Self-Learning HVAC Controller to Reduce Manual Recalibration
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Solution Overview
Problem
Existing HVAC systems require frequent manual adjustments of numerical constants in optimization software to maintain optimal energy efficiency, which is inefficient and time-consuming, especially due to changes in local climate, equipment characteristics, and building conditions.
Innovation Solution
A self-learning controller that communicates with a building automation system to automatically adjust operating parameters of HVAC equipment using real-time data, predicted efficiencies, and self-learning algorithms to minimize energy wastage and optimize energy management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual adjustment of numerical constants is performed periodically, then the HVAC system can adapt to changes in climate and equipment characteristics, but the time and labor required for recalibration increases
Solution Approach 1:
The system performs self-calibration by automatically comparing predicted energy consumption with actual measured consumption, identifying deviations, and adjusting numerical constants without human intervention. This eliminates the need for manual recalibration while maintaining adaptability to changing conditions.
Solution Approach 2:
The system continuously monitors actual energy consumption and feeds this information back to the controller, which then adjusts numerical constants based on the difference between predicted and actual consumption. This closed-loop feedback mechanism enables automatic adaptation to climate and equipment changes.
2Loss of energy
If optimization software is used to control HVAC equipment, then energy consumption is minimized, but the system requires frequent manual recalculation and modification of operating characteristics
Solution Approach 1:
The controller automatically updates numerical constants and operating characteristics by comparing predicted versus actual energy consumption, eliminating the need for manual recalculation and modification. The system maintains optimization while improving ease of operation through self-adjustment.
Solution Approach 2:
The system dynamically adjusts numerical constants and operating parameters in real-time based on changing conditions and actual performance data, rather than relying on static pre-programmed values. This enables continuous optimization without manual intervention.
3Ease of manufacture
If numerical constants are pre-programmed during installation, then the system can operate initially, but the values become outdated over time due to climate and equipment changes
Solution Approach 1:
The system uses feedback from actual energy consumption measurements to continuously update numerical constants, ensuring they remain accurate despite climate and equipment changes. This maintains reliability while keeping the initial setup simple.
Solution Approach 2:
The system performs preliminary calibration during installation with default numerical constants, then automatically refines these values over time through self-learning and comparison with actual performance data, ensuring both easy setup and long-term accuracy.
Data Source
AI summary
A controller is configured to exchange information with a building automation system and includes various executable programs for determining a real time operating efficiency, simulating a predicted or theoretical operating efficiency, comparing the same, and then adjusting one or more operating parameters on equipment utilized by a building's HVAC system. The controller operates to adjust an operating efficiency of the HVAC system. An adjustment module utilized by the controller may modify the HVAC equipment parameters based on the likelihood that various HVAC equipment operates in parallel and on-line near its natural operating curve. In addition, the adjustment module may include a self-learning aspect that permits the controller to more efficiently make similar, future adjustments as needed.