HVAC Equipment Sequencing Using In-Situ Efficiency Modeling
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Solution Overview
Problem
HVAC systems face challenges in continuously modeling and reacting to dynamic efficiency changes of equipment over time, leading to suboptimal energy usage due to equipment wear, maintenance issues, and changing conditions, which conventional sequencing methods fail to address effectively.
Innovation Solution
The development of systems and methods that utilize historical and predicted data, combined with Bayesian, linear regression, or k-nearest neighbors models, to dynamically predict equipment efficiency and optimize sequencing, ensuring efficient operation and load balancing of HVAC equipment, thereby reducing energy consumption and labor-intensive manual processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional static sequencing methods are used to arrange HVAC equipment by efficiency ratings, then initial energy efficiency is improved, but the system cannot adapt to dynamic efficiency changes over time due to equipment wear and maintenance
Solution Approach 1:
The patent implements dynamic sequencing by continuously monitoring actual equipment efficiency through sensors and automatically adjusting the operating sequence based on real-time efficiency data. This replaces static manufacturer ratings with live performance metrics, allowing the system to adapt to equipment wear, maintenance status, and environmental conditions changes.
Solution Approach 2:
The system incorporates feedback loops where sensors monitor equipment performance parameters (temperature, pressure, power consumption) and feed this data back to the sequencing controller. The controller uses this feedback to recalculate efficiency ratings and resequence equipment operations, creating a closed-loop adaptive system that continuously optimizes energy usage.
2Measurement precision
If manual sequencing methods requiring engineer observation and modeling are used, then sequencing accuracy is improved, but labor requirements and time for initial setup increase significantly
Solution Approach 1:
The system performs self-characterization by automatically measuring equipment efficiency parameters through integrated sensors without requiring manual engineer observation or modeling. The equipment itself provides the data needed for sequencing decisions through automated monitoring of operational parameters, eliminating the need for time-consuming manual setup while maintaining measurement accuracy.
Solution Approach 2:
The patent replaces manual engineer modeling and observation processes with automated electronic sensing and computational algorithms. Sensors automatically collect performance data, and computer processors calculate efficiency ratings and determine sequencing, substituting human expertise with automated systems that achieve comparable or superior precision without the time investment.
3Ease of manufacture
If equipment is sequenced based on manufacturer efficiency specifications, then initial sequencing is simplified, but actual operating efficiency deteriorates due to equipment degradation and changing conditions
Solution Approach 1:
The system transitions from using fixed manufacturer specification parameters to dynamically measured operational parameters. Instead of relying on static efficiency ratings that deteriorate with equipment age, the system continuously updates efficiency parameters based on actual operating conditions, maintaining reliability despite equipment degradation or environmental changes.
Data Source
AI summary
Systems and methods for sequencing HVAC equipment of an HVAC system using data recorded in situ to build a model capable of making predictions about equipment efficiency and using that information, in combination with predictions about building load, to produce an operational sequence for the HVAC equipment that promotes an improved or optimized overall energy efficiency for the HVAC system. In one embodiment, the process is automated and utilizes Bayesian computational models or algorithms to generate an initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment.