Data Center HVAC Thermal Optimization Using Predictive Load Control
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Solution Overview
Problem
Existing HVAC systems in data centers often operate inefficiently due to fixed temperature setpoints, leading to increased energy consumption and reduced operational efficiency.
Innovation Solution
A thermal optimization control system that processes real-time and predicted IT load data to dynamically adjust HVAC system operations, optimizing chiller plant efficiency and reducing energy usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fixed temperature setpoints are used in HVAC systems, then temperature stability is maintained, but energy consumption increases
Solution Approach 1:
The patent implements dynamic temperature setpoints that automatically adjust based on real-time thermal load conditions. The system transitions from static fixed setpoints to dynamic adaptive setpoints that change according to actual cooling需求的, thereby reducing energy consumption while maintaining temperature stability within acceptable ranges.
Solution Approach 2:
The system changes the temperature parameter dynamically based on thermal load conditions. When thermal load is high, the setpoint temperature is adjusted to provide adequate cooling; when thermal load is low, the setpoint is relaxed to reduce energy consumption, thus optimizing the balance between temperature stability and energy efficiency.
2Reliability
If HVAC systems operate at full capacity continuously, then temperature control reliability is improved, but energy usage increases
Solution Approach 1:
The system applies partial cooling action only when thermal load conditions require it, rather than operating at full capacity continuously. The dynamic setpoint adjustment enables the HVAC system to operate at reduced capacity during low thermal load periods while maintaining adequate temperature control, thereby reducing energy losses.
Solution Approach 2:
The system uses real-time thermal load monitoring as feedback to dynamically adjust HVAC operation. The thermal load information feeds back to the control system, which then adjusts the temperature setpoints and HVAC capacity accordingly, ensuring reliable temperature control only when and where needed, thus optimizing energy usage.
3Use of energy by moving object
If dynamic temperature adjustment is implemented, then energy efficiency is improved, but system complexity increases
Solution Approach 1:
The patent introduces a thermal optimization control system as an intermediary layer between the thermal load conditions and the HVAC system. This intermediary processes thermal load information and generates optimized temperature setpoints, simplifying the control logic while achieving dynamic adjustment and improved energy efficiency without excessive system complexity.
4Productivity
If thermal load prediction is used, then HVAC optimization is improved, but measurement and detection difficulty increases
Solution Approach 1:
The system performs preliminary thermal load prediction and assessment before making HVAC adjustments. By anticipating thermal load conditions and preparing optimized setpoints in advance, the system achieves better HVAC optimization while managing the measurement and detection complexity through proactive rather than reactive control.
Data Source
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AI summary
A thermal optimization control (32) includes processing circuitry. The processing circuitry is operable to receive information from a building (20) housing at least one computer server, with the information including at least current thermal load. The processing circuitry is operable to control an associated HVAC system for at least an area (22) of the building (20) where the at least one computer server is housed. The processing circuity is operable to optimize the operation of the HVAC system based upon the current and an expected upcoming thermal load on the area (22) of the building (20) where the server is received to reduce energy usage by the HVAC system. A method and building (20) are also disclosed.