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Local Power & Water Consumption Predictor for Data Centers with OpenAI Agents SDK & LocalAI

Data center permitting faces fierce political headwinds due to regional water usage and electrical load surges. Build a local resource impact prediction system using OpenAI Agents SDK backed by an offline LocalAI inference runtime to compute projected daily water consumption and peak grid demand. Ensure model execution runs fully local with less than 5% prediction error.

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What you are building

Build an offline data center resource consumption modeling tool using OpenAI Agents SDK configured with a LocalAI inference runtime.

How work is evaluated

Evaluation

Evaluates local resource consumption predictions against physical thermodynamics ground truth.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

power_exact

Verifies peak power calculation exactness.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

water_mae

Mean Absolute Error in predicted daily water consumption. • target: 5000 • range: 0-100000

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Configure OpenAI Agents SDK to route requests through LocalAI OpenAI-compatible endpoints

  • Build resource demand modeling tool functions for IT megawatt capacity

  • Compute cooling system water evaporative loss based on local dry-bulb weather inputs

  • Ensure zero external network calls during offline policy evaluation

Resources

Dataset notes

Resource consumption dataset containing weather conditions, cooling system specifications, and measured water/power consumption.

Challenge setup

Evaluates local resource consumption predictions against physical thermodynamics ground truth.

Challenge input

JSON object with datacenter_it_mw, cooling_type, ambient_temp_f, pui_factor

OpenAI Agents SDK

Mandatory assigned agent SDK framework.

LocalAI

Mandatory offline local inference runtime integration.

Evaluated output

JSON object with daily_water_gallons, peak_power_mw

Evaluation checks
  • Verifies peak power calculation exactness.
  • Mean Absolute Error in predicted daily water consumption. • target: 5000 • range: 0-100000
Configured evaluation evidence
  • Benchmark: DataCenterEnergyBench
  • Water Mae target: 5000
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • LocalAI · Required
  • OpenAI · Optional
  • aiXplain · Optional

Evaluation contract

  • power_exact · Weight 1
  • water_mae · Weight 1

Metrics

  • water_mae · Target 5000

Benchmarks

  • DataCenterEnergyBench
  • energy

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with datacenter_it_mw, cooling_type, ambient_temp_f, pui_factor

    Output: JSON object with daily_water_gallons, peak_power_mw

    Execution harness: Python

Evaluation tools

  • OpenAI Agents SDK · Agent Framework
  • LocalAI · Local Inference Server

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Local Power & Water Consumption Predictor for Data Centers with OpenAI Agents SDK & LocalAI