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Challenge

Build a Bifacial Solar Array Albedo Tracking Optimizer

Model rear-side irradiance on bifacial photovoltaic arrays against seasonal ground albedo data using pvlib. Generate optimal daily tracking angles using Optuna to maximize combined front and rear energy yield.

EngineeringHosted by Vera
Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Model rear-side irradiance on bifacial photovoltaic arrays against seasonal ground albedo data using pvlib. Generate optimal daily tracking angles using Optuna to maximize combined front and rear energy yield.

Solar Farm Operations Engineer

Ingest seasonal ground albedo telemetry, weather forecasts, and array layout parameters.

Calculate front and rear irradiance utilizing pvlib bifacial performance models.

Identify tracking angles that maximize total panel generation under diffuse light.

Synthesize an updated daily tracking dispatch schedule for array actuators.

A web dashboard displaying seasonal ground albedo trends, diffuse light fractions, and recommended dual-axis tracking adjustments.

Create synthetic normalized records modeling site ground albedo, diffuse fraction, solar zenith, and bifacial panel specifications in SQLite. These serve as simplified synthetic app records, not complete official schema files. Starter examples below are synthetic. They are not downloaded records.

PVLib solar modeling Applies pvlib bifacial modeling features to calculate front and rear plane-of-array irradiance.

optuna, pvlib, fastapi, pydantic. Optimizes tracking tilt angles across PV strings subject to combined front and rear generation non-linearities and albedo shifts. Fast reasoning API or local 8B parameter model for explaining tracking trade-offs and rear-side yield margins.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Calculates combined front and rear plane-of-array irradiance.

Enforces actuator tracking limits and collision constraints.

Outputs hourly tracking angles that increase theoretical yield over standard front-only tracking algorithms.

Requires human validation before updating actual field controllers.

```json [ { "input": { "site_id": "BIFACIAL-01", "ground_albedo": 0.25, "diffuse_fraction": 0.4, "solar_zenith": 35 }, "expected_output": { "optimal_tilt_angle": 30, "front_irradiance_w_m2": 700, "rear_irradiance_w_m2": 85, "total_yield_gain_percent": 4.2 } }, { "input": { "site_id": "BIFACIAL-02", "ground_albedo": 0.6, "diffuse_fraction": 0.8, "solar_zenith": 45 }, "expected_output": { "optimal_tilt_angle": 15, "front_irradiance_w_m2": 400, "rear_irradiance_w_m2": 150, "total_yield_gain_percent": 12.5 } } ] ```

All tracking routes are simulated suggestions; no actual robotic actuators are triggered without human engineering verification. Use mock external writes. Require human review before any real deployment.

Rules

Participation guide

All tracking routes are simulated suggestions; no actual robotic actuators are triggered without human engineering verification. Use mock external writes and human review.

Delivery guide

How work is evaluated

Evaluation

Calculates combined front and rear plane-of-array irradiance. Enforces actuator tracking limits and collision constraints. Outputs hourly tracking angles that increase theoretical yield over standard front-only tracking algorithms. Requires human validation before updating actual field controllers.

Submission format

Runnable source, pinned dependencies, setup commands, working interface, example outputs, recorded demo, tests, and action logs.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
4 scoring checks
Binary
1 pass or fail dimensions
Ordinal
3 scaled dimensions
Dimension 1

Complete workflow

Ingest seasonal ground albedo telemetry, weather forecasts, and array layout parameters. Calculate front and rear irradiance utilizing pvlib bifacial performance models. Identify tracking angles that maximize total panel generation under diffuse light. Synthesize an updated daily tracking dispatch schedule for array actuators.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 2

Correctness and evidence

Calculates combined front and rear plane-of-array irradiance. Enforces actuator tracking limits and collision constraints. Outputs hourly tracking angles that increase theoretical yield over standard front-only tracking algorithms. Requires human validation before updating actual field controllers.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 3

Review interface

A web dashboard displaying seasonal ground albedo trends, diffuse light fractions, and recommended dual-axis tracking adjustments.

Score scale
012
Score 0

Missing or incorrect.

Score 1

Partly complete, with documented gaps.

Score 2

Complete, with reproducible evidence.

Dimension 4

Safe execution

All tracking routes are simulated suggestions; no actual robotic actuators are triggered without human engineering verification.

Binary check

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

Learning goals

What you should walk away with

  • Ingest seasonal ground albedo telemetry, weather forecasts, and array layout parameters.

  • Calculate front and rear irradiance utilizing pvlib bifacial performance models.

  • Identify tracking angles that maximize total panel generation under diffuse light.

  • Synthesize an updated daily tracking dispatch schedule for array actuators.

Resources and assets

Reference links and supporting material

Dataset notes

Create synthetic normalized records modeling site ground albedo, diffuse fraction, solar zenith, and bifacial panel specifications in SQLite. These serve as simplified synthetic app records, not complete official schema files. Supplied starter examples are synthetic, not source extracts.

Challenge setup

Configuration summary for this challenge.

Challenge input

Input details are not configured.

Optuna

Hyperparameter Optimization

Evaluated output

No output checks are configured.

Evaluation checks
  • Ingest seasonal ground albedo telemetry, weather forecasts, and array layout parameters. Calculate front an...
  • Calculates combined front and rear plane-of-array irradiance. Enforces actuator tracking limits and collisi...
  • A web dashboard displaying seasonal ground albedo trends, diffuse light fractions, and recommended dual-axi...
Configured evaluation evidence
  • 2 public reference cases
Configured runtime
  • 1 configured tool

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

Action Space
  • Optuna · Optional

Evaluation contract

  • Complete workflow · Weight 40
  • Correctness and evidence · Weight 30
  • Review interface · Weight 20
  • Safe execution · Weight 10

Reference cases

  • 2 public reference cases

Workflow tasks

No workflow tasks are configured.

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a Bifacial Solar Array Albedo Tracking Optimizer