HVAC Model Predictive Control Using Selectable Linear Thermal Models
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Solution Overview
Problem
Existing model predictive control (MPC) technologies for HVAC systems require excessive computational resources and are impractical for widespread implementation due to complex thermal models and high processing demands.
Innovation Solution
The use of linear thermal models allows for the selection of appropriate models based on setpoints, operating modes, or time, enabling MPC to be performed on resource-limited devices at the edge of the network, thus reducing computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex thermal models are used for MPC in HVAC systems, then control accuracy is improved, but computational resources and processing demands increase excessively
Solution Approach 1:
The patent segments the complex thermal model into multiple simplified linear thermal models, each representing specific operating conditions (heating mode, cooling mode, transition periods). This segmentation allows the system to use simple linear models for routine operations while maintaining accuracy during transitions by selecting the appropriate model segment.
Solution Approach 2:
The system dynamically selects between different linear thermal models based on current operating conditions (temperature setpoints, mode of operation, time of day). This dynamic model selection allows the system to adapt to changing conditions without requiring a single complex model, thereby maintaining accuracy while keeping computational requirements low.
2Measurement precision
If complex thermal models are used for MPC in HVAC systems, then control accuracy is improved, but implementation becomes impractical for widespread deployment
Solution Approach 1:
The patent replaces expensive, complex thermal models with multiple inexpensive linear thermal models that can be easily deployed and replaced. These simplified models require minimal computational resources and can be implemented on standard HVAC controllers, making widespread deployment practical and cost-effective.
Solution Approach 2:
The system changes the parameters of the thermal models from complex non-linear relationships to simple linear parameters that can be easily calculated and stored. This parameter simplification maintains sufficient accuracy for HVAC control while dramatically reducing computational complexity and implementation barriers.
3Ease of manufacture
If resource-limited devices are used for MPC at the network edge, then deployment cost is reduced, but computational capability is constrained
Solution Approach 1:
The patent segments the computational task into model selection (based on simple threshold comparisons of operating conditions) and model execution (using pre-computed linear models). This segmentation allows resource-limited edge devices to perform only the simple selection logic while the computationally intensive model calculations are done offline, enabling deployment on low-cost devices.
Data Source
AI summary
An example control device includes memory storing a first thermal model of a building and a second thermal model of the building, and a processor connected to the memory. The processor is configured to select the first thermal model or the second thermal model as a selected thermal model, and execute model predictive control (MPC) using the selected thermal model to control a heating, cooling, ventilation, and/or air conditioning (HVAC) apparatus at the building.