Power Control Unit for Electrical Apparatus Using Usage Pattern Prediction
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Solution Overview
Problem
Computing systems and other electrical devices waste significant energy when idle or operating at less than full capacity, leading to inefficiencies and increased electricity costs, as they maintain high power consumption even when not in use.
Innovation Solution
A system and method that adjust power supply based on usage patterns, reducing power during idle periods and increasing it before anticipated use, utilizing an input power control unit, power supply, and non-volatile memory to store and refine usage data for efficient energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power is reduced during idle periods, then energy efficiency is improved, but the apparatus may not be ready for immediate use when needed
Solution Approach 1:
The system performs preliminary actions by anticipating future user needs based on learned usage patterns. It proactively transitions to higher power states before the predicted usage time, ensuring the apparatus is ready immediately when the user arrives, thus avoiding reboot delays while maintaining energy savings during truly idle periods.
Solution Approach 2:
The power state of the apparatus is made dynamic rather than static. The system continuously adjusts power consumption based on real-time conditions and predicted future states, transitioning between different power modes (idle, standby, active) according to learned usage patterns, thereby optimizing both energy efficiency and response time.
2Reliability
If power is maintained at full capacity, then the apparatus is always ready for immediate use, but energy consumption increases significantly
Solution Approach 1:
Instead of maintaining full power continuously, the system performs preliminary actions by predicting when full power will be needed and transitioning to appropriate power states in advance. This ensures reliability when needed while avoiding the energy waste of maintaining full power during periods when it won't be required immediately.
Solution Approach 2:
The system changes the power consumption parameter dynamically based on predicted usage patterns. Rather than keeping power at a constant high level, it adjusts the power parameter to match actual needs, using learned patterns to determine optimal power states at different times, thus reducing energy consumption while maintaining availability.
3Loss of energy
If the system learns and adapts usage patterns, then energy efficiency is optimized, but system complexity increases
Solution Approach 1:
The system provides self-service by automatically learning and adapting to user usage patterns without requiring manual configuration or intervention. The pattern recognition system autonomously observes usage behavior, builds predictive models, and adjusts power states accordingly, reducing energy waste while the added complexity is justified by the autonomous adaptive capability.
Data Source
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AI summary
A usage pattern identifies time periods when an electrical apparatus is likely to bepowered-up or not in use. Power provided to an electrical apparatus is increased during time periods that the electrical apparatus is likely to be powered-up. Similarly, the power provided to the electrical apparatus is reduced or removed during tme periods that the electrical apparatus is likely to be out of use or idle. The usage pattern is continually updated and refined by collecting usage data during user interaction with the electrical apparatus.