Solar irradiance and wind-speed forecasting with Python-based machine-learning pipelines — for predictive maintenance, scheduling, deviation-settlement and revenue optimisation.
Capabilities
Each capability is mapped to industry-standard tools — and to the rest of your project lifecycle, so deliverables flow without seams.
Five-minute to one-day generation forecasts feeding intra-day market bids and ramp-rate compliance.
Day-ahead forecasts coupled to PPA and DISCOM scheduling protocols, with deviation-settlement-aware bidding.
ML models on SCADA telemetry to flag inverter, gearbox and transformer degradation before failure.
Storage dispatch, hybrid-plant arbitrage and ancillary-service bidding driven by forecast confidence.
How we work
No hand-offs to sub-contractors mid-project. The engineer who scopes the work signs the deliverable.
Pull NWP, satellite, plant SCADA and metering data into a unified time-series store.
Train ensemble forecasters; benchmark against persistence and DISCOM-published curves.
Containerised inference with retraining on rolling windows; alerting on drift.
Couple forecasts to dispatch and bidding logic; iterate on revenue / penalty outcomes.
Tell us the scope and timeline — we'll route you to the senior engineer who owns this discipline.