller may be configured to personalize the recommendations based in part on the user model and selectively display the personalized recommendations in the mobile application.
In some embodiments, at least sensor is positioned on a respective item employed by the user in one of the plurality of domains. The respective item may be a recycling bin used by the user and the at least sensor is a weighing device operatively connected to the recycling bin. The plurality of domains may be selected by the user. The respective score may be determined for a current energy consuming activity in one of the plurality of domains.
Disclosed herein is a method of operating a system of personal energy consumption account for a user. The system has a controller with a processor and tangible, non-transitory memory on which instructions are recorded. The method includes a mobile application adapted to interface with the user and provide access to the personal energy consumption account. Respective activity data of the user is obtained in a plurality of domains, via the mobile application. The method includes calculating respective carbon dioxide emissions for the plurality of domains, via the controller. The plurality of domains is grouped into one or more energy clusters each defining a respective range of carbon dioxide emissions, via the controller. The method includes determining a respective score for the one or more energy clusters based in part on the respective carbon dioxide emissions, a respective average population consumption value and a respective energy budget, via the controller. The method includes generating recommendations for the user to improve the respective score.
The above features and advantages and other features and advantages of the present disclosure are readily apparent from the following detailed description of the best modes for carrying out the disclosure when taken in connection with the accompanying drawings.
Representative embodiments of this disclosure are shown by way of non-limiting example in the drawings and are described in additional detail below. It should be understood, however, that the novel aspects of this disclosure are not limited to the particular forms illustrated in the above-enumerated drawings. Rather, the disclosure is to cover modifications, equivalents, combinations, sub-combinations, permutations, groupings, and alternatives falling within the scope of this disclosure as encompassed, for instance, by the appended claims.
Referring to the drawings, wherein like reference numbers refer to like components,
Referring to
Personal energy consumption has an accumulative effect on our environment. It is difficult for humans to quantify the effect of their everyday actions on global warming, such as driving for a short trip to the store, turning on an air-conditioner or planting a tree. The system 10 facilitates maintenance of the personal energy consumption account 14 over combinations of the daily activities of the user 12. The advantage is that a large-scale problem is translated into personal low-level actions, conveying to the user 12 the effect of their decisions on the environment. This incentivizes the user 12 to prefer sustainable solutions over non-environmental ones.
Referring to
As described below, the controller C is configured to obtain respective activity data of the user 12 in a plurality of domains 24, as shown in
The controller C of
The wireless network 52 of
Referring now to
Method 100 may be executed in three stages. The first stage (blocks 102 and 104) includes the setting up of domains and collection of user data. The second stage (blocks 106 through 112) includes the computing of emissions and generation of scores for each energy cluster. The third stage (blocks 114 through 120) may include the development of concrete recommendations personal to the user 12 based on analysis of the data gathered.
Beginning at block 102 of
In one embodiment, the food domain 26 may include the following sub-domains: the composition of the diet of the user 12 such as food purchased from restaurants, canteens and takeaways and home-cooked foods, the presence of local versus imported foods in the diet of the user 12 and food waste (estimated by weight, e.g., an estimate of waste thrown in the garbage). The sub-domains in the travel domain 28 may include the following sub-domains: the vehicle 20 owned by the user 12, the type of vehicle 20 (e.g., sport, sedan, SUV), whether the vehicle 20 is fully or partially electric or has an internal combustion engine, the mileage and energy consumption particulars of the vehicle 20, airline flights taken by the user 12, green mobility habits such as cycling, walking, car-sharing rides, and mileage/time spent using public transportation.
The home domain 30 may include the following sub-domains: number of people living at the home of the user 12, heating mechanism in the home (e.g., electricity, wood, oil etc.), electricity saving equipment and habits, the average home temperature in winter and summer, the presence or absence of solar heating or solar panels that convert sun energy to electricity and the installation of energy efficiency improvement devices. The miscellaneous domain 32 may include the following sub-domains: recycling habits (amount and type of material recycled) of the user 12, purchase of household items (frequency and type), purchase of clothes and footwear, and the purchase of health and beauty items.
Also, per block 102, the user 12 may be asked to establish settings such as whether the respective activities in the plurality of domains 24 is automatically recognized or needs to be entered manually through the mobile application 16. The mobile application 16 may further ask the user 12 (via the mobile application 16) to set a predetermined time period (e.g., daily or weekly or monthly) for receiving the respective score 42 (to be determined in block 110). The profile of the user 12 may be stored in the form of a barcode or machine-readable optical label. The barcode may be based on a radio-frequency identification (RFID) system that uses electromagnetic fields for identification and tracking.
Advancing to block 104 of
Proceeding to block 106, the controller C is adapted to identify and automatically monitor user activity in the plurality of domains 24 (selected by the user 12) in block 102 in real time. For the home domain, the controller C may be adapted to monitor the electricity metering at the home of the user 12 and deduce the consumption of various home appliances. The home appliances in the home of the user 12 may be registered with the mobile application 16. For the travel domain 28, the controller C may be adapted to compare GPS data with speed and time difference, to deduce details transportation data. For the travel domain 28, the controller C may be configured to obtain data from a vehicle tracking application running in the vehicle 20. The vehicle tracking application may track distance traveled, speed profile, driving style (e.g., frequency of brake application), and amount and type (fuel or electric) of energy consumed.
