Whole building air quality control system
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Solution Overview
Problem
Existing indoor air quality (IAQ) management systems lack the ability to efficiently and dynamically adjust to changing environmental conditions and user preferences, leading to suboptimal air quality and energy consumption.
Innovation Solution
A whole building air quality control system that integrates sensors, a controller, and machine learning algorithms to continuously monitor and adjust building conditions, setting a desired air quality index (AQI) based on categorical variables and modifying control states iteratively to achieve optimal IAQ.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional IAQ management systems are used, then system simplicity is maintained, but the ability to dynamically adjust to changing environmental conditions and user preferences deteriorates
Solution Approach 1:
The system transitions from static IAQ management to dynamic adjustment by continuously monitoring environmental conditions (temperature, humidity, air quality) and user preferences through multiple sensors, then iteratively modifying control states of HVAC equipment using machine learning algorithms to adapt to changing conditions in real-time
Solution Approach 2:
The system implements closed-loop feedback by continuously measuring building conditions through sensors, comparing actual IAQ against desired AQI targets, and using machine learning algorithms to iteratively adjust control states of IAQ components based on the deviation between current and desired conditions
2Reliability
If machine learning algorithms are implemented for iterative modification of control states, then air quality optimization is improved, but computational complexity and processing requirements worsen
Solution Approach 1:
The machine learning algorithm iteratively modifies control states by making incremental adjustments rather than attempting to optimize all parameters simultaneously, applying partial actions that progressively improve air quality while managing computational complexity through step-by-step refinement
Solution Approach 2:
The system changes control parameters (temperature setpoints, humidity levels, ventilation rates) iteratively based on machine learning algorithm predictions, adjusting physical parameters of the building environment to achieve desired air quality outcomes while managing computational complexity through parameter-based control
3Reliability
If continuous monitoring and iterative adjustment are performed, then indoor air quality is improved, but energy consumption worsens
Solution Approach 1:
The system uses machine learning algorithms to autonomously determine optimal control states without requiring continuous manual intervention or excessive computational resources, enabling the system to self-optimize air quality while managing energy consumption through intelligent decision-making
Solution Approach 2:
The system performs iterative modification of control states at optimized intervals rather than continuously, using machine learning to predict when adjustments are needed based on environmental conditions and usage patterns, reducing unnecessary computational energy consumption while maintaining air quality
Data Source
AI summary
A whole building air quality control system includes an indoor air quality (IAQ) component having at least one control state, a plurality of sensors configured to measure a plurality of building conditions of a building space, and a controller communicably coupled to the IAQ component and the plurality of sensors. The controller includes memory storing a desired air quality index (AQI). The AQI includes a categorical variable. The controller is configured to iteratively modify a control state of the IAQ component using a machine learning algorithm until the plurality of building conditions of the building space satisfy the desired AQI.