HVAC Control Feedback for Self-Learning Energy Optimization
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Solution Overview
Problem
Existing HVAC systems require frequent manual adjustments of numerical constants in building automation systems (BAS) to optimize energy consumption, which is inefficient and time-consuming, especially due to changes in local climate, equipment characteristics, and external conditions.
Innovation Solution
A self-learning controller that communicates with the BAS to automatically adjust operating parameters of HVAC equipment using real-time data, predictive simulations, and self-learning algorithms to minimize energy wastage and optimize energy efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If manual adjustment of numerical constants is used to optimize HVAC energy consumption, then energy efficiency can be improved, but the system requires frequent manual intervention which is time-consuming and inefficient
Solution Approach 1:
The system automatically monitors HVAC equipment performance and adjusts operating parameters without manual intervention. The controller continuously compares actual performance against optimal values and self-corrects deviations, eliminating the need for manual re-programming of numerical constants while maintaining energy optimization.
Solution Approach 2:
The system implements continuous feedback loops where performance data from HVAC equipment is monitored, compared against optimal operating parameters, and used to automatically adjust control settings. This closed-loop feedback mechanism ensures energy efficiency is maintained without requiring manual intervention to recalculate or reprogram system parameters.
2Adaptability or versatility
If manual re-programming of BAS is performed to account for climate and equipment changes, then system adaptability is improved, but the complexity and time required for adjustments increases
Solution Approach 1:
The controller automatically detects changes in equipment characteristics and environmental conditions, then self-adjusts operating parameters without requiring manual re-programming. The system monitors performance deviations and autonomously recalibrates numerical constants to account for climate changes, equipment aging, or component replacements.
Solution Approach 2:
The system transitions from static, manually-set parameters to dynamic, automatically-adjusting parameters that adapt in real-time to changing conditions. The controller continuously modifies operating parameters based on current system state and environmental factors, enabling the HVAC system to remain optimized without manual intervention despite changing conditions.
3Use of energy by moving object
If optimization software adjusts each piece of equipment individually, then equipment-level efficiency is improved, but the overall system efficiency may not be optimized and requires iterative manual adjustments
Solution Approach 1:
The system merges individual equipment optimization with overall system optimization by coordinating control across all HVAC components simultaneously. The controller considers interactions between equipment and adjusts parameters to optimize total system performance rather than individual component performance, achieving better overall energy efficiency without iterative manual adjustments.
Solution Approach 2:
The system implements system-wide feedback loops that monitor overall HVAC performance and coordinate adjustments across multiple pieces of equipment. The controller uses feedback from system-level performance metrics to simultaneously optimize individual equipment operation while maintaining overall system efficiency, eliminating the need for iterative manual recalibration.
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.