HVAC Control Using Transfer Models for Energy-Stable Cooling
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Solution Overview
Problem
Current HVAC systems in data centers operate inefficiently due to decentralized control methods that fail to account for energy costs and redundancy, leading to unnecessary energy waste and instability, especially in variable load conditions and complex airflow patterns.
Innovation Solution
A system utilizing sensors to measure environmental conditions and energy consumption, employing a transfer model to optimize actuator operation levels through predictive and heuristic feedback control, combining multi-rate control strategies to balance energy efficiency and stability.
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 its own temperature and humidity, but energy waste increases due to lack of coordination and awareness of redundant units
Solution Approach 1:
The patent merges decentralized HVAC control units into a coordinated centralized system where a central controller manages multiple units based on global temperature data and energy consumption information, enabling collaborative operation that reduces redundant energy usage while maintaining independent unit capabilities
Solution Approach 2:
The system implements feedback mechanisms where temperature sensors provide real-time data to the central controller, which then adjusts HVAC unit operations accordingly. Energy consumption information is also fed back to optimize the coordination strategy, creating a closed-loop control system that continuously improves energy efficiency
2Reliability
If excess HVAC units are operated at all times for reliability, then adequate cooling is ensured even if a unit fails, but energy consumption increases due to unnecessary operation of redundant units
Solution Approach 1:
The system dynamically adjusts the operational status of HVAC units based on real-time temperature measurements and system state. The central controller can start or stop units as needed, transforming the static 'always-on' redundancy model into a dynamic configuration that maintains reliability while minimizing energy consumption of redundant units
Solution Approach 2:
The patent changes the operational parameters of HVAC units from fixed 'on/off' states to dynamically adjusted states based on temperature thresholds and energy consumption patterns. This allows the system to optimize the number of active units while maintaining adequate cooling capacity for reliability
3Loss of energy
If manual intervention is used to turn off redundant HVAC units, then energy savings can be achieved, but the risk of overheating equipment increases and the system becomes unstable under variable loads
Solution Approach 1:
The system uses continuous temperature feedback from sensors to automatically adjust HVAC unit operation. This eliminates the need for manual intervention by providing real-time data to the central controller, which makes informed decisions about starting or stopping units based on actual thermal conditions, preventing both overheating and unnecessary energy consumption
Solution Approach 2:
The HVAC system performs self-regulation through automated control algorithms that monitor temperature and energy consumption, eliminating the need for manual operator intervention. The system serves itself by automatically optimizing unit operation to balance energy savings with reliability requirements
4Loss of energy
If complex algorithms are attempted to optimize HVAC control, then energy efficiency may improve, but system stability deteriorates
Solution Approach 1:
The patent segments the complex control problem into manageable components: temperature monitoring, energy consumption tracking, unit coordination logic, and start/stop decision-making. This modular approach maintains stability by breaking down complex algorithms into simpler, more predictable control steps that are easier to manage and less prone to instability
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 penalty function. As part of the penalty function, the sensor values can be compared to reference values. The optimized values of the operation levels can account for energy use of actuators at various operation levels and predicted differences of the sensor values relative to the reference values at various operation levels. The predicted difference can be determined using a transfer model. An accuracy of the transfer model can be determined by comparing predicted values to measured values. This accuracy can be used in determining new operational levels from an output of the transfer model (e.g., attenuating the output of the transfer model based on the accuracy).