Middle East 2050 Grid Capacity Forecast Agent with OpenAI Agents SDK
Long-term grid planning across the Middle East requires modeling explosive power demand from sovereign AI datacenters and desalinization plants. Build an autonomous agent using OpenAI Agents SDK with function calling to execute load-forecasting simulations and recommend regional grid interconnect capacity, achieving sub-5% MAPE on 2030-2050 projection benchmarks.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an autonomous grid planning agent using OpenAI Agents SDK with function calling to forecast 2050 regional Middle East electricity demand.
How work is evaluated
Evaluates power demand forecast error (MAPE) and grid expansion recommendations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
forecast_doubling_check
Must project demand doubling (multiplier >= 2.0) by 2050 in line with Rystad report
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mape_error_rate
Mean Absolute Percentage Error relative to baseline scenario benchmark • target: 4.5 • range: 0-20
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Utilize OpenAI Agents SDK (`Runner`, `Agent`, `@function_tool`) for autonomous power simulation
Design load forecast simulation tools modeling AI datacenter growth and desalination growth
Formulate GCC Interconnection Grid transfer expansion recommendations
Evaluate load model accuracy using Mean Absolute Percentage Error (MAPE) targets
Reference links and supporting material
Middle East 2025-2050 power demand projections, AI datacenter MW schedules, and Rystad Energy research data.
Challenge setup
Evaluates power demand forecast error (MAPE) and grid expansion recommendations.
Challenge input
JSON containing baseline load GW, AI load growth factor, and target forecast year.
OpenAI Agents SDK
Required OpenAI framework for autonomous function-calling agents
Evaluated output
JSON containing projected demand GW, required solar/nuclear mix %, and interconnect recommendation.
- Must project demand doubling (multiplier >= 2.0) by 2050 in line with Rystad report
- Mean Absolute Percentage Error relative to baseline scenario benchmark • target: 4.5 • range: 0-20
- Benchmark: Energy Grid Forecasting Benchmark
- Mape Error Rate target: 4.5
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- OpenAI · Required
- Deepgram · Optional
- Adept · Optional
Evaluation contract
- forecast_doubling_check · Weight 1
- mape_error_rate · Weight 1
Metrics
- mape_error_rate · Target 4.5
Benchmarks
- Energy Grid Forecasting Benchmark
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing baseline load GW, AI load growth factor, and target forecast year.
Output: JSON containing projected demand GW, required solar/nuclear mix %, and interconnect recommendation.
Execution harness: Python
Evaluation tools
- OpenAI Agents SDK · Agent Framework
Recipe freeze marker
No recipe freeze time is recorded.