In some embodiments, the controller C is adapted to obtain the respective activity data of the user 12 using various sensors attached to respective items used by the user 12. Referring to
Advancing to block 108 of
Proceeding to block 110 of
The respective energy budget (B) reflects an idealized emission amount, in other words, what the user 12 may ideally spend energy-wise in that category. The respective energy budget (B) and resolution factor (K) may be selected by the controller C for each of the energy clusters. The system resolution (K) may be chosen to obtain a desired score resolution or spacing between the levels of allowed scores, depending on the order of magnitude of the budget (B). For example, if the respective energy budget (B) and the resolution factor (K) are 8 metric tons and 6 metric tons, respectively, then σ=CEILING (8/6)=1.
The score given to the user 12 may be discretized for a given budget for energy consumption, for a given domain 24. As shown in Table 1 below, a current score may be obtained by comparing the difference (UE−UPE) between the user energy consumption value (UE) for the user 12 and the average population consumption value (UPE) and the sigma value (α) for each energy cluster. The system 10 may be designed to allow a specific number of levels of allowed score. In the example shown in the table below, the score is designated to be an integer value in the range of positive 3 to negative 3 as follows: [+3, +2, +1, 0, −1, −2, −3]. The current score may be accumulated with previous scores given on past activities to obtain the respective score 42. Here ε is a value between zero and σ such that 0≤ε<σ. TABLE 1
Advancing to block 112 of
Proceeding to block 114 of
Advancing to block 116 of
Proceeding to block 118 of
In one example, the user model 44 incorporates a neural network with an activation function in an output layer, the activation function predicting a multinomial probability distribution. As understood by those skilled in the art, neural networks are designed to recognize patterns from real-world data (e.g., images, sound, text, time series and others), translate or convert them into numerical form and embed in vectors or matrices. The neural network may employ deep learning maps to match an input vector x to an output vector y. In other words, the neural network learns an activation function f such that f(x) maps toy. The training process enables the neural network to correlate the appropriate activation function f(x) for transforming the input vector x to the output vector y. In the case of a simple linear regression model, two parameters are learned: a bias and a slope. The bias is the level of the output vector y when the input vector x is 0 and the slope is the rate of predicted increase or decrease in the output vector y for each unit increase in the input vector x.
Advancing to block 120 of
The recommendations 46 may include specific recommendations of sustainable and eco-friendly individual behaviors, such as carpooling, cycling, or recycling. For example, a user 12 may be found to make multiple short trips on Fridays. The mobile application 16 may provide the following personalized recommendation 46; “Consider biking on Fridays to help reduce your CO2 emissions. On Fridays, it seems like you can bike short distances.” Similarly on weekdays, the mobile application 16 may recommend that the user 12 leave work one hour later to reduce their respective carbon dioxide emissions 40. The personalized recommendations 46 improve the balance between high-energy and low-energy consumption activities of the user 12.
In summary, the system 10 establishes and maintains a personal energy consumption account 14 for the individual that is user-friendly and personalized. The system 10 computes the energy consumption of a user 12 based on respective carbon dioxide emissions 40, translates this value to a respective score 42, explains this value and learns to recommend actions to the user 12 to balance the account. The system 10 may be adapted for multiple users such as families, communities and businesses. For example, the system 10 may be employed to help a fleet become more environmentally friendly by both analyzing their usage and modifying their fleets accordingly.
The controller C of
Look-up tables, databases, data repositories or other data stores described herein may include various kinds of mechanisms for storing, accessing, and retrieving various kinds of data, including a hierarchical database, a group of files in a file system, an application database in a proprietary format, a relational database energy management system (RDBMS), etc. Each such data store may be included within a computing device employing a computer operating system such as one of those mentioned above and may be accessed via a network in one or more of a variety of manners. A file system may be accessible from a computer operating system and may include files stored in various formats. An RDBMS may employ the Structured Query Language (SQL) in addition to a language for creating, storing, editing, and executing stored procedures, such as the PL/SQL language mentioned above.
The flowcharts illustrate an architecture, functionality, and operation of possible implementations of systems, methods, and computer program products of various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It will also be noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, may be implemented by specific purpose hardware-based systems that perform the specified functions or acts, or combinations of specific purpose hardware and computer instructions. These computer program instructions may also be stored in a computer-readable medium that can direct a controller or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture including instructions to implement the function/act specified in the flowchart and/or block diagram blocks.
The numerical values of parameters (e.g., of quantities or conditions) in this specification, including the appended claims, are to be understood as being modified in each respective instance by the term “about” whether or not “about” actually appears before the numerical value. “About” indicates that the stated numerical value allows some slight imprecision (with some approach to exactness in the value; about or reasonably close to the value; nearly). If the imprecision provided by “about” is not otherwise understood in the art with this ordinary meaning, then “about” as used here indicates at least variations that may arise from ordinary methods of measuring and using such parameters. In addition, disclosure of ranges includes disclosure of each value and further divided ranges within the entire range. Each value within a range and the endpoints of a range are hereby disclosed as separate embodiments.
The detailed description and the drawings or FIGS. are supportive and descriptive of the disclosure, but the scope of the disclosure is defined solely by the claims. While some of the best modes and other embodiments for carrying out the claimed disclosure have been described in detail, various alternative designs and embodiments exist for practicing the disclosure defined in the appended claims. Furthermore, the embodiments shown in the drawings, or the characteristics of various embodiments mentioned in the present description are not necessarily to be understood as embodiments independent of each other. Rather, it is possible that each of the characteristics described in one of the examples of an embodiment can be combined with one or a plurality of other desired characteristics from other embodiments, resulting in other embodiments not described in words or by reference to the drawings. Accordingly, such other embodiments fall within the framework of the scope of the appended claims.
