Self-Optimizing HVAC Control for Energy Cost and Comfort
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Solution Overview
Problem
Conventional building management systems face challenges in optimizing energy costs while maintaining comfort conditions due to the complexity and redundancy in HVAC systems, where conventional feedback control structures often fail to adjust system operation effectively to minimize total costs.
Innovation Solution
A self-optimizing control system that includes a self-optimizing controller configured to generate performance and output variable models using regression techniques, determining gradients to operate building equipment efficiently, thereby optimizing energy usage and cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedback control structures are used to maintain setpoints, then control objectives are satisfied, but optimization cost function measures vary with system operating points and disturbances
Solution Approach 1:
The system employs self-optimizing controllers that automatically adjust control variables to minimize cost functions without requiring external optimization layers. Each controller serves itself by continuously adapting to operating conditions and disturbances, eliminating the need for complex centralized optimization systems while maintaining both control reliability and cost optimization
2Loss of energy
If an RTO layer is added to adjust setpoints for cost minimization, then energy costs are reduced, but computational requirements and system complexity increase
Solution Approach 1:
The patent merges the optimization functionality directly into the existing control layer by equating the control variable with the optimization variable. This integration eliminates the need for a separate RTO layer, achieving cost minimization through the same control structure that maintains setpoints, thereby reducing computational requirements and system complexity while still reducing energy costs
Solution Approach 2:
The control system is designed to perform multiple functions simultaneously: it maintains control objectives (temperature, humidity, etc.) while also minimizing energy costs through the same control variables. This multi-functionality eliminates the need for separate optimization systems and reduces overall system complexity
3Loss of energy
If multiple control variables are adjusted for optimization, then cost minimization is achieved, but control structure complexity increases
Solution Approach 1:
The patent extracts and focuses on a single critical control variable that directly influences the cost function (such as supply air temperature or fan speed). By concentrating optimization efforts on this key variable rather than adjusting multiple variables simultaneously, the system achieves cost minimization while maintaining simple control operation and avoiding the complexity of multi-variable optimization
Data Source
AI summary
A self-optimizing controller for equipment of a plant provides a manipulated variable as an input to the plant and receives an output variable as feedback. The controller generates a performance variable model defining the performance variable as a function of the manipulated variable and an output variable model defining the output variable as a function of the manipulated variable. The controller uses the performance variable model to determine a gradient of the performance variable, uses the output variable model to determine a gradient of the output variable, and generates a self-optimizing variable based on the gradient of the performance variable model and the gradient of the output variable model. The controller operates the equipment of the plant to affect a variable state or condition of the building based on the value of the self-optimizing variable from the self-optimizing variable model.