Next-generation AI architecture designed to predict solar PV generation with high fidelity—even under severe sensor missingness, equipment anomalies, and volatile telemetry.
Industrial solar power plants depend on on-site pyranometers, ambient temperature sensors, and inverter telemetry. Harsh environmental conditions and network cutoffs lead to frequent sensor data drops.
Standard forecasting models degrade rapidly under missing dimensions. KunPredict provides an uncertainty-aware, sensor-resilient forecasting pipeline coupled with generative AI reasoning.
Uninterrupted inference even under 40%+ telemetry missingness.
P10, P50, and P90 uncertainty bands for smart grid scheduling.
Integrating Claude's multi-step reasoning capabilities to ingest SCADA alarms, cross-reference environmental anomalies, and generate automated diagnostic summaries for grid engineers.
KunPredict AI bridges graduate-level machine learning research with real-world solar energy utility management.
Rooted in ongoing M.Sc. thesis research in Artificial Intelligence & Data Mining, focusing on "Sensor Fault-Tolerant Probabilistic Power Forecasting in Photovoltaic Systems."
Employing quantile regression gradient boosting architectures paired with dynamically conditioned missingness vectors to eliminate single-point-of-failure risks.
Leveraging Claude API for automated telemetry diagnostics, translating complex multi-sensor time-series variations into plain-language root-cause analyses.
Currently validating models against utility-scale datasets and expanding agentic AI workflows for renewable grid systems.