End User
PPC
Description
This service analyzes historical inverter and string data (current, voltage, temperature, power output) to predict potential inverter failures and recommend optimal spare parts procurement strategies. By identifying failure patterns and estimating time-to-failure, the AI models help minimize inventory costs, reduce downtime, and ensure timely availability of critical components.
Core Capabilities
Optimization & Decision Support
Business Need
The AI-Powered Spare Parts Procurement Recommendations service addresses the significant challenges associated with managing spare parts inventory for PV inverters. Inefficient spare parts management can lead to a multitude of problems, including high inventory costs due to overstocking of unnecessary parts, increased downtime of PV systems due to the unavailability of critical components when needed, and delays in repair and maintenance activities, ultimately resulting in lost energy production and revenue.
Key Performance Indicators
Accuracy of Failure Prediction (Precision, Recall, F1-score, MAE/RMSE of time-to-failure)
Reduction in downtime due to spare parts availability
Data Provided
Historical generation data of the inverters - CSVs - unknown size - no documentation available - 15 min resolution
Historical generation data of strings - CSVs - unknown size - no documentation available - 15 min resolution
Operations Log – Report - unknown size – with documentation – monthly resolution
TEF
TEF RES