Building Control Setpoints for Fan-Chiller Energy Optimization
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Solution Overview
Problem
Building management systems face complexity and inefficiency in maintaining comfort conditions due to the trade-offs between fan and chiller energy costs, with conventional control structures optimizing for constant setpoints rather than holistic cost minimization.
Innovation Solution
A building management system that employs self-optimizing control, using an analytics circuit to determine a self-optimizing control function based on measured states, adjusting inputs to minimize total energy costs, and monitoring performance to detect faults and optimize equipment operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional feedback control structures are used to maintain constant setpoints, then control objectives are satisfied, but optimization cost function measures vary with system operating points and disturbances leading to increased energy costs
Solution Approach 1:
The system uses self-optimizing control where the control structure automatically adjusts setpoints based on real-time operating conditions without requiring external optimization layers. The controller monitors system state and autonomously modifies setpoints to minimize energy costs while maintaining comfort, enabling the system to serve its own optimization needs
Solution Approach 2:
The patent implements dynamic setpoint adjustment where setpoints are no longer fixed constants but vary continuously based on system operating points and disturbances. This allows the control system to adapt to changing conditions (occupancy, weather, equipment state) and maintain optimization across varying operational scenarios
2Use of energy by moving object
If real-time optimization 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 implementing self-optimizing control algorithms within the controller itself. This integration eliminates the need for a separate real-time optimization layer, reducing system complexity while maintaining the ability to minimize energy costs through dynamic setpoint adjustment
3Use of energy by moving object
If real-time optimization layer is added to adjust setpoints for cost minimization, then energy costs are reduced, but computational requirements increase
Solution Approach 1:
The control system performs self-optimization using computationally efficient algorithms that can be executed within the existing control infrastructure. By avoiding external optimization layers and using streamlined calculations based on real-time sensor data, the system achieves cost minimization without imposing excessive computational burdens
Data Source
AI summary
A building management system includes building equipment configured to operate in accordance with an input to alter a variable state or condition of a building, a feedback controller configured to generate the input as a function of a measured state of the building equipment, and an analytics circuit. The analytics circuit is configured to obtain and store a dataset comprising the measured state and the input for a plurality of time steps, determine, based on at least a portion of the dataset, a self-optimizing control function that defines a self-optimizing control variable as a function of the measured state, calculate a value of the self-optimizing control variable using the self-optimizing control function and the measured state, monitor the value of the self-optimizing control variable over time, and generate an indication of performance of the building equipment relative to optimal performance based on the value of the self-optimizing control variable.