Intelligent Energy Management System for Component-Level Power Demand Estimation
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Solution Overview
Problem
Current power demand estimation methods for information handling systems rely on simplistic assumptions, such as system count and operational hours, leading to inefficiencies in power savings policies and increased energy costs, as they fail to accurately account for component device utilization and productivity impacts.
Innovation Solution
An intelligent energy management system that monitors and analyzes component device utilization data, including power draw and activity levels, using sensors and data repositories like Dell Data Vault, to implement targeted power savings policies that optimize energy usage without impacting productivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplistic power demand estimation methods are used (based on system count and operational hours), then the complexity of the energy management system is reduced, but the accuracy of power demand estimation deteriorates
Solution Approach 1:
The patent segments power demand estimation into component-level analysis (CPU, memory, storage, network devices) rather than treating the system as a whole. Each component's power consumption is estimated separately based on its utilization metrics, enabling accurate aggregate power demand calculation while maintaining manageable system complexity through modular assessment approaches.
Solution Approach 2:
The system performs preliminary characterization of component power consumption patterns during low-utilization states and establishes baseline profiles. This preliminary action enables accurate real-time power demand estimation by comparing current utilization metrics against pre-established component behavior profiles, eliminating the need for complex continuous monitoring while maintaining high accuracy.
2Loss of energy
If targeted power savings policies are implemented based on detailed component monitoring, then energy cost reduction is improved, but the complexity of policy implementation increases
Solution Approach 1:
The patent applies different power savings policies to specific components based on their individual utilization patterns and productivity criticality. Rather than implementing uniform system-wide policies, the system tailors power management actions to local component conditions, applying aggressive savings measures to non-critical components while maintaining performance on productivity-critical components.
Solution Approach 2:
The system dynamically adjusts power policy parameters (such as threshold values, target power levels, and policy aggressiveness) based on changing component utilization patterns and productivity requirements. This enables adaptive power savings implementation that automatically responds to varying system conditions without requiring manual policy reconfiguration.
3Measurement precision
If component device utilization data is monitored in detail, then power demand estimation accuracy is improved, but the amount of data to be processed increases
Solution Approach 1:
The patent extracts only the essential utilization metrics needed for power demand estimation from component device data, rather than processing complete raw data streams. By identifying and extracting key performance indicators directly related to power consumption (such as CPU utilization percentage, memory usage levels, storage I/O rates), the system achieves accurate power estimation while minimizing data processing requirements.
Solution Approach 2:
The system performs preliminary aggregation and filtering of component utilization data to extract only the metrics relevant for power demand calculation. This preliminary data processing reduces the volume of information that requires detailed analysis while preserving the accuracy needed for reliable power estimation.
Data Source
AI summary
An information handling system includes an application processor that executes instructions of an intelligent energy management system that determines energy demand estimation based on component device utilization data from a group of client information handling systems. The information handling system includes a power policy engine that determines a timeseries power cost estimation based on the energy demand estimation, day and time of energy usage, and energy rate for the time and location.