HVAC Equipment Sequencing Using Predictive Efficiency Models
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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 needs, and operational changes, which conventional sequencing methods fail to address effectively.
Innovation Solution
The development of systems and methods that utilize historical and predicted data to build models for predicting equipment efficiency, optimizing sequencing, and balancing loads, employing Bayesian, linear regression, or k-nearest neighbors algorithms to automate the determination of an efficient operational sequence for HVAC equipment, reducing the need for manual labor and accounting for equipment covariance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional static sequencing methods are used based on initial equipment efficiency ratings, then equipment sequencing is simple to implement, but equipment energy efficiency drifts over time as parts wear and conditions change
Solution Approach 1:
The patent implements dynamic sequencing by continuously monitoring equipment efficiency parameters and adjusting the sequencing order in real-time based on current performance data. This allows the system to adapt to equipment wear, maintenance needs, and operational changes, resolving the contradiction between simple implementation and maintaining reliability over time.
Solution Approach 2:
The system incorporates feedback mechanisms that collect operational data from equipment sensors and use this information to continuously update efficiency ratings and re-optimize sequencing. This closed-loop approach ensures equipment energy efficiency is maintained despite wear and changing conditions, while keeping the implementation manageable through automated adjustments.
2Reliability
If manual sequencing methods are used with engineering knowledge and manufacturer specifications, then initial equipment sequencing can be optimized, but the process requires many hours of labor and occurs only at commissioning
Solution Approach 1:
The system enables self-service sequencing by automatically collecting equipment performance data, analyzing efficiency trends, and adjusting sequencing orders without requiring continuous engineering intervention. The automated system performs what would otherwise require manual analysis and re-optimization, eliminating the need for repeated engineering labor while maintaining optimal sequencing.
Solution Approach 2:
The patent replaces manual engineering analysis and judgment with automated computational algorithms that process sensor data and determine optimal sequencing. This substitution of mechanical/engineering processes with automated systems eliminates the time-consuming manual labor while maintaining or improving sequencing optimization quality.
3Stability of the object's composition
If equipment is staged by equal runtime rotation, then equipment wear is distributed evenly, but energy efficiency is compromised by operating less efficient equipment
Solution Approach 1:
The system dynamically changes the sequencing parameter from fixed equal-time rotation to variable time-based sequencing that adjusts equipment selection based on real-time efficiency measurements. This allows the system to prioritize more efficient equipment when operational conditions favor them, reducing energy consumption while still distributing wear through controlled rotation and monitoring.
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 are initial sequence. The process reduces engineering hours and may advantageously provide a means to predict potential sequencing problems for similar types of HVAC equipment.