Server-Based Absence Prediction for Air Conditioner Control
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Solution Overview
Problem
Existing air conditioner control technologies fail to seamlessly transition to user-absence operations, lacking precision in determining and adapting to absence periods.
Innovation Solution
A network system comprising a server and air conditioners that use prediction data to differentiate between predicted absence and actual absence times, adjusting operations accordingly, with the server causing absence-mode operations based on predetermined time periods and probability calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the air conditioner uses a fixed absence detection time period, then the control logic is simple, but it cannot adapt to predicted absence periods and transitions to absence-mode operations accurately
Solution Approach 1:
The system performs preliminary actions by calculating predicted absence time periods in advance using prediction data (such as calendar information, usage patterns) before the actual absence occurs. This allows the air conditioner to proactively prepare for absence-mode operations, improving detection precision by comparing predicted periods with actual sensor data, rather than relying solely on fixed time thresholds.
Solution Approach 2:
The absence detection time period is made dynamic by adjusting it based on predicted absence periods. The control logic adapts the detection threshold dynamically: during predicted absence periods, the system is more sensitive to absence detection, while during predicted presence periods, it maintains normal operation. This dynamic adjustment resolves the contradiction by making the system both precise and adaptively complex only when needed.
2Loss of energy
If the air conditioner transitions to absence-mode operation based on short absence detection, then energy saving is maximized, but false transitions occur during predicted presence periods
Solution Approach 1:
The system uses feedback by continuously comparing actual absence detection results with predicted absence time periods. When a short absence is detected, the system checks whether it falls within a predicted absence period before transitioning to absence-mode operation. This feedback mechanism prevents false transitions during predicted presence periods while still enabling energy-saving operations during genuine absences, thus maintaining both energy efficiency and transition reliability.
3Measurement precision
If the air conditioner uses predicted absence data, then operation accuracy is improved, but the system requires additional data processing and communication infrastructure
Solution Approach 1:
The system introduces a server as an intermediary that handles the complex data processing and prediction calculations. The air conditioner itself remains relatively simple, communicating with the server to obtain predicted absence time periods. This intermediary approach allows the system to benefit from accurate prediction data and advanced processing capabilities without significantly increasing the complexity of the air conditioner unit, as the computational burden is offloaded to the external server infrastructure.
Data Source
AI summary
Provided herein is a network system (1) that includes: an electric apparatus (300); and a server (100) that computes a predicted absence time period by using prediction data concerning a user of the electric apparatus (300), and that detects an absence time in which nobody is detected, the server (100) causing the electric apparatus (300) to perform an absence-time operation upon detecting a first absence time in a period outside of the predicted absence time period, and causing the electric apparatus (300) to perform the absence-time operation upon detecting a second absence time, shorter than the first absence time, in a period within the predicted absence time period.