Model predictive control-based building climate controller incorporating humidity
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Solution Overview
Problem
Existing Model Predictive Control (MPC) systems for climate control in buildings often neglect humidity, leading to inefficient energy use and potential health issues due to mold growth, as they struggle to account for the interdependence of temperature and humidity in cooling coil models, which are complex and nonlinear.
Innovation Solution
A constrained optimization method is employed to set control commands for HVAC systems, explicitly considering humidity constraints through a control-oriented cooling coil model that predicts supply air temperature and humidity ratio, using a polynomial function to formulate the optimization problem as a nonlinear program, allowing for efficient energy management while maintaining thermal comfort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If MPC includes humidity constraints and cooling coil models, then humidity control accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex, difficult-to-obtain coil geometry data with a simplified cooling coil model that uses easily measurable parameters (airflow rate, inlet temperature, inlet humidity ratio, and coil surface area). This simplified model provides sufficient accuracy for MPC control without requiring detailed geometric information that would increase system complexity.
Solution Approach 2:
The patent transforms the complex cooling coil model into a simplified version by changing the parameters from detailed geometric measurements to operational parameters (airflow rate, temperatures, humidity ratios, and surface area). This parameter transformation maintains the essential dehumidification dynamics while reducing model complexity for real-time control.
2Reliability
If rule-based controllers use conservatively designed rules, then humidity constraints are satisfied, but energy use increases
Solution Approach 1:
The patent transitions from static rule-based control to dynamic MPC control that adapts to real-time conditions. The controller continuously optimizes supply air temperature and airflow rate based on current outdoor conditions, zone humidity, and heat load, allowing energy-efficient operation while maintaining humidity constraints when needed rather than always using conservative settings.
Solution Approach 2:
The MPC controller uses feedback from zone humidity sensors and outdoor condition measurements to make informed decisions about humidity control. This feedback mechanism allows the system to apply humidity constraints only when necessary (when outdoor humidity is high or zone humidity is approaching limits) rather than always using conservative control, thereby reducing unnecessary energy consumption.
3Use of energy by moving object
If MPC minimizes energy use without humidity constraints, then energy efficiency is improved, but humidity constraints are violated
Solution Approach 1:
The patent implements dynamic constraint application where humidity constraints are actively enforced only when necessary. The MPC controller evaluates outdoor conditions and zone state to determine when humidity control is needed, allowing energy-efficient operation during dry conditions while preventing mold growth during humid conditions through optimized supply air temperature and airflow rate adjustments.
Solution Approach 2:
The patent changes the control parameters from fixed conservative settings to dynamically optimized values. By adjusting supply air temperature and airflow rate based on real-time conditions and humidity constraints, the system achieves energy efficiency when humidity control is not needed while maintaining reliability when humidity constraints must be satisfied.
Data Source
AI summary
Systems and methods are configured to control operation of an HVAC system providing climate control for a zone of a structure. In various embodiments, a constrained optimization problem is performed to set control commands for controlling operation of the HVAC system to achieve one or more objectives while providing a supply air flow at an air temperature and a humidity ratio for the zone at a future time. For instance, the optimization problem may include a cost function having constraints based on a desired temperature setpoint and a humidity ratio for the zone. A value is set for each control command based on the performance of the constrained optimization problem to achieve at least one of the objectives and as a result, the supply air flow at the air temperature and humidity ratio is provided by the HVAC system at the future time to the zone based on the values.