Intelligent Energy Management System for Power Demand Estimation
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Solution Overview
Problem
Current power demand estimation for information handling systems relies on simplistic assumptions, such as the number of systems and hours of operation, leading to suboptimal power saving policies and increased energy costs, as they do not accurately account for component device utilization and activity levels.
Innovation Solution
An intelligent energy management system that monitors and collects data on component device utilization through sensors, using hardware implementation monitoring and reporting systems to provide granular power draw measurements and productivity index determinations, enabling more precise power demand estimation and optimized policy settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simplistic assumptions (number of systems and hours of operation) are used for power demand estimation, then the estimation process is simple and quick, but the accuracy of power demand estimation deteriorates
Solution Approach 1:
The patent segments the power demand estimation by dividing information handling systems into different productivity classes (e.g., high, medium, low productivity) based on component device utilization. This segmentation allows the system to apply different estimation approaches to different groups, improving overall accuracy while maintaining manageable complexity.
Solution Approach 2:
The patent changes the estimation parameters from simple counts (number of systems, hours of operation) to utilization-based parameters (component device utilization, productivity index). This parameter transformation enables more accurate power demand estimation by reflecting actual system usage patterns rather than just quantity and time.
2Measurement precision
If component device utilization monitoring is implemented, then the accuracy of power demand estimation improves, but the device complexity and data collection requirements increase
Solution Approach 1:
The patent implements a universal monitoring framework that collects component device utilization data across multiple system types and configurations. The productivity index determination methodology is designed to work universally across different hardware platforms, reducing the need for system-specific customization and managing complexity through standardization.
Solution Approach 2:
The patent introduces a productivity index as an intermediary metric that aggregates complex component device utilization data into a single representative value. This intermediary simplifies the relationship between detailed monitoring data and power demand estimation, reducing the complexity of data processing while maintaining estimation accuracy.
3Measurement precision
If granular power draw measurements are collected, then the precision of energy demand prediction improves, but the amount of data to be processed and stored increases
Solution Approach 1:
The patent extracts only the essential information needed for power demand estimation from the granular power draw measurements. By focusing on component device utilization patterns and productivity index determination rather than storing all raw measurement data, the system reduces data volume while maintaining the precision needed for accurate estimation.
Solution Approach 2:
The patent performs preliminary processing of power draw measurements by calculating productivity indices and determining utilization patterns before the actual power demand estimation process. This preliminary action transforms raw data into processed metrics that are more compact and directly applicable to estimation algorithms, reducing the burden of data storage and processing.
4Productivity
If productivity index determination is implemented, then the optimization of power saving policies improves, but the computational requirements and processing time increase
Solution Approach 1:
The patent implements productivity index determination at appropriate levels of detail without calculating every possible metric. By focusing on the essential productivity indicators needed for power saving policy optimization rather than comprehensive analysis of all system parameters, the system achieves effective policy optimization with reduced computational overhead and processing time.
Data Source
AI summary
An information handling system includes an application processor that executes instructions of an intelligent energy management system that determines energy demands for an enterprise, application processor determines a statistical model of power demand estimation for a client in the enterprise. The information handling system includes a network adapter that receives component device utilization data from client, and includes a memory device that stores component device utilization data received from the client. The application processor determines power consumption for component devices across the enterprise that has the client for use in the statistical model of consumed power.