Multi-dimensional optimization for controlling environmental maintenance modules
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Solution Overview
Problem
Current HVAC systems in data centers operate inefficiently due to decentralized controls and excess units, leading to energy waste and difficulty in optimizing operation levels, especially with a large number of actuators and constraints, which existing optimization methods like Nelder-Mead and pattern search algorithms struggle to address effectively.
Innovation Solution
A system and method that uses a predictor model to analyze changes in operation levels of HVAC units based on sensor measurements, identifying optimal changes that keep sensor values within desired ranges while minimizing energy consumption by analyzing points in an N-dimensional space and incorporating a cost function that accounts for energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If decentralized stand-alone controls are used for each HVAC unit, then each unit can independently control temperature and humidity, but energy waste increases due to redundant units operating simultaneously
Solution Approach 1:
The patent merges decentralized HVAC controls into a centralized coordination system that optimizes the operation of multiple units collectively. The system coordinates start/stop sequences and operates a subset of available units based on overall system state, transforming independent control decisions into a unified optimization problem that reduces redundant energy consumption while maintaining temperature and humidity control.
Solution Approach 2:
The system dynamically adjusts which HVAC units operate and at what levels based on real-time sensor feedback and system state. Rather than static decentralized control, the system continuously reoptimizes the operating subset of units, adapting to changing environmental conditions and load requirements to minimize energy waste while maintaining control capabilities.
2Reliability
If excess HVAC units are operated at all times for reliability, then adequate cooling is ensured, but energy consumption increases
Solution Approach 1:
The patent applies partial action by operating only a subset of available HVAC units rather than all units simultaneously. The system determines the minimum necessary operating subset based on current cooling demands and environmental conditions, allowing redundant units to remain idle or operate at reduced capacity, thereby maintaining reliability while reducing overall energy consumption.
Solution Approach 2:
The system changes the operational parameters of the HVAC system by dynamically adjusting which units are active and at what power levels. Rather than maintaining constant full operation of all units, the system varies the operating state of individual units based on optimization criteria, achieving the same reliability outcome with lower total energy consumption.
3Loss of energy
If manual shutdown of redundant HVAC units is performed to save energy, then energy waste is reduced, but the risk of overheating equipment increases
Solution Approach 1:
The patent implements a feedback-based optimization system that continuously monitors sensor data from the environment and adjusts HVAC unit operation accordingly. The system uses real-time temperature and humidity measurements to determine the appropriate operating subset, ensuring that energy-saving shutdown decisions do not compromise equipment cooling requirements. This closed-loop feedback mechanism eliminates the overheating risk associated with manual shutdown approaches.
Solution Approach 2:
The system performs preliminary optimization calculations to determine the optimal subset of HVAC units to operate before actual operation begins. By pre-calculating the appropriate configuration based on current conditions and predicting future demands, the system ensures that sufficient cooling capacity is maintained while minimizing energy waste, preventing overheating before it can occur.
4Productivity
If existing optimization methods like Nelder-Mead or pattern search algorithms are used, then optimization can be performed, but the complexity of high-dimensional spaces with many actuators and constraints makes effective optimization difficult
Solution Approach 1:
The patent segments the high-dimensional optimization problem into more manageable components by formulating it as a mixed-integer quadratic programming problem with specific structural properties. The segmentation approach divides the complex optimization space into discrete unit selection decisions and continuous power level adjustments, allowing the use of specialized algorithms that can handle the combinatorial nature of subset selection while maintaining efficiency in high-dimensional spaces.
Data Source
AI summary
Methods, systems, and apparatuses are provided for controlling an environmental maintenance system that includes a plurality of sensors and a plurality of actuators. The operation levels of the actuators can be determined by optimizing a cost function subject to a constraint, e.g., having no more than a certain number of sensors that are out of range. A predictor model can predict whether certain operation levels of the actuators violate the constraint. The search for acceptable operation levels (i.e., ones that do not violate constraints) can be performed by analyzing points on lines in an N-dimensional space, where N is the number of actuators. The subset of acceptable operation levels along with a cost function (e.g., that incorporates energy consumption information) can be used to change operation levels of the modules to keep the temperatures within a desired range while using minimal energy.