Data Center Workload Scheduling Using Predictive Weather Models
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Solution Overview
Problem
The challenge lies in efficiently selecting sites for renewable energy power systems and managing computational workloads in data centers powered by renewable resources, as existing methods rely on coarse data and long-term site selection processes, leading to variable power output and potential inadequacies in meeting demand.
Innovation Solution
Employing predictive weather and climate models for site selection and workload scheduling, using global and local simulations to model renewable energy availability, and leveraging historical and real-time data to estimate power output, allowing for precise placement of power systems and adaptive scheduling of computational workloads based on predicted energy generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If coarse valued data from established reporting sites is used for site selection, then the site selection process is simpler and faster, but the precision and accuracy of site selection is insufficient
Solution Approach 1:
The patent applies preliminary action by using predictive weather and climate models to pre-assess renewable energy availability at potential sites before physical deployment. This allows the system to identify promising locations in advance using simulated data, so that when instrumentation is deployed, the site is already validated, reducing both time and improving precision.
Solution Approach 2:
The patent uses copying by creating virtual models of weather and climate conditions through predictive simulations. Instead of relying solely on coarse historical data from reporting sites, the system generates detailed predictive copies of expected renewable energy resources at prospective locations, enabling more precise site selection without requiring long-term physical monitoring at each site.
2Reliability
If renewable energy resources are used to generate power, then environmental sustainability is improved, but power output becomes variable and may not meet demand
Solution Approach 1:
The patent applies preliminary action by predicting renewable energy resource availability in advance using weather and climate models. This allows the computational workload to be scheduled beforehand to match expected power generation, ensuring that computational tasks are performed when power is available and preventing power shortages.
Solution Approach 2:
The patent applies dynamics by making the computational workload schedule adaptive and flexible rather than fixed. The scheduling system dynamically adjusts workload allocation based on predicted renewable energy availability, allowing the system to respond to varying power generation conditions while maintaining overall reliability.
3Productivity
If computational workloads are scheduled based on predicted power output, then energy usage efficiency is improved, but the scheduling system becomes more complex
Solution Approach 1:
The patent applies universality by designing a scheduling system that performs multiple functions: it predicts weather conditions, estimates renewable energy availability, optimizes computational workload allocation, and adjusts schedules dynamically. This multi-functional approach consolidates what could be separate complex systems into a unified framework, improving energy efficiency while managing complexity through integration.
Data Source
AI summary
A method described herein includes an act of receiving data that is indicative of predicted weather conditions for a particular geographic region, wherein the particular geographic region has an energy generation system therein, and wherein the energy generation system utilizes at least one renewable energy resource to generate electrical power. The method also includes the act of scheduling a computational workload for at least one computer in a data center based at least in part upon the data that is indicative of the predicted weather conditions for the particular geographic region.