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
Weather-aware machine learning models forecast solar irradiance and wind speed to predict hourly and day-ahead energy generation with typical accuracy improvements of 20–35% over baseline methods. Better forecasts enable grid operators and energy traders to reduce balancing costs, cut curtailment losses, and optimise storage dispatch. Organisations commonly report 10–25% reductions in balancing penalties and improved renewable utilisation rates. 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.
Vendors to consider
Sources
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