Temperature control system and method
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Solution Overview
Problem
Existing temperature control systems for rooms do not effectively account for predicted heat loss or optimize the combined operation of air conditioners and boilers based on this prediction.
Innovation Solution
A system comprising a remote controller that acquires indoor and outdoor temperature data, estimates a heat profile based on historical data, and controls temperature control units to adjust for predicted heat loss or gain, optimizing the operation of air conditioners and boilers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If temperature control units operate based on simple on/off rules without heat loss prediction, then the system is simple to operate, but energy efficiency is poor
Solution Approach 1:
The system performs preliminary action by predicting future heat loss or gain in the room based on historical temperature data and current conditions. This prediction allows the controller to pre-adjust the operation of temperature control units before actual temperature deviations occur, thereby improving energy efficiency by avoiding unnecessary heating or cooling cycles while maintaining comfortable indoor temperatures.
2Measurement precision
If the system controls multiple temperature control units with different characteristics, then temperature control precision is improved, but device complexity increases
Solution Approach 1:
The system applies local quality by recognizing and utilizing the different characteristics of each temperature control unit (such as heating power, response speed, efficiency) to match specific control needs. The controller selectively operates appropriate units based on the predicted heat loss profile and current temperature requirements, optimizing the combination of units to achieve precise temperature control while considering their individual performance characteristics.
3Measurement precision
If the system uses historical data for heat profile estimation, then prediction accuracy is improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system applies partial action by using a selective and optimized approach to historical data utilization. Rather than storing and processing all possible historical data, the system uses temperature data from predetermined time intervals that are sufficient to establish accurate heat loss patterns. This selective data usage maintains prediction accuracy while minimizing data storage requirements and processing complexity.
Data Source
AI summary
A temperature control system and method for temperature control, wherein use is made of indoor and outdoor temperature historical data to generate a heat profile for estimation of a feature heat quantity/temperature in one or more rooms, and for an ensuing control of at least two temperature control units based on the heat profile.