Appliance-Specific Power Usage Conversion Method
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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 energy-saving advice that is not tailored to individual appliance usage, and require extensive memory storage for various comparison scenarios.
Innovation Solution
A method that inputs power usage data for multiple user IDs, generates differential values, and converts these into electricity bill values and use times for specific appliances, providing personalized display data to users, thereby focusing energy-saving efforts on specific appliances with high usage times.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional energy conservation systems provide generalized comparison data, then users can see their power usage relative to others, but the advice remains too general and not tailored to specific appliance usage patterns
Solution Approach 1:
The patent segments aggregate power usage data into appliance-specific components by converting total power differences into equivalent use times for specific appliance types (air conditioners, water heaters, etc.). This segmentation enables personalized advice for each appliance rather than providing only generalized household-level comparisons, directly resolving the contradiction between personalization and complexity.
2Adaptability or versatility
If the system stores extensive categorization and advice data for different scenarios, then personalized energy-saving advice can be provided, but memory requirements increase significantly
Solution Approach 1:
The patent extracts only the essential data elements needed for personalized advice: aggregate power usage data, user appliance ownership information, and appliance power consumption specifications. By extracting and utilizing only these critical elements rather than storing extensive pre-categorized advice databases, the system achieves comprehensive scenario coverage with minimal memory requirements, resolving the contradiction between adaptability and storage quantity.
Solution Approach 2:
The system enables self-service by allowing users to input their own appliance ownership information, which then automatically generates personalized advice based on their specific appliance portfolio. This self-service approach eliminates the need for the system to pre-store advice for every possible appliance combination, significantly reducing memory requirements while maintaining high adaptability to individual user scenarios.
3Loss of information
If the system provides only aggregate power usage comparisons, then memory storage is minimal, but users cannot understand which specific appliances to target for energy-saving measures
Solution Approach 1:
The patent applies local quality by providing appliance-specific energy consumption breakdowns tailored to each user's actual appliance portfolio. Instead of uniform aggregate data, the system calculates and presents power usage differences specific to each appliance type the user owns, enabling targeted energy-saving actions. This localized information quality resolves the contradiction between information completeness and processing complexity.
Data Source
AI summary
A method includes: inputting information indicating power usage; 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 an electric home appliance of a first type corresponding to the first user ID; generating a first electricity bill value by converting the difference value to an electricity bill; generating display data indicating that the first electricity bill value corresponds to the first conversion value; and a transmitting unit that transmits the display data.