AI USE CASE
Solar and Wind Generation Forecasting
Predict renewable energy output using weather-aware ML to improve grid balancing and reduce curtailment.
What it is
Better forecasts enable grid operators and energy traders to reduce balancing costs, cut curtailment losses, and optimise storage dispatch. The solution integrates NWP (Numerical Weather Prediction) data with historical plant output to produce actionable generation schedules.
Data you need
Multi-year historical plant generation data (hourly resolution), co-located or gridded NWP weather forecasts, and plant metadata (capacity, orientation, turbine curves).
Required systems
- data warehouse
- erp
Why it works
- Use ensemble NWP inputs from multiple providers (e.g., ECMWF, GFS) to reduce single-source forecast error.
- Establish automated retraining pipelines triggered by model drift or plant configuration changes.
- Embed forecast API into SCADA, energy management, and trading systems for real-time operational use.
- Define clear KPIs (RMSE, MAE, curtailment reduction) and monitor them continuously post-deployment.
How this goes wrong
- Low-resolution or incomplete historical generation data leads to poorly calibrated models and unreliable forecasts.
- Over-reliance on a single NWP source without ensemble methods causes large errors during extreme or unusual weather events.
- Models degrade silently after equipment changes (new panels, repowering) if no retraining pipeline is in place.
- Forecast outputs are not integrated into trading or dispatch systems, limiting operational impact.
When NOT to do this
Do not deploy this solution if your plant has fewer than 2 years of clean hourly generation records, insufficient historical data will produce forecasts no better than naive baselines and erode stakeholder trust.
Example vendors
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