AeroGrid Analytics delivers AI-powered probabilistic generation forecasting and optimal scheduling for India's renewable energy producers, traders, and DISCOMs — built for CERC/SERC compliance from day one.
With 500 GW RE targets by 2030, deviation settlement costs, and ISTS scheduling mandates, poor forecasting has real financial consequences for every stakeholder in the value chain.
CERC DSM regulations impose severe UI/deviation charges when generation deviates from declared schedules — point forecasting errors translate directly into rupee losses.
Single-value forecasts give no quantification of risk. Cloudy ramp events, wind variability, and NWP model disagreements require probabilistic confidence bands, not a single number.
Forecast generation, schedule optimization, BESS dispatch, and RLDC submission are siloed across teams and tools — causing delays, errors, and missed gate-closure windows.
Each AeroGrid module operates independently or as part of a connected forecasting and scheduling stack — deployable via API or our web dashboard.
Multi-horizon probabilistic forecasts (15-min to 7-day) for solar and wind assets using ensemble NWP blending, plant-level ML models, and Bayesian uncertainty quantification. Outputs P10/P50/P90 distributions.
Converts probabilistic forecasts into optimal generation schedules that minimise expected deviation penalties under CERC DSM regulations. Integrates BESS dispatch for round-the-clock supply commitments.
Live monitoring dashboard aggregating generation actuals, forecast vs actual tracking, DSM meter readings, and alert workflows — across a multi-site renewable portfolio on one screen.
Backtesting, skill score diagnostics, and financial impact attribution — understand which forecast errors cost the most and where model performance can be improved across your portfolio.
A fully automated ingestion-to-submission pipeline built for India's grid constraints, data availability, and gate-closure timelines.
AeroGrid Analytics is designed from the ground up for India's energy regulatory environment — not retrofitted from a European or US forecasting stack.
AeroGrid Analytics was founded by energy engineers and ML practitioners who spent years watching Indian RE assets lose crores to avoidable DSM penalties — because the forecasting tools were either built for European grids or too generic to capture India's unique meteorological and regulatory landscape.
We built AeroGrid to be natively probabilistic, natively Indian, and natively integrated with the scheduling workflows that energy teams actually use — from the IEX day-ahead market to RLDC block-wise scheduling.
We run a 30-day pilot on your live plant data — no integration overhead, no long procurement cycles. See real probabilistic forecasts and estimated DSM savings before you commit.
For IPPs, DISCOMs, trading companies, and BESS operators. Typical onboarding: 5 business days.