Building automation and control system modeling and reporting
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Solution Overview
Problem
Modern HVAC systems in large building complexes face inefficiencies due to the complexity of their subsystems, leading to wasteful energy usage and calibration challenges.
Innovation Solution
A system that mathematically models HVAC components, using actual data to generate optimized set points for subsystems, which are then fed back to controllers to improve operation and increase efficiency, by importing data into a database and applying logical conclusions weighted to provide specific instructions for improving energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a central server controls many individual hardware controllers in HVAC subsystems, then system coordination is improved, but device complexity increases making calibration and maintenance difficult
Solution Approach 1:
The patent segments the complex HVAC system into multiple independent subsystems (boilers, chillers, ventilation, lighting, plumbing), each with its own controller and mathematical model. This segmentation allows each subsystem to be calibrated and maintained independently while still being coordinated through the central server, resolving the contradiction between system coordination and device complexity.
Solution Approach 2:
The patent introduces mathematical models as intermediary components between the central server and individual hardware controllers. These models translate high-level control objectives into specific set points for each subsystem, acting as a mediator that simplifies the calibration process while maintaining overall system coordination.
2Productivity
If subsystems are periodically calibrated and maintained, then operational efficiency is improved, but loss of time occurs during calibration and maintenance activities
Solution Approach 1:
The patent implements preliminary action by continuously running mathematical models that predict optimal set points before actual operational changes are needed. The system proactively identifies calibration needs and prepares optimized parameters in advance, allowing for quicker adjustments during maintenance windows and reducing the time lost during actual calibration activities.
Solution Approach 2:
The system enables self-service calibration through automated mathematical models that continuously analyze sensor data and adjust set points without requiring manual intervention. The models automatically detect when calibration is needed and perform adjustments, significantly reducing the time loss associated with periodic manual calibration and maintenance activities.
3Loss of energy
If mathematical models with multiple sensors and parameters are used to optimize HVAC systems, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies universality by using a standardized mathematical modeling framework that can be applied across all different HVAC subsystems (boilers, chillers, ventilation, lighting, plumbing). This universal approach allows the same modeling techniques and algorithms to optimize energy efficiency across diverse subsystems, improving energy efficiency while managing complexity through standardization rather than requiring unique complex models for each subsystem.
Data Source
AI summary
Systems and methods for modeling, reporting, controlling and optimizing HVAC systems are provided. A server receives HVAC system data, processes the data and sends the processed data to a client device. The client device receives the processed data and then makes certain choices of analog and digital rules to apply to the data to reach conclusions about certain mechanical states of the system and flow efficiencies. The rule choices are sent to the server. The server analyzes the HVAC system based on the rules and generates a report. The report can be used to optimize the mechanical components of the HVAC system or control them directly in a feedback loop.