Power Usage Differential Conversion to Appliance Use Time
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Solution Overview
Problem
Conventional energy conservation systems fail to effectively convey the magnitude of power usage differences to users, leading to generalized advice that is not specific or actionable, and require extensive memory capacity to store multiple energy-saving suggestions for various appliances.
Innovation Solution
A method that inputs power usage data for multiple user IDs, generates differential values, and converts these into specific use times for individual appliances, providing personalized energy-saving recommendations and reducing memory requirements by using templated display data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional energy conservation systems provide generalized advice to users, then memory capacity requirements are reduced, but the advice is not specific or actionable enough for users to understand the magnitude of power usage differences
Solution Approach 1:
The patent transforms power usage data from raw numerical values into meaningful temporal parameters (use time) that users can easily comprehend. By converting differential power values into equivalent appliance usage time, the system provides specific, actionable advice without requiring complex data structures or large memory capacity.
2Loss of information
If the system converts differential power values into use time of specific appliances, then users can understand the magnitude of power usage differences, but the system requires processing and storage of detailed appliance-specific data
Solution Approach 1:
The patent introduces an intermediary conversion process that translates complex power usage differential data into the familiar concept of appliance use time. This intermediary representation serves as a bridge between raw data and user comprehension, making power usage differences tangible and actionable without requiring users to directly interpret complex numerical data.
Solution Approach 2:
The system changes the parameter representation from abstract power consumption values to concrete time-based metrics. By expressing power usage differences as equivalent hours of appliance operation, the system enhances comprehensibility while maintaining data integrity through reversible mathematical transformations.
3Ease of operation
If the system provides personalized energy-saving recommendations based on individual user data, then the advice becomes actionable and specific, but memory capacity requirements increase to store multiple suggestions for various appliances
Solution Approach 1:
The patent extracts only the essential information needed for personalized advice - the differential power usage converted to use time - rather than storing comprehensive lists of energy-saving suggestions for multiple appliances. This extraction approach provides actionable, personalized recommendations while minimizing memory requirements by focusing on the most relevant data.
Data Source
AI summary
A method includes: inputting information indicating power usage corresponding to each of the plurality of user IDs; generating a differential value between i) a first cumulative value of power usage corresponding to a first user ID and ii) a second cumulative value of power usage corresponding to a second user ID in a prescribed period; generating a first conversion value by converting the differential value to a use time of a first electric home appliance among electric home appliances corresponding to the first user ID; generating display data indicating that the differential value corresponds to the first conversion value; and transmitting the display data to an information terminal device corresponding to the first user ID.