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Public evaluation
Degradation Prediction Accuracy
The solution will be evaluated based on the accuracy of degradation predictions, the efficiency of the agentic workflow, and the integration of carbon tracking.
Evaluation type
task based
Challenge
BESS Health Monitoring Agent
Difficulty
Intermediate
Rigor
Unspecified
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Tasks
2
Benchmarks
0
Criteria
0
Task templates
Inputs and expected outputs.
Task 1
Degradation Prediction Accuracy
Evaluates the model's ability to predict Remaining Useful Life (RUL) within a 10% error margin on a held-out test set.
Input format
CSV file containing 50 cycles of voltage/current/temp data.
Output format
JSON containing predicted SoH (State of Health) and RUL in cycles.
Task 2
Carbon Footprint Reporting
Checks if the CodeCarbon integration correctly logs emissions during the prediction task.
Input format
Execution log
Output format
emissions.csv file with non-zero energy values