What Uses Most
The What Uses Most module educates customers about the top three energy uses in their home for the report period. One of the customer's top energy uses is highlighted at the top of the module. Highlighted energy uses must meet one of the following criteria:
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It is the customer’s highest energy use.
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It is the customer’s second-highest energy use.
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The energy use for that category is significantly above the regional average.
The highlighted usage category heading varies depending on the highlighted category and module state (highest energy use, second highest energy use, most above regional average), and is paired with a related energy-savings tip.
Appears in: Limited Income Report, Electric Vehicle Report
Note: While this module was designed for the limited income and electric vehicle customer experiences, it can be used for other audience segments as well. Contact your Delivery Team to discuss module options.
Requirements
Utility Requirements
Category |
Description |
---|---|
Required Cloud Service |
Energy Efficiency Cloud Service |
Scale |
Not applicable. |
Customer Requirements
Category |
Description |
---|---|
Billing Frequency |
Monthly, bi-monthly, or quarterly. |
Data Delivery Frequency |
Not applicable. |
Data Requirements |
|
Data History |
|
Data Coverage |
Not applicable. |
Supported Fuels |
|
Limitations
Report Type: This module is available for the Limited Income Report, and Electric Vehicle Report by default, but may be included in other report experiences. Contact your Delivery Team for more information.
User Experience
This section describes the user experience for the What Uses Most module. The module varies by usage factors. See the User Experience Variations for additional information.
This image is an example of the module for a customer with heating as the highest energy use in the billing period.
User Experience Variations
The user experience of the feature may vary for customers and utilities depending on their service types (gas, electricity, dual fuel, and so on), available data, costs, locale, and other factors. For more information, see the What Uses Most description for Home Energy Reports v3 in the Oracle Utilities Opower Energy Efficiency Cloud Service Product Overview and go to the User Experience Variations section.
Configuration Options
For each element listed in the table, indicate the desired configuration in the Input Value column. If you do not provide an input for optional configurations, the default will be used.
Configuration Option | Input Value |
Call to Action URL The call to action button redirects customers to the [Home Energy Analysis where they can provide more details about their home to improve their report accuracy. Default: The URL redirects the customer to the Home Energy Analysis and must be configured. |
Required Contact your Delivery Team about altering the URL destination. |
Disaggregtion Explainer Header
Heading that prepares the customer to learn ow the categories are calculated. Default: "How do we determine your energy breakdown?" |
Optional Select one of the following:
|
Disaggregtion Explainer Text An explanatory note that clarifies that these categorizations are derived from data science model estimates, and reminds customers that they can improve their report accuracy by completing their Home Energy Analysis. Default: "Your energy breakdown is based on smart meter data and past usage. Complex energy data may cause minor inaccuracies. Improve accuracy by taking the short Home Energy Assessment survey." |
Optional Select one of the following:
|
Call to Action Text The call to action button redirects customers to the [Home Energy Analysis where they can provide more details about their home to improve their report accuracy. Default: "Update my home profile" |
Optional Select one of the following:
|
Calculations
Energy Use Categories
The energy use category calculation varies depending on whether or not the customer has a smart meter.
Customer does not have smart meter or reliable smart meter data is not available: The categories are calculated using a combination of weather data and the customer's historical usage data to get an idea of how energy is being used in their home over time, as well the Home Energy Analysis survey responses that are personalized to their home. With this information, our data science models can create an estimated breakdown of their usage by category or appliance so that you know where to focus and save.
Customer has a smart meter: We use data from your their smart meter, which records their energy use throughout the day, to detect certain appliance usage patterns and get an idea of how energy is being used in their home.
For example, it is common for a refrigerator to cycle on every 15-20 minutes to reach the desired temperature and then turn off. Our data science models can spot patterns like this from a refrigerator and other common appliances to determine which appliances are being used and when to help estimate per-appliance usage in the home. We then provide an estimated breakdown of their usage by appliance so that the customer knows where to focus and save.