Data Center Cooling Model Prediction Using Real-Time Sensor Feedback
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Solution Overview
Problem
Current data center management systems lack the ability to accurately predict cooling performance and airflow rates, leading to inefficiencies in cooling systems and increased energy costs due to the inability to incorporate real-time temperature and airflow measurements into predictive models.
Innovation Solution
A computer-implemented method and system that adjusts cooling models using measured inlet and exhaust air temperature values and airflow percentages to predict future temperature values, incorporating error checking and factors to reduce differences between measured and predicted values, and includes a tool for data center design and management to optimize cooling performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional predictive models are used without real-time measurements, then the system is simpler to operate, but the prediction accuracy of temperature and airflow values deteriorates
Solution Approach 1:
The system incorporates real-time temperature and airflow measurements from sensors into the predictive model, creating a feedback loop where actual measurements continuously adjust and refine the predicted values. This allows the model to adapt to changing conditions and maintain high prediction accuracy without requiring complete redesign of the system architecture.
Solution Approach 2:
The patent introduces an intermediary layer that bridges the gap between simple predictive models and complex measurement systems. This intermediary processes real-time sensor data and integrates it with the predictive model, enabling accurate predictions without directly exposing the complexity of the measurement and adjustment mechanisms to the user.
2Measurement precision
If real-time measurements are incorporated into predictive models, then the prediction accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system implements partial measurement by selecting only the most critical temperature and airflow parameters to measure and incorporate into the model, rather than attempting to measure and process all possible environmental variables. This selective approach maintains prediction accuracy while reducing computational burden.
3Productivity
If cooling models are adjusted frequently with real-time data, then the cooling performance is optimized, but the energy consumption for data processing and model adjustment increases
Solution Approach 1:
The system performs model adjustments at periodic intervals rather than continuously, updating the cooling model with real-time measurements at predetermined time intervals. This periodic approach maintains effective cooling performance while significantly reducing the energy consumption associated with constant data processing and model recalculation.
4Reliability
If comprehensive error checking is implemented on temperature and airflow values, then the reliability of predictions improves, but the processing time and system complexity increase
Solution Approach 1:
The system implements selective error checking by applying validation rules only to critical temperature and airflow parameters that have the greatest impact on prediction reliability. This partial validation approach ensures sufficient reliability without the time penalty of comprehensive checking of all possible parameters.
Data Source
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AI summary
A system and method for evaluating cooling performance of equipment in a data center is disclosed. In one aspect, a method includes receiving a plurality of measured inlet and exhaust air temperature values for at least one cooling provider and a subset of the plurality of equipment racks, implementing a cooling model. The method also includes adjusting at least one of an ambient air temperature value and each of a plurality of airflow values in the cooling model, and adjusting the cooling model to compensate for the adjusted at least one of the ambient air temperature value and each of the plurality of airflow values in the cooling model, substituting inlet and exhaust air temperature values in the cooling model with measured inlet and exhaust air temperature values, and predicting plurality of inlet and exhaust air temperature values in the cooling model.