HVAC Controller With Self-Learning Energy Efficiency Adjustment
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Solution Overview
Problem
Existing HVAC systems require frequent manual adjustments to optimize energy consumption due to changes in local climate, equipment characteristics, and ambient conditions, leading to inefficiencies and energy wastage.
Innovation Solution
A self-learning controller communicates with a building automation system to automatically adjust HVAC equipment parameters using real-time data, predicted information, and self-learning algorithms to minimize energy wastage and optimize operating efficiency.
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 system requires frequent manual intervention and time-consuming recalibration
Solution Approach 1:
The system automatically monitors its own performance metrics and adjusts numerical constants without human intervention. The controller continuously compares actual energy consumption against optimal values and self-corrects parameter settings, eliminating the need for manual recalibration while maintaining adaptability to changing conditions
Solution Approach 2:
The system implements closed-loop feedback by continuously monitoring HVAC equipment performance, comparing actual energy consumption with predicted optimal consumption, and automatically adjusting operating parameters. This feedback mechanism enables the system to adapt to climate and equipment changes in real-time without manual intervention
2Loss of energy
If optimization software adjusts operating parameters to minimize energy consumption, then energy efficiency is improved, but the system complexity increases due to multiple modules and algorithms
Solution Approach 1:
The patent introduces a controller as an intermediary device that houses the optimization software and acts as a mediator between the building automation system and HVAC equipment. This centralized intermediary simplifies the overall system architecture by consolidating complex algorithms and data processing in a single dedicated component rather than distributing complexity across multiple systems
Solution Approach 2:
The system replaces manual mechanical adjustment of HVAC parameters with automated electronic control through optimization software. The controller uses computational algorithms to automatically adjust operating parameters, substituting manual mechanical tuning with intelligent software-based control that reduces energy consumption without proportionally increasing operational complexity
3Productivity
If real-time data is continuously monitored and compared with predicted information, then energy consumption is optimized, but the computational requirements and processing time increase
Solution Approach 1:
The system applies partial optimization by focusing computational resources on adjusting only the most impactful operating parameters rather than optimizing all system variables simultaneously. This selective approach maintains energy management efficiency while reducing unnecessary computational overhead and energy consumption
Data Source
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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.