Cooling System Control Using Historical Data for Lower Power Use
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Solution Overview
Problem
Cooling systems consume excessive energy due to sub-optimal control variable settings based on manufacturer suggestions and personal experience, lacking consideration for historical data and the impact on air handling units (AHUs).
Innovation Solution
A cooling control system that monitors various components using sensors to collect historical data, calculates optimal setpoints for control variables, and explores new values to minimize power consumption, incorporating a control module that adjusts and optimizes settings for efficient operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If control variables are set based on manufacturer suggestions and personal experience, then the system is easy to operate, but power consumption is excessive
Solution Approach 1:
The cooling control system automatically monitors its own operation, collects historical data, determines optimal setpoints, and adjusts control variables without external intervention. This self-service capability eliminates the need for manual expert configuration while achieving optimal energy efficiency through continuous automated optimization.
Solution Approach 2:
The system continuously monitors control variable values and power consumption, uses this feedback to determine optimal setpoints through data analysis, and adjusts control variables accordingly. This closed-loop feedback mechanism enables the system to learn from historical operations and continuously improve energy efficiency automatically.
2Productivity
If manufacturer performance curves are used for optimization, then the approach is simple to implement, but the result is sub-optimal
Solution Approach 1:
The patent replaces the mechanical engineering approach using manufacturer performance curves with a data-driven computational approach. Instead of relying on theoretical models and curves, the system uses actual historical operational data and machine learning algorithms to determine optimal control variable setpoints, achieving superior optimization effectiveness.
Solution Approach 2:
The system transitions from using fixed manufacturer-specified parameters to dynamically adjusting control variable setpoints based on analyzed historical data. By changing the approach from static theoretical parameters to dynamic data-derived parameters, the system achieves better optimization while managing complexity through automated data processing.
3Use of energy by moving object
If historical data is collected and analyzed to determine optimal setpoints, then power consumption is minimized, but the system complexity increases
Solution Approach 1:
The cooling control system performs multiple functions using the same data infrastructure: it monitors control variables, tracks power consumption, determines optimal setpoints, and adjusts control variables. This multi-functionality reduces overall system complexity by consolidating operations into a unified platform rather than requiring separate systems for each function.
Solution Approach 2:
The system automatically collects historical data, analyzes it to determine optimal setpoints, and implements adjustments without external intervention. This self-service capability eliminates the need for complex manual data collection and analysis processes, reducing operational complexity while achieving minimal power consumption through continuous optimization.
Data Source
AI summary
The present invention is directed to an apparatus for minimizing power consumption in a cooling system. In one embodiment, the apparatus comprises one or more processors, one or more sensors associated with one or more regulated environments and one or more chillers that regulate temperature of the one or more regulated environments and a storage device, coupled to the one or more processors, storing instructions that when executed by the one or more processors performs a method. The method comprises gathering readings from the one or more sensors, determining a cost and power consumption associated with setting values for a plurality of control variables associated with the one or more chiller plants, selecting values for the control variables with a minimum cost as optimized control variable values and applying the optimized control variable values to the plurality of control variables to minimize power consumption of the cooling system.