Green Hydrogen Feasibility Assessor with Pydantic AI & Edge Impulse
Clean energy planners require type-safe assessment workflows that incorporate edge sensor readings to re-evaluate shelved hydrogen assets. Build a structured feasibility agent using Pydantic AI and Edge Impulse runtime model data to classify operational metrics.
What you are building
The core problem, expected build, and operating context for this challenge.
Design a type-safe Pydantic AI workflow connected to Edge Impulse sensor classifications to evaluate green hydrogen plant viability.
How work is evaluated
Evaluates Pydantic AI validation strictness and feasibility classification precision.
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.
type_safety_verification
Ensures returned payload strictly adheres to FeasibilityResult Pydantic class.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
lcoh_calculation_accuracy
Accuracy of Levelized Cost of Hydrogen estimation. • target: 0.98 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement type-safe agent schemas with Pydantic AI validation
Integrate Edge Impulse inferencing output into agent structured context
Evaluate electrolyzer degradation and power cost tipping points
Enforce explicit output types for clean energy investment decisions
Reference links and supporting material
Levelized Cost of Hydrogen (LCOH) parameters, solar PV tariff curves, electrolyzer health metrics, and Edge Impulse vibration anomaly logs.
Challenge setup
Evaluates Pydantic AI validation strictness and feasibility classification precision.
Challenge input
JSON with power_cost_usd_mwh, electrolyzer_efficiency_pct, and membrane_vibration_edge_state
Pydantic AI
Framework ensuring strict type-safety and Pydantic validation.
Edge Impulse
Provides tinyML vibration and anomaly detection classification.
Evaluated output
JSON with lcoh_usd_kg, recommendation (PROCEED|SHELVE|RENEGOTIATE_POWER), and confidence_score
- Ensures returned payload strictly adheres to FeasibilityResult Pydantic class.
- Accuracy of Levelized Cost of Hydrogen estimation. • target: 0.98 • range: 0-1
- Benchmark: CleanEnergy-Econ-Eval
- Lcoh Calculation Accuracy target: 0.98
- 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.
View technical recipe
Configured tools
- Pydantic AI · Required
- Edge Impulse · Optional
- Weights & Biases · Optional
- Pydantic AI · Required
Evaluation contract
- type_safety_verification · Weight 1
- lcoh_calculation_accuracy · Weight 1
Metrics
- lcoh_calculation_accuracy · Target 0.98
Benchmarks
- CleanEnergy-Econ-Eval
- energy
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON with power_cost_usd_mwh, electrolyzer_efficiency_pct, and membrane_vibration_edge_state
Output: JSON with lcoh_usd_kg, recommendation (PROCEED|SHELVE|RENEGOTIATE_POWER), and confidence_score
Execution harness: Python
Evaluation tools
- Pydantic AI · agent_framework
- Edge Impulse · edge_inference
Recipe freeze marker
No recipe freeze time is recorded.